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fit-params : refactor + add option to output estimated memory per device (#22171)

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* fit-params : add option to output estimated memory per device

* cont : minor

* cont : refactor

* cont : move fit params implementation to libcommon

* cont : header

* cont : headers

* cont : codeowners
This commit is contained in:
Georgi Gerganov 2026-04-21 09:54:36 +03:00 • committed by GitHub
parent ff6b1062af
commit cfe9838d26
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19 changed files with 1123 additions and 980 deletions
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1
CODEOWNERS
View file

@ -23,6 +23,7 @@
/ci/ @ggerganov
/cmake/ @ggerganov
/common/ @ggml-org/llama-common
/common/fit.* @JohannesGaessler
/common/jinja/ @CISC
/common/ngram-map.* @srogmann
/convert_*.py @CISC

2
common/CMakeLists.txt
View file

@ -73,6 +73,8 @@ add_library(${TARGET}
debug.h
download.cpp
download.h
fit.cpp
fit.h
hf-cache.cpp
hf-cache.h
http.h

14
common/arg.cpp
View file

@ -2426,6 +2426,20 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
}
}
).set_env("LLAMA_ARG_FIT"));
add_opt(common_arg(
{ "-fitp", "--fit-print" }, "[on|off]",
string_format("print the estimated required memory ('on' or 'off', default: '%s')", params.fit_params_print ? "on" : "off"),
[](common_params & params, const std::string & value) {
if (is_truthy(value)) {
params.fit_params_print = true;
} else if (is_falsey(value)) {
params.fit_params_print = false;
} else {
throw std::runtime_error(
string_format("error: unknown value for --fit-print: '%s'\n", value.c_str()));
}
}
).set_examples({LLAMA_EXAMPLE_FIT_PARAMS}).set_env("LLAMA_ARG_FIT_ESTIMATE"));
add_opt(common_arg(
{ "-fitt", "--fit-target" }, "MiB0,MiB1,MiB2,...",
string_format("target margin per device for --fit, comma-separated list of values, "

3
common/common.cpp
View file

@ -3,6 +3,7 @@
#include "build-info.h"
#include "common.h"
#include "fit.h"
#include "log.h"
#include "llama.h"
#include "sampling.h"
@ -1147,7 +1148,7 @@ common_init_result::common_init_result(common_params & params) :
if (params.fit_params) {
LOG_INF("%s: fitting params to device memory, for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on\n", __func__);
llama_params_fit(params.model.path.c_str(), &mparams, &cparams,
common_fit_params(params.model.path.c_str(), &mparams, &cparams,
params.tensor_split,
params.tensor_buft_overrides.data(),
params.fit_params_target.data(),

11
common/common.h
View file

@ -420,11 +420,12 @@ struct common_params {
// offload params
std::vector<ggml_backend_dev_t> devices; // devices to use for offloading
int32_t n_gpu_layers = -1; // number of layers to store in VRAM, -1 is auto, <= -2 is all
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
bool fit_params = true; // whether to fit unset model/context parameters to free device memory
int32_t fit_params_min_ctx = 4096; // minimum context size to set when trying to reduce memory use
int32_t n_gpu_layers = -1; // number of layers to store in VRAM, -1 is auto, <= -2 is all
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
bool fit_params = true; // whether to fit unset model/context parameters to free device memory
bool fit_params_print = false; // print the estimated required memory to run the model
int32_t fit_params_min_ctx = 4096; // minimum context size to set when trying to reduce memory use
// margin per device in bytes for fitting parameters to free memory:
std::vector<size_t> fit_params_target = std::vector<size_t>(llama_max_devices(), 1024 * 1024*1024);

