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109
docs/android.md
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@ -2,55 +2,82 @@
# Android
## Build on Android using Termux
[Termux](https://github.com/termux/termux-app#installation) is a method to execute `llama.cpp` on an Android device (no root required).
```
apt update && apt upgrade -y
apt install git make cmake
```
It's recommended to move your model inside the `~/` directory for best performance:
```
cd storage/downloads
mv model.gguf ~/
```
[Termux](https://termux.dev/en/) is an Android terminal emulator and Linux environment app (no root required). As of writing, Termux is available experimentally in the Google Play Store; otherwise, it may be obtained directly from the project repo or on F-Droid.
[Get the code](https://github.com/ggerganov/llama.cpp#get-the-code) & [follow the Linux build instructions](https://github.com/ggerganov/llama.cpp#build) to build `llama.cpp`.
## Building the Project using Android NDK
Obtain the [Android NDK](https://developer.android.com/ndk) and then build with CMake.
Execute the following commands on your computer to avoid downloading the NDK to your mobile. Alternatively, you can also do this in Termux:
```
$ mkdir build-android
$ cd build-android
$ export NDK=<your_ndk_directory>
$ cmake -DCMAKE_TOOLCHAIN_FILE=$NDK/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a -DANDROID_PLATFORM=android-23 -DCMAKE_C_FLAGS=-march=armv8.4a+dotprod ..
$ make
```
Install [termux](https://github.com/termux/termux-app#installation) on your device and run `termux-setup-storage` to get access to your SD card (if Android 11+ then run the command twice).
Finally, copy these built `llama` binaries and the model file to your device storage. Because the file permissions in the Android sdcard cannot be changed, you can copy the executable files to the `/data/data/com.termux/files/home/bin` path, and then execute the following commands in Termux to add executable permission:
(Assumed that you have pushed the built executable files to the /sdcard/llama.cpp/bin path using `adb push`)
```
$cp -r /sdcard/llama.cpp/bin /data/data/com.termux/files/home/
$cd /data/data/com.termux/files/home/bin
$chmod +x ./*
```
Download model [llama-2-7b-chat.Q4_K_M.gguf](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/blob/main/llama-2-7b-chat.Q4_K_M.gguf), and push it to `/sdcard/llama.cpp/`, then move it to `/data/data/com.termux/files/home/model/`
With Termux, you can install and run `llama.cpp` as if the environment were Linux. Once in the Termux shell:
```
$mv /sdcard/llama.cpp/llama-2-7b-chat.Q4_K_M.gguf /data/data/com.termux/files/home/model/
$ apt update && apt upgrade -y
$ apt install git cmake
```
Now, you can start chatting:
Then, follow the [build instructions](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md), specifically for CMake.
Once the binaries are built, download your model of choice (e.g., from Hugging Face). It's recommended to place it in the `~/` directory for best performance:
```
$cd /data/data/com.termux/files/home/bin
$./llama-cli -m ../model/llama-2-7b-chat.Q4_K_M.gguf -n 128 -cml
$ curl -L {model-url} -o ~/{model}.gguf
```
Here's a demo of an interactive session running on Pixel 5 phone:
Then, if you are not already in the repo directory, `cd` into `llama.cpp` and:
```
$ ./build/bin/llama-simple -m ~/{model}.gguf -c {context-size} -p "{your-prompt}"
```
Here, we show `llama-simple`, but any of the executables under `examples` should work, in theory. Be sure to set `context-size` to a reasonable number (say, 4096) to start with; otherwise, memory could spike and kill your terminal.
To see what it might look like visually, here's an old demo of an interactive session running on a Pixel 5 phone:
https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4
## Cross-compile using Android NDK
It's possible to build `llama.cpp` for Android on your host system via CMake and the Android NDK. If you are interested in this path, ensure you already have an environment prepared to cross-compile programs for Android (i.e., install the Android SDK). Note that, unlike desktop environments, the Android environment ships with a limited set of native libraries, and so only those libraries are available to CMake when building with the Android NDK (see: https://developer.android.com/ndk/guides/stable_apis.)
Once you're ready and have cloned `llama.cpp`, invoke the following in the project directory:
```
$ cmake \
-DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK/build/cmake/android.toolchain.cmake \
-DANDROID_ABI=arm64-v8a \
-DANDROID_PLATFORM=android-28 \
-DCMAKE_C_FLAGS="-march=armv8.7a" \
-DCMAKE_CXX_FLAGS="-march=armv8.7a" \
-DGGML_OPENMP=OFF \
-DGGML_LLAMAFILE=OFF \
-B build-android
```
Notes:
- While later versions of Android NDK ship with OpenMP, it must still be installed by CMake as a dependency, which is not supported at this time
- `llamafile` does not appear to support Android devices (see: https://github.com/Mozilla-Ocho/llamafile/issues/325)
The above command should configure `llama.cpp` with the most performant options for modern devices. Even if your device is not running `armv8.7a`, `llama.cpp` includes runtime checks for available CPU features it can use.
Feel free to adjust the Android ABI for your target. Once the project is configured:
```
$ cmake --build build-android --config Release -j{n}
$ cmake --install build-android --prefix {install-dir} --config Release
```
After installing, go ahead and download the model of your choice to your host system. Then:
```
$ adb shell "mkdir /data/local/tmp/llama.cpp"
$ adb push {install-dir} /data/local/tmp/llama.cpp/
$ adb push {model}.gguf /data/local/tmp/llama.cpp/
$ adb shell
```
In the `adb shell`:
```
$ cd /data/local/tmp/llama.cpp
$ LD_LIBRARY_PATH=lib ./bin/llama-simple -m {model}.gguf -c {context-size} -p "{your-prompt}"
```
That's it!
Be aware that Android will not find the library path `lib` on its own, so we must specify `LD_LIBRARY_PATH` in order to run the installed executables. Android does support `RPATH` in later API levels, so this could change in the future. Refer to the previous section for information about `context-size` (very important!) and running other `examples`.

