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ggml : add RPC backend (#6829)

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* ggml : add RPC backend

The RPC backend proxies all operations to a remote server which runs a
regular backend (CPU, CUDA, Metal, etc).

* set TCP_NODELAY

* add CI workflows

* Address review comments

* fix warning

* implement llama_max_devices() for RPC

* Address review comments

* Address review comments

* wrap sockfd into a struct

* implement get_alignment and get_max_size

* add get_device_memory

* fix warning

* win32 support

* add README

* readme : trim trailing whitespace

* Address review comments

* win32 fix

* Address review comments

* fix compile warnings on macos
This commit is contained in:
Radoslav Gerganov 2024-05-14 14:27:19 +03:00 committed by GitHub
parent 541600201e
commit 5e31828d3e
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
12 changed files with 1395 additions and 98 deletions
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32
.github/workflows/build.yml vendored
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@ -340,6 +340,36 @@ jobs:
cd build
ctest -L main --verbose
ubuntu-latest-cmake-rpc:
runs-on: ubuntu-latest
continue-on-error: true
steps:
- name: Clone
id: checkout
uses: actions/checkout@v4
- name: Dependencies
id: depends
run: |
sudo apt-get update
sudo apt-get install build-essential
- name: Build
id: cmake_build
run: |
mkdir build
cd build
cmake -DLLAMA_RPC=ON ..
cmake --build . --config Release -j $(nproc)
- name: Test
id: cmake_test
run: |
cd build
ctest -L main --verbose
ubuntu-22-cmake-vulkan:
runs-on: ubuntu-22.04
@ -663,6 +693,8 @@ jobs:
strategy:
matrix:
include:
- build: 'rpc'
defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_RPC=ON -DBUILD_SHARED_LIBS=ON'
- build: 'noavx'
defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX=OFF -DLLAMA_AVX2=OFF -DLLAMA_FMA=OFF -DBUILD_SHARED_LIBS=ON'
- build: 'avx2'

13
CMakeLists.txt
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@ -123,6 +123,7 @@ set(LLAMA_METAL_MACOSX_VERSION_MIN "" CACHE STRING
set(LLAMA_METAL_STD "" CACHE STRING "llama: metal standard version (-std flag)")
option(LLAMA_KOMPUTE "llama: use Kompute" OFF)
option(LLAMA_MPI "llama: use MPI" OFF)
option(LLAMA_RPC "llama: use RPC" OFF)
option(LLAMA_QKK_64 "llama: use super-block size of 64 for k-quants" OFF)
option(LLAMA_SYCL "llama: use SYCL" OFF)
option(LLAMA_SYCL_F16 "llama: use 16 bit floats for sycl calculations" OFF)
@ -494,6 +495,17 @@ if (LLAMA_MPI)
endif()
endif()
if (LLAMA_RPC)
add_compile_definitions(GGML_USE_RPC)
if (WIN32)
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ws2_32)
endif()
set(GGML_HEADERS_RPC ggml-rpc.h)
set(GGML_SOURCES_RPC ggml-rpc.cpp)
endif()
if (LLAMA_CLBLAST)
find_package(CLBlast)
if (CLBlast_FOUND)
@ -1176,6 +1188,7 @@ add_library(ggml OBJECT
${GGML_SOURCES_OPENCL} ${GGML_HEADERS_OPENCL}
${GGML_SOURCES_METAL} ${GGML_HEADERS_METAL}
${GGML_SOURCES_MPI} ${GGML_HEADERS_MPI}
${GGML_SOURCES_RPC} ${GGML_HEADERS_RPC}
${GGML_SOURCES_EXTRA} ${GGML_HEADERS_EXTRA}
${GGML_SOURCES_SYCL} ${GGML_HEADERS_SYCL}
${GGML_SOURCES_KOMPUTE} ${GGML_HEADERS_KOMPUTE}