951
common/fit.cpp Normal file
View file

@ -0,0 +1,951 @@
#include "fit.h"
#include "log.h"
#include "../src/llama-ext.h"
#include <array>
#include <cassert>
#include <stdexcept>
#include <cinttypes>
#include <set>
#include <string>
#include <vector>
// this enum is only used in llama_params_fit_impl but needs to be defined outside of it to fix a Windows compilation issue
// enum to identify part of a layer for distributing its tensors:
enum common_layer_fraction_t {
LAYER_FRACTION_NONE = 0, // nothing
LAYER_FRACTION_ATTN = 1, // attention
LAYER_FRACTION_UP = 2, // attention + up
LAYER_FRACTION_GATE = 3, // attention + up + gate
LAYER_FRACTION_MOE = 4, // everything but sparse MoE weights
};
class common_params_fit_exception : public std::runtime_error {
using std::runtime_error::runtime_error;
};
static std::vector<llama_device_memory_data> common_get_device_memory_data(
const char * path_model,
const llama_model_params * mparams,
const llama_context_params * cparams,
std::vector<ggml_backend_dev_t> & devs,
uint32_t & hp_ngl,
uint32_t & hp_n_ctx_train,
uint32_t & hp_n_expert,
ggml_log_level log_level) {
struct user_data_t {
struct {
ggml_log_callback callback;
void * user_data;
} original_logger;
ggml_log_level min_level; // prints below this log level go to debug log
};
user_data_t ud;
llama_log_get(&ud.original_logger.callback, &ud.original_logger.user_data);
ud.min_level = log_level;
llama_log_set([](ggml_log_level level, const char * text, void * user_data) {
const user_data_t * ud = (const user_data_t *) user_data;
const ggml_log_level level_eff = level >= ud->min_level ? level : GGML_LOG_LEVEL_DEBUG;
ud->original_logger.callback(level_eff, text, ud->original_logger.user_data);
}, &ud);
llama_model_params mparams_copy = *mparams;
mparams_copy.no_alloc = true;
mparams_copy.use_mmap = false;
mparams_copy.use_mlock = false;
llama_model * model = llama_model_load_from_file(path_model, mparams_copy);
if (model == nullptr) {
llama_log_set(ud.original_logger.callback, ud.original_logger.user_data);
throw std::runtime_error("failed to load model");
}
llama_context * ctx = llama_init_from_model(model, *cparams);
if (ctx == nullptr) {
llama_model_free(model);
llama_log_set(ud.original_logger.callback, ud.original_logger.user_data);
throw std::runtime_error("failed to create llama_context from model");
}
const size_t nd = llama_model_n_devices(model);
std::vector<llama_device_memory_data> ret(nd + 1);
llama_memory_breakdown memory_breakdown = llama_get_memory_breakdown(ctx);
for (const auto & [buft, mb] : memory_breakdown) {
if (ggml_backend_buft_is_host(buft)) {
ret.back().mb.model += mb.model;
ret.back().mb.context += mb.context;
ret.back().mb.compute += mb.compute;
continue;
}
ggml_backend_dev_t dev = ggml_backend_buft_get_device(buft);
if (!dev) {
continue;
}
for (size_t i = 0; i < nd; i++) {
if (dev == llama_model_get_device(model, i)) {
ret[i].mb.model += mb.model;
ret[i].mb.context += mb.context;
ret[i].mb.compute += mb.compute;
break;
}
}
}
{
ggml_backend_dev_t cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
if (cpu_dev == nullptr) {
throw std::runtime_error("no CPU backend found");
}
size_t free;
size_t total;
ggml_backend_dev_memory(cpu_dev, &free, &total);
ret.back().free = free;
ret.back().total = total;
}
for (size_t i = 0; i < nd; i++) {
size_t free;
size_t total;
ggml_backend_dev_memory(llama_model_get_device(model, i), &free, &total);
// devices can return 0 bytes for free and total memory if they do not
// have any to report. in this case, we will use the host memory as a fallback
// fixes: https://github.com/ggml-org/llama.cpp/issues/18577
if (free == 0 && total == 0) {
free = ret.back().free;
total = ret.back().total;
}
ret[i].free = free;
ret[i].total = total;
}
devs.clear();
for (int i = 0; i < llama_model_n_devices(model); i++) {
devs.push_back(llama_model_get_device(model, i));
}
hp_ngl = llama_model_n_layer(model);
hp_n_ctx_train = llama_model_n_ctx_train(model);
hp_n_expert = llama_model_n_expert(model);
common_memory_breakdown_print(ctx);
llama_free(ctx);
llama_model_free(model);
llama_log_set(ud.original_logger.callback, ud.original_logger.user_data);
return ret;
}
static void common_params_fit_impl(
const char * path_model, struct llama_model_params * mparams, struct llama_context_params * cparams,
float * tensor_split, struct llama_model_tensor_buft_override * tensor_buft_overrides,
size_t * margins_s, uint32_t n_ctx_min, enum ggml_log_level log_level) {
if (mparams->split_mode == LLAMA_SPLIT_MODE_TENSOR) {
throw common_params_fit_exception("llama_params_fit is not implemented for SPLIT_MODE_TENSOR, abort");
}
constexpr int64_t MiB = 1024*1024;
typedef std::vector<llama_device_memory_data> dmds_t;
const llama_model_params default_mparams = llama_model_default_params();
std::vector<ggml_backend_dev_t> devs;
uint32_t hp_ngl = 0; // hparams.n_gpu_layers
uint32_t hp_nct = 0; // hparams.n_ctx_train
uint32_t hp_nex = 0; // hparams.n_expert
// step 1: get data for default parameters and check whether any changes are necessary in the first place
LOG_INF("%s: getting device memory data for initial parameters:\n", __func__);
const dmds_t dmds_full = common_get_device_memory_data(path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
const size_t nd = devs.size(); // number of devices
std::vector<int64_t> margins; // this function uses int64_t rather than size_t for memory sizes to more conveniently handle deficits
margins.reserve(nd);
if (nd == 0) {
margins.push_back(margins_s[0]);
} else {
for (size_t id = 0; id < nd; id++) {
margins.push_back(margins_s[id]);
}
}
std::vector<std::string> dev_names;
{
dev_names.reserve(nd);
size_t max_length = 0;
for (const auto & dev : devs) {
std::string name = ggml_backend_dev_name(dev);
name += " (";
name += ggml_backend_dev_description(dev);
name += ")";
dev_names.push_back(name);
max_length = std::max(max_length, name.length());
}
for (std::string & dn : dev_names) {
dn.insert(dn.end(), max_length - dn.length(), ' ');
}
}
int64_t sum_free = 0;
int64_t sum_projected_free = 0;
int64_t sum_projected_used = 0;
int64_t sum_projected_model = 0;
std::vector<int64_t> projected_free_per_device;
projected_free_per_device.reserve(nd);
if (nd == 0) {
sum_projected_used = dmds_full.back().mb.total();
sum_free = dmds_full.back().total;
sum_projected_free = sum_free - sum_projected_used;
LOG_INF("%s: projected to use %" PRId64 " MiB of host memory vs. %" PRId64 " MiB of total host memory\n",
__func__, sum_projected_used/MiB, sum_free/MiB);
if (sum_projected_free >= margins[0]) {
LOG_INF("%s: will leave %" PRId64 " >= %" PRId64 " MiB of system memory, no changes needed\n",
__func__, sum_projected_free/MiB, margins[0]/MiB);
return;
}
} else {
if (nd > 1) {
LOG_INF("%s: projected memory use with initial parameters [MiB]:\n", __func__);
}
for (size_t id = 0; id < nd; id++) {
const llama_device_memory_data & dmd = dmds_full[id];
const int64_t projected_used = dmd.mb.total();
const int64_t projected_free = dmd.free - projected_used;
projected_free_per_device.push_back(projected_free);
sum_free += dmd.free;
sum_projected_used += projected_used;