20
flake.lock generated
View file

@ -5,11 +5,11 @@
"nixpkgs-lib": "nixpkgs-lib"
},
"locked": {
"lastModified": 1726153070,
"narHash": "sha256-HO4zgY0ekfwO5bX0QH/3kJ/h4KvUDFZg8YpkNwIbg1U=",
"lastModified": 1727826117,
"narHash": "sha256-K5ZLCyfO/Zj9mPFldf3iwS6oZStJcU4tSpiXTMYaaL0=",
"owner": "hercules-ci",
"repo": "flake-parts",
"rev": "bcef6817a8b2aa20a5a6dbb19b43e63c5bf8619a",
"rev": "3d04084d54bedc3d6b8b736c70ef449225c361b1",
"type": "github"
},
"original": {
@ -20,11 +20,11 @@
},
"nixpkgs": {
"locked": {
"lastModified": 1727348695,
"narHash": "sha256-J+PeFKSDV+pHL7ukkfpVzCOO7mBSrrpJ3svwBFABbhI=",
"lastModified": 1728018373,
"narHash": "sha256-NOiTvBbRLIOe5F6RbHaAh6++BNjsb149fGZd1T4+KBg=",
"owner": "NixOS",
"repo": "nixpkgs",
"rev": "1925c603f17fc89f4c8f6bf6f631a802ad85d784",
"rev": "bc947f541ae55e999ffdb4013441347d83b00feb",
"type": "github"
},
"original": {
@ -36,14 +36,14 @@
},
"nixpkgs-lib": {
"locked": {
"lastModified": 1725233747,
"narHash": "sha256-Ss8QWLXdr2JCBPcYChJhz4xJm+h/xjl4G0c0XlP6a74=",
"lastModified": 1727825735,
"narHash": "sha256-0xHYkMkeLVQAMa7gvkddbPqpxph+hDzdu1XdGPJR+Os=",
"type": "tarball",
"url": "https://github.com/NixOS/nixpkgs/archive/356624c12086a18f2ea2825fed34523d60ccc4e3.tar.gz"
"url": "https://github.com/NixOS/nixpkgs/archive/fb192fec7cc7a4c26d51779e9bab07ce6fa5597a.tar.gz"
},
"original": {
"type": "tarball",
"url": "https://github.com/NixOS/nixpkgs/archive/356624c12086a18f2ea2825fed34523d60ccc4e3.tar.gz"
"url": "https://github.com/NixOS/nixpkgs/archive/fb192fec7cc7a4c26d51779e9bab07ce6fa5597a.tar.gz"
}
},
"root": {