10
common/common.cpp
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@ -1060,6 +1060,14 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
#endif // GGML_USE_CUDA_SYCL_VULKAN
return true;
}
if (arg == "--rpc") {
if (++i >= argc) {
invalid_param = true;
return true;
}
params.rpc_servers = argv[i];
return true;
}
if (arg == "--no-mmap") {
params.use_mmap = false;
return true;
@ -1557,6 +1565,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n");
printf(" or for intermediate results and KV (with split-mode = row) (default: %d)\n", params.main_gpu);
}
printf(" --rpc SERVERS comma separated list of RPC servers\n");
printf(" --verbose-prompt print a verbose prompt before generation (default: %s)\n", params.verbose_prompt ? "true" : "false");
printf(" --no-display-prompt don't print prompt at generation (default: %s)\n", !params.display_prompt ? "true" : "false");
printf(" -gan N, --grp-attn-n N\n");
@ -1830,6 +1839,7 @@ struct llama_model_params llama_model_params_from_gpt_params(const gpt_params &
if (params.n_gpu_layers != -1) {
mparams.n_gpu_layers = params.n_gpu_layers;
}
mparams.rpc_servers = params.rpc_servers.c_str();
mparams.main_gpu = params.main_gpu;
mparams.split_mode = params.split_mode;
mparams.tensor_split = params.tensor_split;

1
common/common.h
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@ -82,6 +82,7 @@ struct gpt_params {
float yarn_beta_slow = 1.0f; // YaRN high correction dim
int32_t yarn_orig_ctx = 0; // YaRN original context length
float defrag_thold = -1.0f; // KV cache defragmentation threshold
std::string rpc_servers = ""; // comma separated list of RPC servers
ggml_backend_sched_eval_callback cb_eval = nullptr;
void * cb_eval_user_data = nullptr;

3
examples/CMakeLists.txt
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@ -49,4 +49,7 @@ else()
add_subdirectory(server)
endif()
add_subdirectory(export-lora)
if (LLAMA_RPC)
add_subdirectory(rpc)
endif()
endif()

2
examples/rpc/CMakeLists.txt Normal file
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@ -0,0 +1,2 @@
add_executable(rpc-server rpc-server.cpp)
target_link_libraries(rpc-server PRIVATE ggml llama)

74
examples/rpc/README.md Normal file
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@ -0,0 +1,74 @@
## Overview
The `rpc-server` allows running `ggml` backend on a remote host.
The RPC backend communicates with one or several instances of `rpc-server` and offloads computations to them.
This can be used for distributed LLM inference with `llama.cpp` in the following way:
```mermaid
flowchart TD
rpcb---|TCP|srva
rpcb---|TCP|srvb
rpcb-.-|TCP|srvn
subgraph hostn[Host N]
srvn[rpc-server]-.-backend3["Backend (CUDA,Metal,etc.)"]
end
subgraph hostb[Host B]
srvb[rpc-server]---backend2["Backend (CUDA,Metal,etc.)"]
end
subgraph hosta[Host A]
srva[rpc-server]---backend["Backend (CUDA,Metal,etc.)"]
end
subgraph host[Main Host]
ggml[llama.cpp]---rpcb[RPC backend]
end
style hostn stroke:#66,stroke-width:2px,stroke-dasharray: 5 5
```
Each host can run a different backend, e.g. one with CUDA and another with Metal.
You can also run multiple `rpc-server` instances on the same host, each with a different backend.
## Usage
On each host, build the corresponding backend with `cmake` and add `-DLLAMA_RPC=ON` to the build options.
For example, to build the CUDA backend with RPC support:
```bash
mkdir build-rpc-cuda
cd build-rpc-cuda
cmake .. -DLLAMA_CUDA=ON -DLLAMA_RPC=ON
cmake --build . --config Release
```
Then, start the `rpc-server` with the backend:
```bash
$ bin/rpc-server 0.0.0.0 50052
create_backend: using CUDA backend
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: CUDA_USE_TENSOR_CORES: yes
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA T1200 Laptop GPU, compute capability 7.5, VMM: yes
Starting RPC server on 0.0.0.0:50052
```
When using the CUDA backend, you can specify the device with the `CUDA_VISIBLE_DEVICES` environment variable, e.g.:
```bash
$ CUDA_VISIBLE_DEVICES=0 bin/rpc-server 0.0.0.0 50052
```
This way you can run multiple `rpc-server` instances on the same host, each with a different CUDA device.
On the main host build `llama.cpp` only with `-DLLAMA_RPC=ON`:
```bash
mkdir build-rpc
cd build-rpc
cmake .. -DLLAMA_RPC=ON
cmake --build . --config Release
```
Finally, use the `--rpc` option to specify the host and port of each `rpc-server`:
```bash
$ bin/main -m ../models/tinyllama-1b/ggml-model-f16.gguf -p "Hello, my name is" --repeat-penalty 1.0 -n 64 --rpc 192.168.88.10:50052,192.168.88.11:50052 -ngl 99
```