sum_projected_free += projected_free;
sum_projected_model += dmd.mb.model;
if (nd > 1) {
LOG_INF("%s: - %s: %6" PRId64 " total, %6" PRId64 " used, %6" PRId64 " free vs. target of %6" PRId64 "\n",
__func__, dev_names[id].c_str(), dmd.total/MiB, projected_used/MiB, projected_free/MiB, margins[id]/MiB);
}
}
assert(sum_free >= 0 && sum_projected_used >= 0);
LOG_INF("%s: projected to use %" PRId64 " MiB of device memory vs. %" PRId64 " MiB of free device memory\n",
__func__, sum_projected_used/MiB, sum_free/MiB);
if (nd == 1) {
if (projected_free_per_device[0] >= margins[0]) {
LOG_INF("%s: will leave %" PRId64 " >= %" PRId64 " MiB of free device memory, no changes needed\n",
__func__, projected_free_per_device[0]/MiB, margins[0]/MiB);
return;
}
} else {
bool changes_needed = false;
for (size_t id = 0; id < nd; id++) {
if (projected_free_per_device[id] < margins[id]) {
changes_needed = true;
break;
}
}
if (!changes_needed) {
LOG_INF("%s: targets for free memory can be met on all devices, no changes needed\n", __func__);
return;
}
}
}
// step 2: try reducing memory use by reducing the context size
{
int64_t global_surplus = sum_projected_free;
if (nd == 0) {
global_surplus -= margins[0];
} else {
for (size_t id = 0; id < nd; id++) {
global_surplus -= margins[id];
}
}
if (global_surplus < 0) {
if (nd <= 1) {
LOG_INF("%s: cannot meet free memory target of %" PRId64 " MiB, need to reduce device memory by %" PRId64 " MiB\n",
__func__, margins[0]/MiB, -global_surplus/MiB);
} else {
LOG_INF(
"%s: cannot meet free memory targets on all devices, need to use %" PRId64 " MiB less in total\n",
__func__, -global_surplus/MiB);
}
if (cparams->n_ctx == 0) {
if (hp_nct > n_ctx_min) {
int64_t sum_used_target = sum_free;
if (nd == 0) {
sum_used_target -= margins[0];
} else {
for (size_t id = 0; id < nd; id++) {
sum_used_target -= margins[id];
}
}
if (nd > 1) {
// for multiple devices we need to be more conservative in terms of how much context we think can fit:
// - for dense models only whole layers can be assigned to devices
// - for MoE models only whole tensors can be assigned to devices, which we estimate to be <= 1/3 of a layer
// - on average we expect a waste of 0.5 layers/tensors per device
// - use slightly more than the expected average for nd devices to be safe
const int64_t model_per_layer = sum_projected_model / std::min(uint32_t(mparams->n_gpu_layers), hp_ngl);
sum_used_target -= (nd + 1) * model_per_layer / (hp_nex == 0 ? 2 : 6);
}
int64_t sum_projected_used_min_ctx = 0;
cparams->n_ctx = n_ctx_min;
const dmds_t dmds_min_ctx = common_get_device_memory_data(path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
if (nd == 0) {
sum_projected_used_min_ctx = dmds_min_ctx.back().mb.total();
} else {
for (size_t id = 0; id < nd; id++) {
sum_projected_used_min_ctx += dmds_min_ctx[id].mb.total();
}
}
if (sum_used_target > sum_projected_used_min_ctx) {
// linear interpolation between minimum and maximum context size:
cparams->n_ctx += (hp_nct - n_ctx_min) * (sum_used_target - sum_projected_used_min_ctx)
/ (sum_projected_used - sum_projected_used_min_ctx);
cparams->n_ctx = std::max(cparams->n_ctx - cparams->n_ctx % 256, n_ctx_min); // round down context for CUDA backend
const int64_t bytes_per_ctx = (sum_projected_used - sum_projected_used_min_ctx) / (hp_nct - n_ctx_min);
const int64_t memory_reduction = (hp_nct - cparams->n_ctx) * bytes_per_ctx;
LOG_INF("%s: context size reduced from %" PRIu32 " to %" PRIu32 " -> need %" PRId64 " MiB less memory in total\n",
__func__, hp_nct, cparams->n_ctx, memory_reduction/MiB);
if (nd <= 1) {
LOG_INF("%s: entire model can be fit by reducing context\n", __func__);
return;
}
LOG_INF("%s: entire model should be fit across devices by reducing context\n", __func__);
} else {
const int64_t memory_reduction = sum_projected_used - sum_projected_used_min_ctx;
LOG_INF("%s: context size reduced from %" PRIu32 " to %" PRIu32 " -> need %" PRId64 " MiB less memory in total\n",
__func__, hp_nct, cparams->n_ctx, memory_reduction/MiB);
}
} else {
if (n_ctx_min == UINT32_MAX) {
LOG_INF("%s: user has requested full context size of %" PRIu32 " -> no change\n", __func__, hp_nct);
} else {
LOG_INF("%s: default model context size is %" PRIu32 " which is <= the min. context size of %" PRIu32 " -> no change\n",
__func__, hp_nct, n_ctx_min);
}
}
} else {
LOG_INF("%s: context size set by user to %" PRIu32 " -> no change\n", __func__, cparams->n_ctx);
}
}
}
if (nd == 0) {
throw common_params_fit_exception("was unable to fit model into system memory by reducing context, abort");
}
if (mparams->n_gpu_layers != default_mparams.n_gpu_layers) {
throw common_params_fit_exception("n_gpu_layers already set by user to " + std::to_string(mparams->n_gpu_layers) + ", abort");
}
if (nd > 1) {
if (!tensor_split) {
throw common_params_fit_exception("did not provide a buffer to write the tensor_split to, abort");
}
if (mparams->tensor_split) {
for (size_t id = 0; id < nd; id++) {
if (mparams->tensor_split[id] != 0.0f) {
throw common_params_fit_exception("model_params::tensor_split already set by user, abort");
}
}
}
if (mparams->split_mode == LLAMA_SPLIT_MODE_ROW) {
throw common_params_fit_exception("changing weight allocation for LLAMA_SPLIT_MODE_ROW not implemented, abort");
}
}
if (!tensor_buft_overrides) {
throw common_params_fit_exception("did not provide buffer to set tensor_buft_overrides, abort");
}
if (mparams->tensor_buft_overrides && (mparams->tensor_buft_overrides->pattern || mparams->tensor_buft_overrides->buft)) {
throw common_params_fit_exception("model_params::tensor_buft_overrides already set by user, abort");
}
// step 3: iteratively fill the back to front with "dense" layers
// - for a dense model simply fill full layers, giving each device a contiguous slice of the model
// - for a MoE model, same as dense model but with all MoE tensors in system memory
// utility function that returns a static C string matching the tensors for a specific layer index and layer fraction:
auto get_overflow_pattern = [&](const size_t il, const common_layer_fraction_t lf) -> const char * {
constexpr size_t n_strings = 1000;
if (il >= n_strings) {
throw std::runtime_error("at most " + std::to_string(n_strings) + " model layers are supported");
}
switch (lf) {
case LAYER_FRACTION_ATTN: {
static std::array<std::string, n_strings> patterns;
if (patterns[il].empty()) {
patterns[il] = "blk\\." + std::to_string(il) + "\\.ffn_(gate|up|gate_up|down).*";
}
return patterns[il].c_str();
}
case LAYER_FRACTION_UP: {
static std::array<std::string, n_strings> patterns;
if (patterns[il].empty()) {
patterns[il] = "blk\\." + std::to_string(il) + "\\.ffn_(gate|gate_up|down).*";
}
return patterns[il].c_str();
}
case LAYER_FRACTION_GATE: {
static std::array<std::string, n_strings> patterns;
if (patterns[il].empty()) {
patterns[il] = "blk\\." + std::to_string(il) + "\\.ffn_down.*";
}
return patterns[il].c_str();
}
case LAYER_FRACTION_MOE: {
static std::array<std::string, n_strings> patterns;
if (patterns[il].empty()) {
patterns[il] = "blk\\." + std::to_string(il) + "\\.ffn_(up|down|gate_up|gate)_(ch|)exps";