1
ggml/include/ggml-backend.h
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@ -170,6 +170,7 @@ extern "C" {
// Functions that may be obtained using ggml_backend_reg_get_proc_address
typedef ggml_backend_buffer_type_t (*ggml_backend_split_buffer_type_t)(const float *);
typedef void (*ggml_backend_set_n_threads_t)(ggml_backend_t, int);
//
// Backend registry

2
ggml/include/ggml-blas.h
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@ -17,6 +17,8 @@ GGML_API bool ggml_backend_is_blas(ggml_backend_t backend);
// for openblas and blis, this will also set the number of threads used for blas operations
GGML_API void ggml_backend_blas_set_n_threads(ggml_backend_t backend_blas, int n_threads);
GGML_API ggml_backend_reg_t ggml_backend_blas_reg(void);
#ifdef __cplusplus
}

18
ggml/src/CMakeLists.txt
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@ -190,22 +190,24 @@ if (GGML_BLAS)
# see https://gitlab.kitware.com/cmake/cmake/-/issues/20268
find_package(PkgConfig REQUIRED)
if (${GGML_BLAS_VENDOR} MATCHES "Generic")
pkg_check_modules(DepBLAS REQUIRED blas)
pkg_check_modules(DepBLAS blas)
elseif (${GGML_BLAS_VENDOR} MATCHES "OpenBLAS")
# As of openblas v0.3.22, the 64-bit is named openblas64.pc
pkg_check_modules(DepBLAS openblas64)
if (NOT DepBLAS_FOUND)
pkg_check_modules(DepBLAS REQUIRED openblas)
pkg_check_modules(DepBLAS openblas)
endif()
elseif (${GGML_BLAS_VENDOR} MATCHES "FLAME")
pkg_check_modules(DepBLAS REQUIRED blis)
add_compile_definitions(GGML_BLAS_USE_BLIS)
pkg_check_modules(DepBLAS blis)
elseif (${GGML_BLAS_VENDOR} MATCHES "ATLAS")
pkg_check_modules(DepBLAS REQUIRED blas-atlas)
pkg_check_modules(DepBLAS blas-atlas)
elseif (${GGML_BLAS_VENDOR} MATCHES "FlexiBLAS")
pkg_check_modules(DepBLAS REQUIRED flexiblas_api)
pkg_check_modules(DepBLAS flexiblas_api)
elseif (${GGML_BLAS_VENDOR} MATCHES "Intel")
add_compile_definitions(GGML_BLAS_USE_MKL)
# all Intel* libraries share the same include path
pkg_check_modules(DepBLAS REQUIRED mkl-sdl)
pkg_check_modules(DepBLAS mkl-sdl)
elseif (${GGML_BLAS_VENDOR} MATCHES "NVHPC")
# this doesn't provide pkg-config
# suggest to assign BLAS_INCLUDE_DIRS on your own
@ -1361,6 +1363,10 @@ if (MATH_LIBRARY)
endif()
endif()
if (CMAKE_SYSTEM_NAME MATCHES "Android")
list(APPEND GGML_EXTRA_LIBS_PRIVATE dl) # Must be linked explicitly
endif()
list(REMOVE_DUPLICATES GGML_EXTRA_LIBS_PRIVATE)
list(REMOVE_DUPLICATES GGML_EXTRA_LIBS_PUBLIC)
target_link_libraries(ggml PRIVATE ${GGML_EXTRA_LIBS_PRIVATE} PUBLIC ${GGML_EXTRA_LIBS_PUBLIC})

