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llama : remove LLAMA_MAX_DEVICES from llama.h

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ggml-ci
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Georgi Gerganov 2024-01-31 15:51:23 +02:00
parent efb7bdbbd0
commit 43312b2039
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GPG key ID: BF970631944C16B7
6 changed files with 61 additions and 55 deletions
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6
common/common.cpp
View file

@ -637,11 +637,11 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
const std::regex regex{R"([,/]+)"};
std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1};
std::vector<std::string> split_arg{it, {}};
if (split_arg.size() >= LLAMA_MAX_DEVICES) {
if (split_arg.size() >= llama_max_devices()) {
invalid_param = true;
break;
}
for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i) {
for (size_t i = 0; i < llama_max_devices(); ++i) {
if (i < split_arg.size()) {
params.tensor_split[i] = std::stof(split_arg[i]);
} else {
@ -1651,7 +1651,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
fprintf(stream, "cont_batching: %s # default: false\n", params.cont_batching ? "true" : "false");
fprintf(stream, "temp: %f # default: 0.8\n", sparams.temp);
const std::vector<float> tensor_split_vector(params.tensor_split, params.tensor_split + LLAMA_MAX_DEVICES);
const std::vector<float> tensor_split_vector(params.tensor_split, params.tensor_split + llama_max_devices());
dump_vector_float_yaml(stream, "tensor_split", tensor_split_vector);
fprintf(stream, "tfs: %f # default: 1.0\n", sparams.tfs_z);

66
common/common.h
View file

@ -43,40 +43,40 @@ extern char const *LLAMA_BUILD_TARGET;
int32_t get_num_physical_cores();
struct gpt_params {
uint32_t seed = -1; // RNG seed
uint32_t seed = -1; // RNG seed
int32_t n_threads = get_num_physical_cores();
int32_t n_threads_draft = -1;
int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads)
int32_t n_threads_batch_draft = -1;
int32_t n_predict = -1; // new tokens to predict
int32_t n_ctx = 512; // context size
int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS)
int32_t n_keep = 0; // number of tokens to keep from initial prompt
int32_t n_draft = 8; // number of tokens to draft during speculative decoding
int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited)
int32_t n_parallel = 1; // number of parallel sequences to decode
int32_t n_sequences = 1; // number of sequences to decode
float p_accept = 0.5f; // speculative decoding accept probability
float p_split = 0.1f; // speculative decoding split probability
int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default)
int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default)
llama_split_mode split_mode = LLAMA_SPLIT_LAYER; // how to split the model across GPUs
int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs
int32_t n_beams = 0; // if non-zero then use beam search of given width.
int32_t grp_attn_n = 1; // group-attention factor
int32_t grp_attn_w = 512; // group-attention width
int32_t n_print = -1; // print token count every n tokens (-1 = disabled)
float rope_freq_base = 0.0f; // RoPE base frequency
float rope_freq_scale = 0.0f; // RoPE frequency scaling factor
float yarn_ext_factor = -1.0f; // YaRN extrapolation mix factor
float yarn_attn_factor = 1.0f; // YaRN magnitude scaling factor
float yarn_beta_fast = 32.0f; // YaRN low correction dim
float yarn_beta_slow = 1.0f; // YaRN high correction dim
int32_t yarn_orig_ctx = 0; // YaRN original context length
int8_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED; // TODO: better to be int32_t for alignment
// pinging @cebtenzzre
int32_t n_threads = get_num_physical_cores();
int32_t n_threads_draft = -1;
int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads)
int32_t n_threads_batch_draft = -1;
int32_t n_predict = -1; // new tokens to predict
int32_t n_ctx = 512; // context size
int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS)
int32_t n_keep = 0; // number of tokens to keep from initial prompt
int32_t n_draft = 8; // number of tokens to draft during speculative decoding
int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited)
int32_t n_parallel = 1; // number of parallel sequences to decode
int32_t n_sequences = 1; // number of sequences to decode
float p_accept = 0.5f; // speculative decoding accept probability
float p_split = 0.1f; // speculative decoding split probability
int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default)
int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default)
llama_split_mode split_mode = LLAMA_SPLIT_LAYER; // how to split the model across GPUs
int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
float tensor_split[128] = {0}; // how split tensors should be distributed across GPUs
int32_t n_beams = 0; // if non-zero then use beam search of given width.
int32_t grp_attn_n = 1; // group-attention factor
int32_t grp_attn_w = 512; // group-attention width
int32_t n_print = -1; // print token count every n tokens (-1 = disabled)
float rope_freq_base = 0.0f; // RoPE base frequency
float rope_freq_scale = 0.0f; // RoPE frequency scaling factor
float yarn_ext_factor = -1.0f; // YaRN extrapolation mix factor
float yarn_attn_factor = 1.0f; // YaRN magnitude scaling factor
float yarn_beta_fast = 32.0f; // YaRN low correction dim
float yarn_beta_slow = 1.0f; // YaRN high correction dim
int32_t yarn_orig_ctx = 0; // YaRN original context length
int8_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED; // TODO: better to be int32_t for alignment
// pinging @cebtenzzre
// // sampling parameters
struct llama_sampling_params sparams;