}
return patterns[il].c_str();
}
default:
GGML_ABORT("fatal error");
}
};
struct ngl_t {
uint32_t n_layer = 0; // number of total layers
uint32_t n_part = 0; // number of partial layers, <= n_layer
// for the first partial layer varying parts can overflow, all further layers use LAYER_FRACTION_MOE:
common_layer_fraction_t overflow_type = LAYER_FRACTION_MOE;
uint32_t n_full() const {
assert(n_layer >= n_part);
return n_layer - n_part;
}
};
const size_t ntbo = llama_max_tensor_buft_overrides();
// utility function to set n_gpu_layers and tensor_split
auto set_ngl_tensor_split_tbo = [&](
const std::vector<ngl_t> & ngl_per_device,
const std::vector<ggml_backend_buffer_type_t> & overflow_bufts,
llama_model_params & mparams) {
mparams.n_gpu_layers = 0;
for (size_t id = 0; id < nd; id++) {
mparams.n_gpu_layers += ngl_per_device[id].n_layer;
if (nd > 1) {
tensor_split[id] = ngl_per_device[id].n_layer;
}
}
assert(uint32_t(mparams.n_gpu_layers) <= hp_ngl + 1);
uint32_t il0 = hp_ngl + 1 - mparams.n_gpu_layers; // start index for tensor buft overrides
mparams.tensor_split = tensor_split;
size_t itbo = 0;
for (size_t id = 0; id < nd; id++) {
il0 += ngl_per_device[id].n_full();
for (uint32_t il = il0; il < il0 + ngl_per_device[id].n_part; il++) {
if (itbo + 1 >= ntbo) {
tensor_buft_overrides[itbo].pattern = nullptr;
tensor_buft_overrides[itbo].buft = nullptr;
itbo++;
mparams.tensor_buft_overrides = tensor_buft_overrides;
throw common_params_fit_exception("llama_max_tensor_buft_overrides() == "
+ std::to_string(ntbo) + " is insufficient for model");
}
tensor_buft_overrides[itbo].pattern = get_overflow_pattern(il, il == il0 ? ngl_per_device[id].overflow_type : LAYER_FRACTION_MOE);
tensor_buft_overrides[itbo].buft = il == il0 ? overflow_bufts[id] : ggml_backend_cpu_buffer_type();
itbo++;
}
il0 += ngl_per_device[id].n_part;
}
tensor_buft_overrides[itbo].pattern = nullptr;
tensor_buft_overrides[itbo].buft = nullptr;
itbo++;
mparams.tensor_buft_overrides = tensor_buft_overrides;
};
// utility function that returns the memory use per device for given numbers of layers per device
auto get_memory_for_layers = [&](
const char * func_name,
const std::vector<ngl_t> & ngl_per_device,
const std::vector<ggml_backend_buffer_type_t> & overflow_bufts) -> std::vector<int64_t> {
llama_model_params mparams_copy = *mparams;
set_ngl_tensor_split_tbo(ngl_per_device, overflow_bufts, mparams_copy);
const dmds_t dmd_nl = common_get_device_memory_data(
path_model, &mparams_copy, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
LOG_INF("%s: memory for test allocation by device:\n", func_name);
for (size_t id = 0; id < nd; id++) {
const ngl_t & n = ngl_per_device[id];
LOG_INF(
"%s: id=%zu, n_layer=%2" PRIu32 ", n_part=%2" PRIu32 ", overflow_type=%d, mem=%6" PRId64 " MiB\n",
func_name, id, n.n_layer, n.n_part, int(n.overflow_type), dmd_nl[id].mb.total()/MiB);
}
std::vector<int64_t> ret;
ret.reserve(nd);
for (size_t id = 0; id < nd; id++) {
ret.push_back(dmd_nl[id].mb.total());
}
return ret;
};
int64_t global_surplus_cpu_moe = 0;
if (hp_nex > 0) {
const static std::string pattern_moe_all = "blk\\.\\d+\\.ffn_(up|down|gate_up|gate)_(ch|)exps"; // matches all MoE tensors
ggml_backend_buffer_type_t cpu_buft = ggml_backend_cpu_buffer_type();
tensor_buft_overrides[0] = {pattern_moe_all.c_str(), cpu_buft};
tensor_buft_overrides[1] = {nullptr, nullptr};
mparams->tensor_buft_overrides = tensor_buft_overrides;
LOG_INF("%s: getting device memory data with all MoE tensors moved to system memory:\n", __func__);
const dmds_t dmds_cpu_moe = common_get_device_memory_data(
path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
for (size_t id = 0; id < nd; id++) {
global_surplus_cpu_moe += dmds_cpu_moe[id].free;
global_surplus_cpu_moe -= int64_t(dmds_cpu_moe[id].mb.total()) + margins[id];
}
if (global_surplus_cpu_moe > 0) {
LOG_INF("%s: with only dense weights in device memory there is a total surplus of %" PRId64 " MiB\n",
__func__, global_surplus_cpu_moe/MiB);
} else {
LOG_INF("%s: with only dense weights in device memory there is still a total deficit of %" PRId64 " MiB\n",
__func__, -global_surplus_cpu_moe/MiB);
}
// reset
tensor_buft_overrides[0] = {nullptr, nullptr};
mparams->tensor_buft_overrides = tensor_buft_overrides;
}
std::vector<int64_t> targets; // maximum acceptable memory use per device
targets.reserve(nd);
for (size_t id = 0; id < nd; id++) {
targets.push_back(dmds_full[id].free - margins[id]);
LOG_INF("%s: id=%zu, target=%" PRId64 " MiB\n", __func__, id, targets[id]/MiB);
}
std::vector<ggml_backend_buffer_type_t> overflow_bufts; // which bufts the first partial layer of a device overflows to:
overflow_bufts.reserve(nd);
for (size_t id = 0; id < nd; id++) {
overflow_bufts.push_back(ggml_backend_cpu_buffer_type());
}
std::vector<ngl_t> ngl_per_device(nd);
std::vector<int64_t> mem = get_memory_for_layers(__func__, ngl_per_device, overflow_bufts);
// optimize the number of layers per device using the method of false position:
// - ngl_per_device has 0 layers for each device, lower bound
// - try a "high" configuration where a device is given all unassigned layers
// - interpolate the memory use / layer between low and high linearly to get a guess where it meets our target
// - check memory use of our guess, replace either the low or high bound
// - once we only have a difference of a single layer, stop and return the lower bound that just barely still fits
// - the last device has the output layer, which cannot be a partial layer
if (hp_nex == 0) {
LOG_INF("%s: filling dense layers back-to-front:\n", __func__);
} else {
LOG_INF("%s: filling dense-only layers back-to-front:\n", __func__);
}
for (int id = nd - 1; id >= 0; id--) {
uint32_t n_unassigned = hp_ngl + 1;
for (size_t jd = id + 1; jd < nd; ++jd) {
assert(n_unassigned >= ngl_per_device[jd].n_layer);
n_unassigned -= ngl_per_device[jd].n_layer;
}
std::vector<ngl_t> ngl_per_device_high = ngl_per_device;
ngl_per_device_high[id].n_layer = n_unassigned;
if (hp_nex > 0) {
ngl_per_device_high[id].n_part = size_t(id) < nd - 1 ? ngl_per_device_high[id].n_layer : ngl_per_device_high[id].n_layer - 1;
}
if (ngl_per_device_high[id].n_layer > 0) {
std::vector<int64_t> mem_high = get_memory_for_layers(__func__, ngl_per_device_high, overflow_bufts);
if (mem_high[id] > targets[id]) {
assert(ngl_per_device_high[id].n_layer > ngl_per_device[id].n_layer);
uint32_t delta = ngl_per_device_high[id].n_layer - ngl_per_device[id].n_layer;
LOG_INF("%s: start filling device %" PRIu32 ", delta=%" PRIu32 "\n", __func__, id, delta);
while (delta > 1) {
uint32_t step_size = int64_t(delta) * (targets[id] - mem[id]) / (mem_high[id] - mem[id]);
step_size = std::max(step_size, uint32_t(1));
step_size = std::min(step_size, delta - 1);
std::vector<ngl_t> ngl_per_device_test = ngl_per_device;
ngl_per_device_test[id].n_layer += step_size;
if (hp_nex) {
ngl_per_device_test[id].n_part += size_t(id) == nd - 1 && ngl_per_device_test[id].n_part == 0 ?
step_size - 1 : step_size; // the first layer is the output layer which must always be full
}
const std::vector<int64_t> mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts);