14
ggml/src/ggml-backend-impl.h
View file

@ -88,6 +88,7 @@ extern "C" {
void (*free)(ggml_backend_t backend);
// Will be moved to the device interface
// buffer allocation
ggml_backend_buffer_type_t (*get_default_buffer_type)(ggml_backend_t backend);
@ -112,17 +113,9 @@ extern "C" {
// IMPORTANT: these functions have been moved to the device interface and will be removed from the backend interface
// new backends should implement the device interface instead
// These functions are being moved to the device interface
// check if the backend can compute an operation
bool (*supports_op) (ggml_backend_t backend, const struct ggml_tensor * op);
// check if the backend can use tensors allocated in a buffer type
bool (*supports_buft)(ggml_backend_t backend, ggml_backend_buffer_type_t buft);
// check if the backend wants to run an operation, even if the weights are allocated in a CPU buffer
// these should be expensive operations with large batch sizes that may benefit from running on this backend
// even if the weight has to be copied from the CPU temporarily
bool (*offload_op) (ggml_backend_t backend, const struct ggml_tensor * op);
// (optional) event synchronization
@ -184,9 +177,8 @@ extern "C" {
// check if the backend can use tensors allocated in a buffer type
bool (*supports_buft)(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft);
// check if the backend wants to run an operation, even if the weights are allocated in a CPU buffer
// these should be expensive operations with large batch sizes that may benefit from running on this backend
// even if the weight has to be copied from the CPU temporarily
// (optional) check if the backend wants to run an operation, even if the weights are allocated in an incompatible buffer
// these should be expensive operations that may benefit from running on this backend instead of the CPU backend
bool (*offload_op)(ggml_backend_dev_t dev, const struct ggml_tensor * op);
// (optional) event synchronization

25
ggml/src/ggml-backend.cpp
View file

@ -500,7 +500,11 @@ bool ggml_backend_dev_supports_buft(ggml_backend_dev_t device, ggml_backend_buff
}
bool ggml_backend_dev_offload_op(ggml_backend_dev_t device, const struct ggml_tensor * op) {
if (device->iface.offload_op != NULL) {
return device->iface.offload_op(device, op);
}
return false;
}
// Backend (reg)
@ -534,6 +538,10 @@ void * ggml_backend_reg_get_proc_address(ggml_backend_reg_t reg, const char * na
#include "ggml-metal.h"
#endif
#ifdef GGML_USE_BLAS
#include "ggml-blas.h"
#endif
struct ggml_backend_registry {
std::vector<ggml_backend_reg_t> backends;
std::vector<ggml_backend_dev_t> devices;
@ -545,10 +553,13 @@ struct ggml_backend_registry {
#ifdef GGML_USE_METAL
register_backend(ggml_backend_metal_reg());
#endif
register_backend(ggml_backend_cpu_reg());
#ifdef GGML_USE_BLAS
register_backend(ggml_backend_blas_reg());
#endif
// TODO: sycl, vulkan, kompute, cann
register_backend(ggml_backend_cpu_reg());
}
void register_backend(ggml_backend_reg_t reg) {
@ -1229,16 +1240,22 @@ static ggml_backend_dev_t ggml_backend_cpu_reg_get_device(ggml_backend_reg_t reg
};
return &ggml_backend_cpu_device;
}
static void * ggml_backend_cpu_get_proc_address(ggml_backend_reg_t reg, const char * name) {
if (strcmp(name, "ggml_backend_set_n_threads") == 0) {
return (void *)ggml_backend_cpu_set_n_threads;
}
return NULL;
GGML_UNUSED(reg);
GGML_UNUSED(index);
}
static const struct ggml_backend_reg_i ggml_backend_cpu_reg_i = {
/* .get_name = */ ggml_backend_cpu_reg_get_name,
/* .get_device_count = */ ggml_backend_cpu_reg_get_device_count,
/* .get_device = */ ggml_backend_cpu_reg_get_device,
/* .get_proc_address = */ NULL,
/* .get_proc_address = */ ggml_backend_cpu_get_proc_address,
};
ggml_backend_reg_t ggml_backend_cpu_reg(void) {