70
examples/rpc/rpc-server.cpp Normal file
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@ -0,0 +1,70 @@
#ifdef GGML_USE_CUDA
#include "ggml-cuda.h"
#endif
#ifdef GGML_USE_METAL
#include "ggml-metal.h"
#endif
#include "ggml-rpc.h"
#include <string>
#include <stdio.h>
static ggml_backend_t create_backend() {
ggml_backend_t backend = NULL;
#ifdef GGML_USE_CUDA
fprintf(stderr, "%s: using CUDA backend\n", __func__);
backend = ggml_backend_cuda_init(0); // init device 0
if (!backend) {
fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__);
}
#elif GGML_USE_METAL
fprintf(stderr, "%s: using Metal backend\n", __func__);
backend = ggml_backend_metal_init();
if (!backend) {
fprintf(stderr, "%s: ggml_backend_metal_init() failed\n", __func__);
}
#endif
// if there aren't GPU Backends fallback to CPU backend
if (!backend) {
fprintf(stderr, "%s: using CPU backend\n", __func__);
backend = ggml_backend_cpu_init();
}
return backend;
}
static void get_backend_memory(size_t * free_mem, size_t * total_mem) {
#ifdef GGML_USE_CUDA
ggml_backend_cuda_get_device_memory(0, free_mem, total_mem);
#else
// TODO: implement for other backends
*free_mem = 1;
*total_mem = 1;
#endif
}
int main(int argc, char * argv[]) {
if (argc < 3) {
fprintf(stderr, "Usage: %s <host> <port>\n", argv[0]);
return 1;
}
const char * host = argv[1];
int port = std::stoi(argv[2]);
if (port <= 0 || port > 65535) {
fprintf(stderr, "Invalid port number: %d\n", port);
return 1;
}
ggml_backend_t backend = create_backend();
if (!backend) {
fprintf(stderr, "Failed to create backend\n");
return 1;
}
printf("Starting RPC server on %s:%d\n", host, port);
size_t free_mem, total_mem;
get_backend_memory(&free_mem, &total_mem);
std::string endpoint = std::string(host) + ":" + std::to_string(port);
start_rpc_server(backend, endpoint.c_str(), free_mem, total_mem);
ggml_backend_free(backend);
return 0;
}

1023
ggml-rpc.cpp Normal file
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24
ggml-rpc.h Normal file
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@ -0,0 +1,24 @@
#pragma once
#include "ggml.h"
#include "ggml-backend.h"
#ifdef __cplusplus
extern "C" {
#endif
#define GGML_RPC_MAX_SERVERS 16
// backend API
GGML_API GGML_CALL ggml_backend_t ggml_backend_rpc_init(const char * endpoint);
GGML_API GGML_CALL bool ggml_backend_is_rpc(ggml_backend_t backend);
GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_rpc_buffer_type(const char * endpoint);
GGML_API GGML_CALL void ggml_backend_rpc_get_device_memory(const char * endpoint, size_t * free, size_t * total);
GGML_API GGML_CALL void start_rpc_server(ggml_backend_t backend, const char * endpoint, size_t free_mem, size_t total_mem);
#ifdef __cplusplus
}
#endif