if (mem_test[id] <= targets[id]) {
ngl_per_device = ngl_per_device_test;
mem = mem_test;
LOG_INF("%s: set ngl_per_device[%d].n_layer=%" PRIu32 "\n", __func__, id, ngl_per_device[id].n_layer);
} else {
ngl_per_device_high = ngl_per_device_test;
mem_high = mem_test;
LOG_INF("%s: set ngl_per_device_high[%d].n_layer=%" PRIu32 "\n", __func__, id, ngl_per_device_high[id].n_layer);
}
delta = ngl_per_device_high[id].n_layer - ngl_per_device[id].n_layer;
}
} else {
assert(ngl_per_device_high[id].n_layer == n_unassigned);
ngl_per_device = ngl_per_device_high;
mem = mem_high;
LOG_INF("%s: set ngl_per_device[%d].n_layer=%" PRIu32 "\n", __func__, id, ngl_per_device[id].n_layer);
}
}
const int64_t projected_margin = dmds_full[id].free - mem[id];
LOG_INF(
"%s: - %s: %2" PRIu32 " layers, %6" PRId64 " MiB used, %6" PRId64 " MiB free\n",
__func__, dev_names[id].c_str(), ngl_per_device[id].n_layer, mem[id]/MiB, projected_margin/MiB);
}
if (hp_nex == 0 || global_surplus_cpu_moe <= 0) {
set_ngl_tensor_split_tbo(ngl_per_device, overflow_bufts, *mparams);
return;
}
// step 4: for a MoE model where all dense tensors fit,
// convert the dense-only layers in the back to full layers in the front until all devices are full
// essentially the same procedure as for the dense-only layers except front-to-back
// also, try fitting at least part of one more layer to reduce waste for "small" GPUs with e.g. 24 GiB VRAM
size_t id_dense_start = nd;
for (int id = nd - 1; id >= 0; id--) {
if (ngl_per_device[id].n_layer > 0) {
id_dense_start = id;
continue;
}
break;
}
assert(id_dense_start < nd);
LOG_INF("%s: converting dense-only layers to full layers and filling them front-to-back with overflow to next device/system memory:\n", __func__);
for (size_t id = 0; id <= id_dense_start && id_dense_start < nd; id++) {
std::vector<ngl_t> ngl_per_device_high = ngl_per_device;
for (size_t jd = id_dense_start; jd < nd; jd++) {
const uint32_t n_layer_move = jd < nd - 1 ? ngl_per_device_high[jd].n_layer : ngl_per_device_high[jd].n_layer - 1;
ngl_per_device_high[id].n_layer += n_layer_move;
ngl_per_device_high[jd].n_layer -= n_layer_move;
ngl_per_device_high[jd].n_part = 0;
}
size_t id_dense_start_high = nd - 1;
std::vector<int64_t> mem_high = get_memory_for_layers(__func__, ngl_per_device_high, overflow_bufts);
if (mem_high[id] > targets[id]) {
assert(ngl_per_device_high[id].n_full() >= ngl_per_device[id].n_full());
uint32_t delta = ngl_per_device_high[id].n_full() - ngl_per_device[id].n_full();
while (delta > 1) {
uint32_t step_size = int64_t(delta) * (targets[id] - mem[id]) / (mem_high[id] - mem[id]);
step_size = std::max(step_size, uint32_t(1));
step_size = std::min(step_size, delta - 1);
std::vector<ngl_t> ngl_per_device_test = ngl_per_device;
size_t id_dense_start_test = id_dense_start;
uint32_t n_converted_test = 0;
for (;id_dense_start_test < nd; id_dense_start_test++) {
const uint32_t n_convert_jd = std::min(step_size - n_converted_test, ngl_per_device_test[id_dense_start_test].n_part);
ngl_per_device_test[id_dense_start_test].n_layer -= n_convert_jd;
ngl_per_device_test[id_dense_start_test].n_part -= n_convert_jd;
ngl_per_device_test[id].n_layer += n_convert_jd;
n_converted_test += n_convert_jd;
if (ngl_per_device_test[id_dense_start_test].n_part > 0) {
break;
}
}
const std::vector<int64_t> mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts);
if (mem_test[id] <= targets[id]) {
ngl_per_device = ngl_per_device_test;
mem = mem_test;
id_dense_start = id_dense_start_test;
LOG_INF("%s: set ngl_per_device[%zu].(n_layer, n_part)=(%" PRIu32 ", %" PRIu32 "), id_dense_start=%zu\n",
__func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start);
} else {
ngl_per_device_high = ngl_per_device_test;
mem_high = mem_test;
id_dense_start_high = id_dense_start_test;
LOG_INF("%s: set ngl_per_device_high[%zu].(n_layer, n_part)=(%" PRIu32 ", %" PRIu32 "), id_dense_start_high=%zu\n",
__func__, id, ngl_per_device_high[id].n_layer, ngl_per_device_high[id].n_part, id_dense_start_high);
}
assert(ngl_per_device_high[id].n_full() >= ngl_per_device[id].n_full());
delta = ngl_per_device_high[id].n_full() - ngl_per_device[id].n_full();
}
} else {
ngl_per_device = ngl_per_device_high;
mem = mem_high;
id_dense_start = id_dense_start_high;
LOG_INF("%s: set ngl_per_device[%zu].(n_layer, n_part)=(%" PRIu32 ", %" PRIu32 "), id_dense_start=%zu\n",
__func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start);
}
// try to fit at least part of one more layer
if (ngl_per_device[id_dense_start].n_layer > (id < nd - 1 ? 0 : 1)) {
std::vector<ngl_t> ngl_per_device_test = ngl_per_device;
size_t id_dense_start_test = id_dense_start;
ngl_per_device_test[id_dense_start_test].n_layer--;
ngl_per_device_test[id_dense_start_test].n_part--;
ngl_per_device_test[id].n_layer++;
ngl_per_device_test[id].n_part++;
if (ngl_per_device_test[id_dense_start_test].n_part == 0) {
id_dense_start_test++;
}
ngl_per_device_test[id].overflow_type = LAYER_FRACTION_UP;
std::vector<ggml_backend_buffer_type_t> overflow_bufts_test = overflow_bufts;
if (id < nd - 1) {
overflow_bufts_test[id] = ggml_backend_dev_buffer_type(devs[id + 1]);
}
LOG_INF("%s: trying to fit one extra layer with overflow_type=LAYER_FRACTION_UP\n", __func__);
std::vector<int64_t> mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts_test);
if (mem_test[id] < targets[id] && (id + 1 == nd || mem_test[id + 1] < targets[id + 1])) {
ngl_per_device = ngl_per_device_test;
overflow_bufts = overflow_bufts_test;
mem = mem_test;
id_dense_start = id_dense_start_test;
LOG_INF("%s: set ngl_per_device[%zu].(n_layer, n_part, overflow_type)=(%" PRIu32 ", %" PRIu32 ", UP), id_dense_start=%zu\n",
__func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start);
ngl_per_device_test[id].overflow_type = LAYER_FRACTION_GATE;
LOG_INF("%s: trying to fit one extra layer with overflow_type=LAYER_FRACTION_GATE\n", __func__);
mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts_test);
if (mem_test[id] < targets[id] && (id + 1 == nd || mem_test[id + 1] < targets[id + 1])) {
ngl_per_device = ngl_per_device_test;
overflow_bufts = overflow_bufts_test;
mem = mem_test;
id_dense_start = id_dense_start_test;
LOG_INF("%s: set ngl_per_device[%zu].(n_layer, n_part, overflow_type)=(%" PRIu32 ", %" PRIu32 ", GATE), id_dense_start=%zu\n",
__func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start);
}
} else {
ngl_per_device_test[id].overflow_type = LAYER_FRACTION_ATTN;
LOG_INF("%s: trying to fit one extra layer with overflow_type=LAYER_FRACTION_ATTN\n", __func__);
mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts_test);
if (mem_test[id] < targets[id] && (id + 1 == nd || mem_test[id + 1] < targets[id + 1])) {
ngl_per_device = ngl_per_device_test;
overflow_bufts = overflow_bufts_test;
mem = mem_test;
id_dense_start = id_dense_start_test;
LOG_INF("%s: set ngl_per_device[%zu].(n_layer, n_part, overflow_type)=(%" PRIu32 ", %" PRIu32 ", ATTN), id_dense_start=%zu\n",
__func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start);
}
}
}
const int64_t projected_margin = dmds_full[id].free - mem[id];
LOG_INF(