256
ggml/src/ggml-blas.cpp
View file

@ -4,6 +4,7 @@
#include <future>
#include <vector>
#include <cstring>
#if defined(GGML_USE_ACCELERATE)
# include <Accelerate/Accelerate.h>
@ -26,30 +27,6 @@ struct ggml_backend_blas_context {
#endif
};
// helper function to determine if it is better to use BLAS or not
// for large matrices, BLAS is faster
static bool ggml_backend_blas_use_blas(const struct ggml_tensor * dst) {
const struct ggml_tensor * src0 = dst->src[0];
const struct ggml_tensor * src1 = dst->src[1];
const int64_t ne10 = src1->ne[0];
const int64_t ne0 = dst->ne[0];
const int64_t ne1 = dst->ne[1];
// TODO: find the optimal values for these
if (ggml_is_contiguous(src0) &&
ggml_is_contiguous(src1) &&
src1->type == GGML_TYPE_F32 &&
(ne0 >= 32 && ne1 >= 32 && ne10 >= 32)) {
/*printf("BLAS: %d %d %d %d %d\n", ne0, ne1, ne10, ne00, ne01);*/
return true;
}
return false;
}
static void ggml_backend_blas_mul_mat(ggml_backend_blas_context * ctx, struct ggml_tensor * dst) {
const struct ggml_tensor * src0 = dst->src[0];
const struct ggml_tensor * src1 = dst->src[1];
@ -235,7 +212,7 @@ static void ggml_backend_blas_out_prod(ggml_backend_blas_context * ctx, struct g
// backend interface
static const char * ggml_backend_blas_name(ggml_backend_t backend) {
static const char * ggml_backend_blas_get_name(ggml_backend_t backend) {
return "BLAS";
GGML_UNUSED(backend);
@ -285,29 +262,8 @@ static enum ggml_status ggml_backend_blas_graph_compute(ggml_backend_t backend,
GGML_UNUSED(backend);
}
static bool ggml_backend_blas_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
const struct ggml_tensor * src0 = op->src[0];
const struct ggml_tensor * src1 = op->src[1];
return (op->op == GGML_OP_MUL_MAT && ggml_backend_blas_use_blas(op)) ||
(op->op == GGML_OP_OUT_PROD && op->src[0]->type == GGML_TYPE_F32 &&
op->src[1]->type == GGML_TYPE_F32 &&
ggml_is_matrix(src0) &&
ggml_is_matrix(src1) &&
ggml_is_contiguous(src0) &&
(ggml_is_contiguous(src1) || ggml_is_transposed(src1)));
GGML_UNUSED(backend);
}
static bool ggml_backend_blas_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) {
return ggml_backend_buft_is_host(buft);
GGML_UNUSED(backend);
}
static struct ggml_backend_i blas_backend_i = {
/* .get_name = */ ggml_backend_blas_name,
/* .get_name = */ ggml_backend_blas_get_name,
/* .free = */ ggml_backend_blas_free,
/* .get_default_buffer_type = */ ggml_backend_blas_get_default_buffer_type,
/* .set_tensor_async = */ NULL,
@ -319,8 +275,8 @@ static struct ggml_backend_i blas_backend_i = {
/* .graph_plan_update = */ NULL,
/* .graph_plan_compute = */ NULL,
/* .graph_compute = */ ggml_backend_blas_graph_compute,
/* .supports_op = */ ggml_backend_blas_supports_op,
/* .supports_buft = */ ggml_backend_blas_supports_buft,
/* .supports_op = */ NULL,
/* .supports_buft = */ NULL,
/* .offload_op = */ NULL,
/* .event_record = */ NULL,
/* .event_wait = */ NULL,