2
examples/batched-bench/batched-bench.cpp
View file

@ -88,7 +88,7 @@ int main(int argc, char ** argv) {
llama_model_params model_params = llama_model_default_params();
const std::vector<float> t_split (LLAMA_MAX_DEVICES, 0.0f);
const std::vector<float> t_split(llama_max_devices(), 0.0f);
model_params.n_gpu_layers = n_gpu_layers;
model_params.tensor_split = t_split.data();

16
examples/llama-bench/llama-bench.cpp
View file

@ -160,7 +160,7 @@ struct cmd_params {
std::vector<int> main_gpu;
std::vector<bool> no_kv_offload;
std::vector<bool> mul_mat_q;
std::vector<std::array<float, LLAMA_MAX_DEVICES>> tensor_split;
std::vector<std::vector<float>> tensor_split;
int reps;
bool verbose;
output_formats output_format;
@ -179,7 +179,7 @@ static const cmd_params cmd_params_defaults = {
/* main_gpu */ {0},
/* no_kv_offload */ {false},
/* mul_mat_q */ {true},
/* tensor_split */ {{}},
/* tensor_split */ {std::vector<float>(llama_max_devices(), 0.0f)},
/* reps */ 5,
/* verbose */ false,
/* output_format */ MARKDOWN
@ -380,10 +380,10 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
const std::regex regex{R"([;/]+)"};
std::sregex_token_iterator it{ts.begin(), ts.end(), regex, -1};
std::vector<std::string> split_arg{it, {}};
GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);
GGML_ASSERT(split_arg.size() <= llama_max_devices());
std::array<float, LLAMA_MAX_DEVICES> tensor_split;
for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i) {
std::vector<float> tensor_split(llama_max_devices());
for (size_t i = 0; i < llama_max_devices(); ++i) {
if (i < split_arg.size()) {
tensor_split[i] = std::stof(split_arg[i]);
} else {
@ -459,7 +459,7 @@ struct cmd_params_instance {
int main_gpu;
bool no_kv_offload;
bool mul_mat_q;
std::array<float, LLAMA_MAX_DEVICES> tensor_split;
std::vector<float> tensor_split;
llama_model_params to_llama_mparams() const {
llama_model_params mparams = llama_model_default_params();
@ -582,7 +582,7 @@ struct test {
int main_gpu;
bool no_kv_offload;
bool mul_mat_q;
std::array<float, LLAMA_MAX_DEVICES> tensor_split;
std::vector<float> tensor_split;
int n_prompt;
int n_gen;
std::string test_time;
@ -704,7 +704,7 @@ struct test {
std::vector<std::string> get_values() const {
std::string tensor_split_str;
int max_nonzero = 0;
for (int i = 0; i < LLAMA_MAX_DEVICES; i++) {
for (size_t i = 0; i < llama_max_devices(); i++) {
if (tensor_split[i] > 0) {
max_nonzero = i;
}

12
llama.cpp
View file

@ -10090,8 +10090,16 @@ struct llama_model_quantize_params llama_model_quantize_default_params() {
return result;
}
int32_t llama_max_devices(void) {
return LLAMA_MAX_DEVICES;
size_t llama_max_devices(void) {
#if defined(GGML_USE_METAL)
return 1;
#elif defined(GGML_USE_CUDA)
return GGML_CUDA_MAX_DEVICES;
#elif defined(GGML_USE_SYCL)
return GGML_SYCL_MAX_DEVICES;
#else
return 1;
#endif
}
bool llama_mmap_supported(void) {

14
llama.h
View file

@ -5,13 +5,10 @@
#include "ggml-backend.h"
#ifdef GGML_USE_CUBLAS
#include "ggml-cuda.h"
#define LLAMA_MAX_DEVICES GGML_CUDA_MAX_DEVICES
#elif defined(GGML_USE_SYCL)
#include "ggml-sycl.h"
#define LLAMA_MAX_DEVICES GGML_SYCL_MAX_DEVICES
#else
#define LLAMA_MAX_DEVICES 1
#endif // GGML_USE_CUBLAS
#endif
#include <stddef.h>
#include <stdint.h>
#include <stdio.h>
@ -50,7 +47,7 @@
#define LLAMA_SESSION_VERSION 4
#if defined(GGML_USE_CUBLAS) || 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 when llama.cpp is compiled with support for offloading model layers to GPU.
#define LLAMA_SUPPORTS_GPU_OFFLOAD
#endif
@ -201,7 +198,7 @@ extern "C" {
// LLAMA_SPLIT_LAYER: ignored
int32_t main_gpu;
// proportion of the model (layers or rows) to offload to each GPU, size: LLAMA_MAX_DEVICES
// proportion of the model (layers or rows) to offload to each GPU, size: llama_max_devices()
const float * tensor_split;
// Called with a progress value between 0.0 and 1.0. Pass NULL to disable.
@ -338,7 +335,8 @@ extern "C" {
LLAMA_API int64_t llama_time_us(void);
LLAMA_API int32_t llama_max_devices(void);
LLAMA_API size_t llama_max_devices(void);
LLAMA_API bool llama_mmap_supported (void);
LLAMA_API bool llama_mlock_supported(void);