"%s: - %s: %2" PRIu32 " layers (%2" PRIu32 " overflowing), %6" PRId64 " MiB used, %6" PRId64 " MiB free\n",
__func__, dev_names[id].c_str(), ngl_per_device[id].n_layer, ngl_per_device[id].n_part, mem[id]/MiB, projected_margin/MiB);
}
// print info for devices that were not changed during the conversion from dense only to full layers:
for (size_t id = id_dense_start + 1; id < nd; id++) {
const int64_t projected_margin = dmds_full[id].free - mem[id];
LOG_INF(
"%s: - %s: %2" PRIu32 " layers (%2" PRIu32 " overflowing), %6" PRId64 " MiB used, %6" PRId64 " MiB free\n",
__func__, dev_names[id].c_str(), ngl_per_device[id].n_layer, ngl_per_device[id].n_part, mem[id]/MiB, projected_margin/MiB);
}
set_ngl_tensor_split_tbo(ngl_per_device, overflow_bufts, *mparams);
}
enum common_params_fit_status common_fit_params(
const char * path_model,
llama_model_params * mparams,
llama_context_params * cparams,
float * tensor_split,
llama_model_tensor_buft_override * tensor_buft_overrides,
size_t * margins,
uint32_t n_ctx_min,
ggml_log_level log_level) {
const int64_t t0_us = llama_time_us();
common_params_fit_status status = COMMON_PARAMS_FIT_STATUS_SUCCESS;
try {
common_params_fit_impl(path_model, mparams, cparams, tensor_split, tensor_buft_overrides, margins, n_ctx_min, log_level);
LOG_INF("%s: successfully fit params to free device memory\n", __func__);
} catch (const common_params_fit_exception & e) {
LOG_WRN("%s: failed to fit params to free device memory: %s\n", __func__, e.what());
status = COMMON_PARAMS_FIT_STATUS_FAILURE;
} catch (const std::runtime_error & e) {
LOG_ERR("%s: encountered an error while trying to fit params to free device memory: %s\n", __func__, e.what());
status = COMMON_PARAMS_FIT_STATUS_ERROR;
}
const int64_t t1_us = llama_time_us();
LOG_INF("%s: fitting params to free memory took %.2f seconds\n", __func__, (t1_us - t0_us) * 1e-6);
return status;
}
void common_memory_breakdown_print(const struct llama_context * ctx) {
//const auto & devices = ctx->get_model().devices;
const auto * model = llama_get_model(ctx);
std::vector<ggml_backend_dev_t> devices;
for (int i = 0; i < llama_model_n_devices(model); i++) {
devices.push_back(llama_model_get_device(model, i));
}
llama_memory_breakdown memory_breakdown = llama_get_memory_breakdown(ctx);
std::vector<std::array<std::string, 9>> table_data;
table_data.reserve(devices.size());
const std::string template_header = "%s: | %s | %s %s %s %s %s %s %s |\n";
const std::string template_gpu = "%s: | %s | %s = %s + (%s = %s + %s + %s) + %s |\n";
const std::string template_other = "%s: | %s | %s %s %s = %s + %s + %s %s |\n";
table_data.push_back({template_header, "memory breakdown [MiB]", "total", "free", "self", "model", "context", "compute", "unaccounted"});
constexpr size_t MiB = 1024 * 1024;
const std::vector<std::string> desc_prefixes_strip = {"NVIDIA ", "GeForce ", "Tesla ", "AMD ", "Radeon ", "Instinct "};
// track seen buffer types to avoid double counting:
std::set<ggml_backend_buffer_type_t> seen_buffer_types;
// accumulative memory breakdown for each device and for host:
std::vector<llama_memory_breakdown_data> mb_dev(devices.size());
llama_memory_breakdown_data mb_host;
for (const auto & buft_mb : memory_breakdown) {
ggml_backend_buffer_type_t buft = buft_mb.first;
const llama_memory_breakdown_data & mb = buft_mb.second;
if (ggml_backend_buft_is_host(buft)) {
mb_host.model += mb.model;
mb_host.context += mb.context;
mb_host.compute += mb.compute;
seen_buffer_types.insert(buft);
continue;
}
ggml_backend_dev_t dev = ggml_backend_buft_get_device(buft);
if (dev) {
int i_dev = -1;
for (size_t i = 0; i < devices.size(); i++) {
if (devices[i] == dev) {
i_dev = i;
break;
}
}
if (i_dev != -1) {
mb_dev[i_dev].model += mb.model;
mb_dev[i_dev].context += mb.context;
mb_dev[i_dev].compute += mb.compute;
seen_buffer_types.insert(buft);
continue;
}
}
}
// print memory breakdown for each device:
for (size_t i = 0; i < devices.size(); i++) {
ggml_backend_dev_t dev = devices[i];
llama_memory_breakdown_data mb = mb_dev[i];
const std::string name = ggml_backend_dev_name(dev);
std::string desc = ggml_backend_dev_description(dev);
for (const std::string & prefix : desc_prefixes_strip) {
if (desc.length() >= prefix.length() && desc.substr(0, prefix.length()) == prefix) {
desc = desc.substr(prefix.length());
}
}
size_t free, total;
ggml_backend_dev_memory(dev, &free, &total);
const size_t self = mb.model + mb.context + mb.compute;
const size_t unaccounted = total - self - free;
table_data.push_back({
template_gpu,
" - " + name + " (" + desc + ")",
std::to_string(total / MiB),
std::to_string(free / MiB),
std::to_string(self / MiB),
std::to_string(mb.model / MiB),
std::to_string(mb.context / MiB),
std::to_string(mb.compute / MiB),
std::to_string(unaccounted / MiB)});
}
// print memory breakdown for host:
{
const size_t self = mb_host.model + mb_host.context + mb_host.compute;
table_data.push_back({
template_other,
" - Host",
"", // total
"", // free
std::to_string(self / MiB),
std::to_string(mb_host.model / MiB),
std::to_string(mb_host.context / MiB),
std::to_string(mb_host.compute / MiB),
""}); // unaccounted
}
// print memory breakdown for all remaining buffer types:
for (const auto & buft_mb : memory_breakdown) {
ggml_backend_buffer_type_t buft = buft_mb.first;
const llama_memory_breakdown_data & mb = buft_mb.second;
if (seen_buffer_types.count(buft) == 1) {
continue;
}
const std::string name = ggml_backend_buft_name(buft);
const size_t self = mb.model + mb.context + mb.compute;
table_data.push_back({
template_other,
" - " + name,
"", // total
"", // free
std::to_string(self / MiB),
std::to_string(mb.model / MiB),
std::to_string(mb.context / MiB),
std::to_string(mb.compute / MiB),
""}); // unaccounted
seen_buffer_types.insert(buft);
}
for (size_t j = 1; j < table_data[0].size(); j++) {
size_t max_len = 0;
for (const auto & td : table_data) {
max_len = std::max(max_len, td[j].length());
}
for (auto & td : table_data) {
td[j].insert(j == 1 ? td[j].length() : 0, max_len - td[j].length(), ' ');
}
}
for (const auto & td : table_data) {
LOG_INF(td[0].c_str(),
__func__, td[1].c_str(), td[2].c_str(), td[3].c_str(), td[4].c_str(), td[5].c_str(),
td[6].c_str(), td[7].c_str(), td[8].c_str());
}
}
void common_fit_print(
const char * path_model,
llama_model_params * mparams,
llama_context_params * cparams) {
std::vector<ggml_backend_dev_t> devs;
uint32_t hp_ngl = 0; // hparams.n_gpu_layers
uint32_t hp_nct = 0; // hparams.n_ctx_train
uint32_t hp_nex = 0; // hparams.n_expert
auto dmd = common_get_device_memory_data(path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, GGML_LOG_LEVEL_ERROR);
GGML_ASSERT(dmd.size() == devs.size() + 1);
for (size_t id = 0; id < devs.size(); id++) {
printf("%s ", ggml_backend_dev_name(devs[id]));
printf("%zu ", dmd[id].mb.model/1024/1024);
printf("%zu ", dmd[id].mb.context/1024/1024);
printf("%zu ", dmd[id].mb.compute/1024/1024);
printf("\n");
}
printf("Host ");
printf("%zu ", dmd.back().mb.model/1024/1024);
printf("%zu ", dmd.back().mb.context/1024/1024);
printf("%zu ", dmd.back().mb.compute/1024/1024);
printf("\n");
}