238
llama.cpp
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@ -7,6 +7,10 @@
#include "ggml-alloc.h"
#include "ggml-backend.h"
#ifdef GGML_USE_RPC
# include "ggml-rpc.h"
#endif
#ifdef GGML_USE_CUDA
# include "ggml-cuda.h"
#elif defined(GGML_USE_CLBLAST)
@ -1685,91 +1689,6 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_cpu(bool host_buffer
GGML_UNUSED(host_buffer);
}
static ggml_backend_buffer_type_t llama_default_buffer_type_offload(int gpu) {
ggml_backend_buffer_type_t buft = nullptr;
#ifdef GGML_USE_METAL
buft = ggml_backend_metal_buffer_type();
#elif defined(GGML_USE_CUDA)
buft = ggml_backend_cuda_buffer_type(gpu);
#elif defined(GGML_USE_VULKAN)
buft = ggml_backend_vk_buffer_type(gpu);
#elif defined(GGML_USE_SYCL)
buft = ggml_backend_sycl_buffer_type(gpu);
#elif defined(GGML_USE_CLBLAST)
buft = ggml_backend_opencl_buffer_type();
#elif defined(GGML_USE_KOMPUTE)
buft = ggml_backend_kompute_buffer_type(gpu);
if (buft == nullptr) {
LLAMA_LOG_WARN("%s: cannot use GPU %d, check `vulkaninfo --summary`\n", __func__, gpu);
}
#endif
if (buft == nullptr) {
buft = llama_default_buffer_type_cpu(true);
}
return buft;
GGML_UNUSED(gpu);
}
static ggml_backend_buffer_type_t llama_default_buffer_type_split(int fallback_gpu, const float * tensor_split) {
ggml_backend_buffer_type_t buft = nullptr;
#ifdef GGML_USE_CUDA
if (ggml_backend_cuda_get_device_count() > 1) {
buft = ggml_backend_cuda_split_buffer_type(tensor_split);
}
#endif
#ifdef GGML_USE_SYCL
if (ggml_backend_sycl_get_device_count() > 1) {
buft = ggml_backend_sycl_split_buffer_type(tensor_split);
}
#endif
if (buft == nullptr) {
buft = llama_default_buffer_type_offload(fallback_gpu);
}
return buft;
GGML_UNUSED(tensor_split);
}
static size_t llama_get_device_count() {
#if defined(GGML_USE_CUDA)
return ggml_backend_cuda_get_device_count();
#elif defined(GGML_USE_SYCL)
return ggml_backend_sycl_get_device_count();
#elif defined(GGML_USE_VULKAN)
return ggml_backend_vk_get_device_count();
#else
return 1;
#endif
}
static size_t llama_get_device_memory(int device) {
#if defined(GGML_USE_CUDA)
size_t total;
size_t free;
ggml_backend_cuda_get_device_memory(device, &free, &total);
return free;
#elif defined(GGML_USE_SYCL)
size_t total;
size_t free;
ggml_backend_sycl_get_device_memory(device, &free, &total);
return free;
#elif defined(GGML_USE_VULKAN)
size_t total;
size_t free;
ggml_backend_vk_get_device_memory(device, &free, &total);
return free;
#else
return 1;
GGML_UNUSED(device);
#endif
}
//
// globals
//
@ -2210,6 +2129,8 @@ struct llama_model {
int main_gpu;
int n_gpu_layers;
std::vector<std::string> rpc_servers;
// gguf metadata
std::unordered_map<std::string, std::string> gguf_kv;
@ -2353,6 +2274,104 @@ struct llama_context {
#endif
};
static ggml_backend_buffer_type_t llama_default_buffer_type_offload(const llama_model & model, int gpu) {
ggml_backend_buffer_type_t buft = nullptr;
#ifdef GGML_USE_RPC
std::string endpoint = model.rpc_servers[gpu];
buft = ggml_backend_rpc_buffer_type(endpoint.c_str());
#elif defined(GGML_USE_METAL)
buft = ggml_backend_metal_buffer_type();
#elif defined(GGML_USE_CUDA)
buft = ggml_backend_cuda_buffer_type(gpu);
#elif defined(GGML_USE_VULKAN)
buft = ggml_backend_vk_buffer_type(gpu);
#elif defined(GGML_USE_SYCL)
buft = ggml_backend_sycl_buffer_type(gpu);
#elif defined(GGML_USE_CLBLAST)
buft = ggml_backend_opencl_buffer_type();
#elif defined(GGML_USE_KOMPUTE)
buft = ggml_backend_kompute_buffer_type(gpu);
if (buft == nullptr) {
LLAMA_LOG_WARN("%s: cannot use GPU %d, check `vulkaninfo --summary`\n", __func__, gpu);
}
#endif
if (buft == nullptr) {
buft = llama_default_buffer_type_cpu(true);