@ -337,7 +293,7 @@ ggml_backend_t ggml_backend_blas_init(void) {
ggml_backend_t backend = new ggml_backend {
/* .guid = */ ggml_backend_blas_guid(),
/* .interface = */ blas_backend_i,
/* .device = */ nullptr,
/* .device = */ ggml_backend_reg_dev_get(ggml_backend_blas_reg(), 0),
/* .context = */ ctx,
};
@ -364,3 +320,203 @@ void ggml_backend_blas_set_n_threads(ggml_backend_t backend_blas, int n_threads)
ggml_backend_blas_context * ctx = (ggml_backend_blas_context *)backend_blas->context;
ctx->n_threads = n_threads;
}
// device interface
static const char * ggml_backend_blas_device_get_name(ggml_backend_dev_t dev) {
return "BLAS";
GGML_UNUSED(dev);
}
static const char * ggml_backend_blas_device_get_description(ggml_backend_dev_t dev) {
#if defined(GGML_USE_ACCELERATE)
return "Accelerate";
#elif defined(GGML_BLAS_USE_MKL)
return "MKL";
#elif defined(GGML_BLAS_USE_BLIS)
return "BLIS";
#elif defined(GGML_BLAS_USE_NVPL)
return "NVPL";
#elif defined(OPENBLAS_VERSION)
return "OpenBLAS";
#else
return "BLAS";
#endif
GGML_UNUSED(dev);
}
static void ggml_backend_blas_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
// TODO
*free = 0;
*total = 0;
GGML_UNUSED(dev);
}
static enum ggml_backend_dev_type ggml_backend_blas_device_get_type(ggml_backend_dev_t dev) {
return GGML_BACKEND_DEVICE_TYPE_CPU;
GGML_UNUSED(dev);
}
static void ggml_backend_blas_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) {
props->name = ggml_backend_blas_device_get_name(dev);
props->description = ggml_backend_blas_device_get_description(dev);
props->type = ggml_backend_blas_device_get_type(dev);
ggml_backend_blas_device_get_memory(dev, &props->memory_free, &props->memory_total);
props->caps = {
/* .async = */ false,
/* .host_buffer = */ false,
/* .buffer_from_host_ptr = */ true,
/* .events = */ false,
};
}
static ggml_backend_t ggml_backend_blas_device_init(ggml_backend_dev_t dev, const char * params) {
return ggml_backend_blas_init();
GGML_UNUSED(dev);
GGML_UNUSED(params);
}
static ggml_backend_buffer_type_t ggml_backend_blas_device_get_buffer_type(ggml_backend_dev_t dev) {
return ggml_backend_cpu_buffer_type();
GGML_UNUSED(dev);
}
static ggml_backend_buffer_t ggml_backend_blas_device_buffer_from_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) {
return ggml_backend_cpu_buffer_from_ptr(ptr, size);
GGML_UNUSED(dev);
GGML_UNUSED(max_tensor_size);
}
static bool ggml_backend_blas_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) {
const struct ggml_tensor * src0 = op->src[0];
const struct ggml_tensor * src1 = op->src[1];
switch (op->op) {
case GGML_OP_NONE:
case GGML_OP_RESHAPE:
case GGML_OP_VIEW:
case GGML_OP_PERMUTE:
case GGML_OP_TRANSPOSE:
return true;
case GGML_OP_MUL_MAT:
{
// BLAS usually is only faster for large matrices
const struct ggml_tensor * src0 = op->src[0];
const struct ggml_tensor * src1 = op->src[1];
const int64_t ne10 = src1->ne[0];