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#pragma once
#include "ggml.h"
enum common_params_fit_status {
COMMON_PARAMS_FIT_STATUS_SUCCESS = 0, // found allocations that are projected to fit
COMMON_PARAMS_FIT_STATUS_FAILURE = 1, // could not find allocations that are projected to fit
COMMON_PARAMS_FIT_STATUS_ERROR = 2, // a hard error occurred, e.g. because no model could be found at the specified path
};
// fits mparams and cparams to free device memory (assumes system memory is unlimited)
// - returns true if the parameters could be successfully modified to fit device memory
// - this function is NOT thread safe because it modifies the global llama logger state
// - only parameters that have the same value as in llama_default_model_params are modified
// with the exception of the context size which is modified if and only if equal to 0
enum common_params_fit_status common_fit_params(
const char * path_model,
struct llama_model_params * mparams,
struct llama_context_params * cparams,
float * tensor_split, // writable buffer for tensor split, needs at least llama_max_devices elements
struct llama_model_tensor_buft_override * tensor_buft_overrides, // writable buffer for overrides, needs at least llama_max_tensor_buft_overrides elements
size_t * margins, // margins of memory to leave per device in bytes
uint32_t n_ctx_min, // minimum context size to set when trying to reduce memory use
enum ggml_log_level log_level); // minimum log level to print during fitting, lower levels go to debug log
// print estimated memory to stdout
void common_fit_print(
const char * path_model,
struct llama_model_params * mparams,
struct llama_context_params * cparams);
void common_memory_breakdown_print(const struct llama_context * ctx);