}
return buft;
GGML_UNUSED(model);
GGML_UNUSED(gpu);
}
static ggml_backend_buffer_type_t llama_default_buffer_type_split(const llama_model & model, int fallback_gpu, const float * tensor_split) {
ggml_backend_buffer_type_t buft = nullptr;
#ifdef GGML_USE_CUDA
if (ggml_backend_cuda_get_device_count() > 1) {
buft = ggml_backend_cuda_split_buffer_type(tensor_split);
}
#endif
#ifdef GGML_USE_SYCL
if (ggml_backend_sycl_get_device_count() > 1) {
buft = ggml_backend_sycl_split_buffer_type(tensor_split);
}
#endif
if (buft == nullptr) {
buft = llama_default_buffer_type_offload(model, fallback_gpu);
}
return buft;
GGML_UNUSED(tensor_split);
}
static size_t llama_get_device_count(const llama_model & model) {
#if defined(GGML_USE_RPC)
return model.rpc_servers.size();
#elif defined(GGML_USE_CUDA)
return ggml_backend_cuda_get_device_count();
#elif defined(GGML_USE_SYCL)
return ggml_backend_sycl_get_device_count();
#elif defined(GGML_USE_VULKAN)
return ggml_backend_vk_get_device_count();
#else
return 1;
#endif
GGML_UNUSED(model);
}
static size_t llama_get_device_memory(const llama_model & model, int device) {
#if defined(GGML_USE_RPC)
size_t total;
size_t free;
std::string endpoint = model.rpc_servers[device];
ggml_backend_rpc_get_device_memory(endpoint.c_str(), &free, &total);
return free;
#elif defined(GGML_USE_CUDA)
size_t total;
size_t free;
ggml_backend_cuda_get_device_memory(device, &free, &total);
return free;
#elif defined(GGML_USE_SYCL)
size_t total;
size_t free;
ggml_backend_sycl_get_device_memory(device, &free, &total);
return free;
#elif defined(GGML_USE_VULKAN)
size_t total;
size_t free;
ggml_backend_vk_get_device_memory(device, &free, &total);
return free;
#else
return 1;
#endif
GGML_UNUSED(model);
GGML_UNUSED(device);
}
//
// kv cache helpers
//
@ -4791,13 +4810,13 @@ static bool llm_load_tensors(
if (split_mode == LLAMA_SPLIT_MODE_LAYER) {
// calculate the split points
int device_count = llama_get_device_count();
int device_count = llama_get_device_count(model);
bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + device_count, [](float x) { return x == 0.0f; });
std::vector<float> splits(device_count);
if (all_zero) {
// default split, by free memory
for (int i = 0; i < device_count; ++i) {
splits[i] = llama_get_device_memory(i);
splits[i] = llama_get_device_memory(model, i);
}
} else {
std::copy(tensor_split, tensor_split + device_count, splits.begin());
@ -4817,35 +4836,35 @@ static bool llm_load_tensors(
int act_gpu_layers = std::min(n_gpu_layers, (int)n_layer + 1);
for (int64_t i = i_gpu_start; i < n_layer; ++i) {
int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(i - i_gpu_start)/act_gpu_layers) - splits.begin();
model.buft_layer[i] = llama_default_buffer_type_offload(layer_gpu);
model.buft_layer[i] = llama_default_buffer_type_offload(model, layer_gpu);
}
// assign the output layer
if (n_gpu_layers > n_layer) {
int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(act_gpu_layers - 1)/act_gpu_layers) - splits.begin();
model.buft_output = llama_default_buffer_type_offload(layer_gpu);
model.buft_output = llama_default_buffer_type_offload(model, layer_gpu);
} else {
model.buft_output = llama_default_buffer_type_cpu(true);
}
} else {
ggml_backend_buffer_type_t split_buft;
if (split_mode == LLAMA_SPLIT_MODE_ROW) {
split_buft = llama_default_buffer_type_split(main_gpu, tensor_split);
split_buft = llama_default_buffer_type_split(model, main_gpu, tensor_split);
} else {
// LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_LAYER in backends where it is not supported
split_buft = llama_default_buffer_type_offload(main_gpu);