const int64_t ne0 = op->ne[0];
const int64_t ne1 = op->ne[1];
// TODO: find the optimal value
const int64_t min_batch = 32;
return (ggml_is_contiguous(src0) &&
ggml_is_contiguous(src1) &&
src1->type == GGML_TYPE_F32 &&
(ne0 >= min_batch && ne1 >= min_batch && ne10 >= min_batch));
}
case GGML_OP_OUT_PROD:
return (op->src[0]->type == GGML_TYPE_F32 &&
op->src[1]->type == GGML_TYPE_F32 &&
ggml_is_matrix(src0) &&
ggml_is_matrix(src1) &&
ggml_is_contiguous(src0) &&
(ggml_is_contiguous(src1) || ggml_is_transposed(src1)));
default:
return false;
}
GGML_UNUSED(dev);
}
static bool ggml_backend_blas_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) {
return ggml_backend_buft_is_host(buft);
GGML_UNUSED(dev);
}
static const struct ggml_backend_device_i ggml_backend_blas_device_i = {
/* .get_name = */ ggml_backend_blas_device_get_name,
/* .get_description = */ ggml_backend_blas_device_get_description,
/* .get_memory = */ ggml_backend_blas_device_get_memory,
/* .get_type = */ ggml_backend_blas_device_get_type,
/* .get_props = */ ggml_backend_blas_device_get_props,
/* .init_backend = */ ggml_backend_blas_device_init,
/* .get_buffer_type = */ ggml_backend_blas_device_get_buffer_type,
/* .get_host_buffer_type = */ NULL,
/* .buffer_from_host_ptr = */ ggml_backend_blas_device_buffer_from_ptr,
/* .supports_op = */ ggml_backend_blas_device_supports_op,
/* .supports_buft = */ ggml_backend_blas_device_supports_buft,
/* .offload_op = */ NULL,
/* .event_new = */ NULL,
/* .event_free = */ NULL,
/* .event_synchronize = */ NULL,
};
// backend reg interface
static const char * ggml_backend_blas_reg_get_name(ggml_backend_reg_t reg) {
return "BLAS";
GGML_UNUSED(reg);
}
static size_t ggml_backend_blas_reg_get_device_count(ggml_backend_reg_t reg) {
return 1;
GGML_UNUSED(reg);
}
static ggml_backend_dev_t ggml_backend_blas_reg_get_device(ggml_backend_reg_t reg, size_t index) {
GGML_ASSERT(index == 0);
static ggml_backend_device ggml_backend_blas_device = {
/* .iface = */ ggml_backend_blas_device_i,
/* .reg = */ reg,
/* .context = */ nullptr,
};
return &ggml_backend_blas_device;
GGML_UNUSED(reg);
GGML_UNUSED(index);
}
static void * ggml_backend_blas_get_proc_address(ggml_backend_reg_t reg, const char * name) {
if (std::strcmp(name, "ggml_backend_set_n_threads") == 0) {
return (void *)ggml_backend_blas_set_n_threads;
}
return NULL;
GGML_UNUSED(reg);
GGML_UNUSED(name);
}
static const struct ggml_backend_reg_i ggml_backend_blas_reg_i = {
/* .get_name = */ ggml_backend_blas_reg_get_name,
/* .get_device_count = */ ggml_backend_blas_reg_get_device_count,
/* .get_device = */ ggml_backend_blas_reg_get_device,
/* .get_proc_address = */ ggml_backend_blas_get_proc_address,
};
ggml_backend_reg_t ggml_backend_blas_reg(void) {
static struct ggml_backend_reg ggml_backend_blas_reg = {
/* .iface = */ ggml_backend_blas_reg_i,
/* .context = */ NULL,
};
return &ggml_backend_blas_reg;
}