split_buft = llama_default_buffer_type_offload(model, main_gpu);
}
// assign the repeating layers
for (int64_t i = i_gpu_start; i < n_layer; ++i) {
model.buft_layer[i] = {
split_buft,
llama_default_buffer_type_offload(main_gpu)
llama_default_buffer_type_offload(model, main_gpu)
};
}
// assign the output layer
if (n_gpu_layers > n_layer) {
model.buft_output = {
split_buft,
llama_default_buffer_type_offload(main_gpu)
llama_default_buffer_type_offload(model, main_gpu)
};
} else {
model.buft_output = llama_default_buffer_type_cpu(true);
@ -15390,6 +15409,7 @@ struct llama_model_params llama_model_default_params() {
/*.split_mode =*/ LLAMA_SPLIT_MODE_LAYER,
/*.main_gpu =*/ 0,
/*.tensor_split =*/ nullptr,
/*.rpc_servers =*/ nullptr,
/*.progress_callback =*/ nullptr,
/*.progress_callback_user_data =*/ nullptr,
/*.kv_overrides =*/ nullptr,
@ -15460,7 +15480,9 @@ struct llama_model_quantize_params llama_model_quantize_default_params() {
}
size_t llama_max_devices(void) {
#if defined(GGML_USE_METAL)
#if defined(GGML_USE_RPC)
return GGML_RPC_MAX_SERVERS;
#elif defined(GGML_USE_METAL)
return 1;
#elif defined(GGML_USE_CUDA)
return GGML_CUDA_MAX_DEVICES;
@ -15483,7 +15505,7 @@ bool llama_supports_mlock(void) {
bool llama_supports_gpu_offload(void) {
#if defined(GGML_USE_CUDA) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) || defined(GGML_USE_VULKAN) || \
defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE)
defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE) || defined(GGML_USE_RPC)
// Defined when llama.cpp is compiled with support for offloading model layers to GPU.
return true;
#else
@ -15546,7 +15568,17 @@ struct llama_model * llama_load_model_from_file(
return true;
};
}
if (params.rpc_servers != nullptr) {
// split the servers set them into model->rpc_servers
std::string servers(params.rpc_servers);
size_t pos = 0;
while ((pos = servers.find(",")) != std::string::npos) {
std::string server = servers.substr(0, pos);
model->rpc_servers.push_back(server);
servers.erase(0, pos + 1);
}
model->rpc_servers.push_back(servers);
}
int status = llama_model_load(path_model, *model, params);
GGML_ASSERT(status <= 0);
if (status < 0) {
@ -15693,7 +15725,17 @@ struct llama_context * llama_new_context_with_model(
if (!hparams.vocab_only) {
// initialize backends
#ifdef GGML_USE_METAL
#if defined(GGML_USE_RPC)
for (auto & server : model->rpc_servers) {
ggml_backend_t backend = ggml_backend_rpc_init(server.c_str());
if (backend == nullptr) {
LLAMA_LOG_ERROR("%s: failed to connect RPC backend to %s\n", __func__, server.c_str());
llama_free(ctx);
return nullptr;
}
ctx->backends.push_back(backend);
}
#elif defined(GGML_USE_METAL)
if (model->n_gpu_layers > 0) {
ctx->backend_metal = ggml_backend_metal_init();
if (ctx->backend_metal == nullptr) {
@ -15850,7 +15892,7 @@ struct llama_context * llama_new_context_with_model(
// enabling pipeline parallelism in the scheduler increases memory usage, so it is only done when necessary
bool pipeline_parallel =
llama_get_device_count() > 1 &&
llama_get_device_count(*model) > 1 &&
model->n_gpu_layers > (int)model->hparams.n_layer &&
model->split_mode == LLAMA_SPLIT_MODE_LAYER &&
params.offload_kqv;

3
llama.h
View File

@ -242,6 +242,9 @@ extern "C" {
// proportion of the model (layers or rows) to offload to each GPU, size: llama_max_devices()
const float * tensor_split;
// comma separated list of RPC servers to use for offloading
const char * rpc_servers;
// Called with a progress value between 0.0 and 1.0. Pass NULL to disable.
// If the provided progress_callback returns true, model loading continues.
// If it returns false, model loading is immediately aborted.