70
src/llama.cpp
View file

@ -22,10 +22,6 @@
# include "ggml-cann.h"
#endif
#ifdef GGML_USE_BLAS
# include "ggml-blas.h"
#endif
// TODO: replace with ggml API call
#define QK_K 256
@ -3288,9 +3284,8 @@ struct llama_context {
std::unordered_map<struct llama_lora_adapter *, float> lora_adapters;
std::vector<ggml_backend_t> backends;
#ifdef GGML_USE_BLAS
ggml_backend_t backend_blas = nullptr;
#endif
std::vector<std::pair<ggml_backend_t, ggml_backend_set_n_threads_t>> set_n_threads_fns;
ggml_backend_t backend_cpu = nullptr;
ggml_threadpool_t threadpool = nullptr;
@ -8908,7 +8903,8 @@ static bool llm_load_tensors(
bufs.reserve(n_max_backend_buffer);
// check if this backend device supports buffer_from_host_ptr
ggml_backend_dev_t dev = ggml_backend_buft_get_device(buft);
// when using a host buffer as the CPU bakcend buffer, use the CPU device to prioritize using buffer_from_host_ptr over the host buffer
ggml_backend_dev_t dev = ggml_backend_buft_get_device(buft == llama_default_buffer_type_cpu(model, true) ? ggml_backend_cpu_buffer_type() : buft);
bool buffer_from_host_ptr_supported = false;
if (dev) {
ggml_backend_dev_props props;
@ -17048,17 +17044,19 @@ static void llama_graph_compute(
int n_threads,
ggml_threadpool * threadpool) {
if (lctx.backend_cpu != nullptr) {
ggml_backend_cpu_set_n_threads(lctx.backend_cpu, n_threads);
ggml_backend_cpu_set_threadpool(lctx.backend_cpu, threadpool);
ggml_backend_cpu_set_abort_callback(lctx.backend_cpu, lctx.abort_callback, lctx.abort_callback_data);
}
#ifdef GGML_USE_BLAS
if (lctx.backend_blas != nullptr) {
ggml_backend_blas_set_n_threads(lctx.backend_blas, n_threads);
}
#endif
ggml_backend_sched_graph_compute_async(lctx.sched, gf);
// set the number of threads for all the backends
for (const auto & set_n_threads_fn : lctx.set_n_threads_fns) {
set_n_threads_fn.second(set_n_threads_fn.first, n_threads);
}
auto err = ggml_backend_sched_graph_compute_async(lctx.sched, gf);
if (err != GGML_STATUS_SUCCESS) {
LLAMA_LOG_ERROR("%s: ggml_backend_sched_graph_compute_async failed with error %d\n", __func__, err);
}
// fprintf(stderr, "splits: %d\n", ggml_backend_sched_get_n_splits(lctx.sched));
}
@ -19110,9 +19108,16 @@ struct llama_model * llama_load_model_from_file(
// TODO: rework API to give user more control over device selection
for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
ggml_backend_dev_t dev = ggml_backend_dev_get(i);
// skip the CPU backend since it is handled separately
if (ggml_backend_dev_type(dev) != GGML_BACKEND_DEVICE_TYPE_CPU_FULL) {
switch (ggml_backend_dev_type(dev)) {
case GGML_BACKEND_DEVICE_TYPE_CPU:
case GGML_BACKEND_DEVICE_TYPE_CPU_FULL:
// skip CPU backends since they are `handled separately
break;
case GGML_BACKEND_DEVICE_TYPE_GPU:
case GGML_BACKEND_DEVICE_TYPE_GPU_FULL:
model->devices.push_back(dev);
break;
}
}
@ -19407,14 +19412,19 @@ struct llama_context * llama_new_context_with_model(
}
#endif
#ifdef GGML_USE_BLAS
ctx->backend_blas = ggml_backend_blas_init();
if (ctx->backend_blas == nullptr) {
LLAMA_LOG_WARN("%s: failed to initialize BLAS backend\n", __func__);
} else {
ctx->backends.push_back(ctx->backend_blas);
// add other backends (such as BLAS)
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) == GGML_BACKEND_DEVICE_TYPE_CPU) {
ggml_backend_t backend = ggml_backend_dev_init(dev, nullptr);
if (backend == nullptr) {
LLAMA_LOG_ERROR("%s: failed to initialize %s backend\n", __func__, ggml_backend_dev_name(dev));
llama_free(ctx);
return nullptr;
}
ctx->backends.push_back(backend);
}
}
#endif
ctx->backend_cpu = ggml_backend_cpu_init();
if (ctx->backend_cpu == nullptr) {
@ -19424,6 +19434,18 @@ struct llama_context * llama_new_context_with_model(
}
ctx->backends.push_back(ctx->backend_cpu);
// create a list of the set_n_threads functions in the backends
for (auto * backend : ctx->backends) {
ggml_backend_dev_t dev = ggml_backend_get_device(backend);
ggml_backend_reg_t reg = dev ? ggml_backend_dev_backend_reg(dev) : nullptr;
if (reg) {
auto ggml_backend_set_n_threads_fn = (ggml_backend_set_n_threads_t) ggml_backend_reg_get_proc_address(reg, "ggml_backend_set_n_threads");
if (ggml_backend_set_n_threads_fn) {
ctx->set_n_threads_fns.emplace_back(backend, ggml_backend_set_n_threads_fn);
}
}
}
if (!llama_kv_cache_init(ctx->kv_self, ctx, type_k, type_v, kv_size, cparams.offload_kqv)) {
LLAMA_LOG_ERROR("%s: llama_kv_cache_init() failed for self-attention cache\n", __func__);
llama_free(ctx);

6
tests/test-backend-ops.cpp
View file

@ -3820,9 +3820,11 @@ int main(int argc, char ** argv) {
continue;
}
if (ggml_backend_is_cpu(backend)) {
ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(dev);
auto ggml_backend_set_n_threads_fn = (ggml_backend_set_n_threads_t) ggml_backend_reg_get_proc_address(reg, "ggml_backend_set_n_threads");
if (ggml_backend_set_n_threads_fn) {
// TODO: better value for n_threads
ggml_backend_cpu_set_n_threads(backend, std::thread::hardware_concurrency() / 2);
ggml_backend_set_n_threads_fn(backend, std::thread::hardware_concurrency());
}
printf(" Device description: %s\n", ggml_backend_dev_description(dev));