6
common/sampling.cpp
View file

@ -1,10 +1,12 @@
#include "sampling.h"
#include "common.h"
#include "ggml.h"
#include "fit.h"
#include "log.h"
#include "reasoning-budget.h"
#include "ggml.h"
#include <algorithm>
#include <cctype>
#include <climits>
@ -511,7 +513,7 @@ void common_perf_print(const struct llama_context * ctx, const struct common_sam
LOG_INF("%s: unaccounted time = %10.2f ms / %5.1f %% (total - sampling - prompt eval - eval) / (total)\n", __func__, t_unacc_ms, t_unacc_pc);
LOG_INF("%s: graphs reused = %10d\n", __func__, data.n_reused);
llama_memory_breakdown_print(ctx);
common_memory_breakdown_print(ctx);
}
}

24
include/llama.h
View file

@ -511,27 +511,6 @@ extern "C" {
// Frees all allocated memory
LLAMA_API void llama_free(struct llama_context * ctx);
enum llama_params_fit_status {
LLAMA_PARAMS_FIT_STATUS_SUCCESS = 0, // found allocations that are projected to fit
LLAMA_PARAMS_FIT_STATUS_FAILURE = 1, // could not find allocations that are projected to fit
LLAMA_PARAMS_FIT_STATUS_ERROR = 2, // a hard error occurred, e.g. because no model could be found at the specified path
};
// fits mparams and cparams to free device memory (assumes system memory is unlimited)
// - returns true if the parameters could be successfully modified to fit device memory
// - this function is NOT thread safe because it modifies the global llama logger state
// - only parameters that have the same value as in llama_default_model_params are modified
// with the exception of the context size which is modified if and only if equal to 0
LLAMA_API enum llama_params_fit_status llama_params_fit(
const char * path_model,
struct llama_model_params * mparams,
struct llama_context_params * cparams,
float * tensor_split, // writable buffer for tensor split, needs at least llama_max_devices elements
struct llama_model_tensor_buft_override * tensor_buft_overrides, // writable buffer for overrides, needs at least llama_max_tensor_buft_overrides elements
size_t * margins, // margins of memory to leave per device in bytes
uint32_t n_ctx_min, // minimum context size to set when trying to reduce memory use
enum ggml_log_level log_level); // minimum log level to print during fitting, lower levels go to debug log
LLAMA_API int64_t llama_time_us(void);
LLAMA_API size_t llama_max_devices(void);
@ -1546,9 +1525,6 @@ extern "C" {
LLAMA_API void llama_perf_sampler_print(const struct llama_sampler * chain);
LLAMA_API void llama_perf_sampler_reset( struct llama_sampler * chain);
// print a breakdown of per-device memory use via LLAMA_LOG:
LLAMA_API void llama_memory_breakdown_print(const struct llama_context * ctx);
//
// training
//

146
src/llama-context.cpp
View file

@ -2636,7 +2636,7 @@ void llama_context::perf_reset() {
n_reused = 0;
}
std::map<ggml_backend_buffer_type_t, llama_memory_breakdown_data> llama_context::memory_breakdown() const {
llama_memory_breakdown llama_context::memory_breakdown() const {
std::map<ggml_backend_buffer_type_t, llama_memory_breakdown_data> ret;
for (const auto & [buft, size] : model.memory_breakdown()) {
ret[buft].model += size;
@ -3493,142 +3493,6 @@ void llama_perf_context_reset(llama_context * ctx) {
ctx->perf_reset();
}
void llama_memory_breakdown_print(const struct llama_context * ctx) {
const auto & devices = ctx->get_model().devices;
std::map<ggml_backend_buffer_type_t, llama_memory_breakdown_data> memory_breakdown = ctx->memory_breakdown();
std::vector<std::array<std::string, 9>> table_data;
table_data.reserve(devices.size());
const std::string template_header = "%s: | %s | %s %s %s %s %s %s %s |\n";
const std::string template_gpu = "%s: | %s | %s = %s + (%s = %s + %s + %s) + %s |\n";
const std::string template_other = "%s: | %s | %s %s %s = %s + %s + %s %s |\n";
table_data.push_back({template_header, "memory breakdown [MiB]", "total", "free", "self", "model", "context", "compute", "unaccounted"});
constexpr size_t MiB = 1024 * 1024;
const std::vector<std::string> desc_prefixes_strip = {"NVIDIA ", "GeForce ", "Tesla ", "AMD ", "Radeon ", "Instinct "};
// track seen buffer types to avoid double counting:
std::set<ggml_backend_buffer_type_t> seen_buffer_types;
// accumulative memory breakdown for each device and for host:
std::vector<llama_memory_breakdown_data> mb_dev(devices.size());
llama_memory_breakdown_data mb_host;
for (const auto & buft_mb : memory_breakdown) {
ggml_backend_buffer_type_t buft = buft_mb.first;
const llama_memory_breakdown_data & mb = buft_mb.second;
if (ggml_backend_buft_is_host(buft)) {
mb_host.model += mb.model;
mb_host.context += mb.context;
mb_host.compute += mb.compute;
seen_buffer_types.insert(buft);
continue;
}
ggml_backend_dev_t dev = ggml_backend_buft_get_device(buft);
if (dev) {
int i_dev = -1;
for (size_t i = 0; i < devices.size(); i++) {
if (devices[i].dev == dev) {
i_dev = i;
break;
}
}
if (i_dev != -1) {
mb_dev[i_dev].model += mb.model;
mb_dev[i_dev].context += mb.context;
mb_dev[i_dev].compute += mb.compute;
seen_buffer_types.insert(buft);
continue;
}
}
}
// print memory breakdown for each device:
for (size_t i = 0; i < devices.size(); i++) {
ggml_backend_dev_t dev = devices[i].dev;
llama_memory_breakdown_data mb = mb_dev[i];
const std::string name = ggml_backend_dev_name(dev);
std::string desc = ggml_backend_dev_description(dev);
for (const std::string & prefix : desc_prefixes_strip) {
if (desc.length() >= prefix.length() && desc.substr(0, prefix.length()) == prefix) {
desc = desc.substr(prefix.length());
}
}
size_t free, total;
ggml_backend_dev_memory(dev, &free, &total);
const size_t self = mb.model + mb.context + mb.compute;
const size_t unaccounted = total - self - free;
table_data.push_back({
template_gpu,
" - " + name + " (" + desc + ")",
std::to_string(total / MiB),
std::to_string(free / MiB),
std::to_string(self / MiB),
std::to_string(mb.model / MiB),
std::to_string(mb.context / MiB),
std::to_string(mb.compute / MiB),
std::to_string(unaccounted / MiB)});
}
// print memory breakdown for host:
{
const size_t self = mb_host.model + mb_host.context + mb_host.compute;
table_data.push_back({
template_other,
" - Host",
"", // total
"", // free
std::to_string(self / MiB),
std::to_string(mb_host.model / MiB),
std::to_string(mb_host.context / MiB),
std::to_string(mb_host.compute / MiB),
""}); // unaccounted
}
// print memory breakdown for all remaining buffer types:
for (const auto & buft_mb : memory_breakdown) {
ggml_backend_buffer_type_t buft = buft_mb.first;
const llama_memory_breakdown_data & mb = buft_mb.second;
if (seen_buffer_types.count(buft) == 1) {
continue;
}
const std::string name = ggml_backend_buft_name(buft);
const size_t self = mb.model + mb.context + mb.compute;
table_data.push_back({
template_other,
" - " + name,
"", // total
"", // free
std::to_string(self / MiB),
std::to_string(mb.model / MiB),
std::to_string(mb.context / MiB),
std::to_string(mb.compute / MiB),
""}); // unaccounted
seen_buffer_types.insert(buft);
}
for (size_t j = 1; j < table_data[0].size(); j++) {
size_t max_len = 0;
for (const auto & td : table_data) {
max_len = std::max(max_len, td[j].length());
}
for (auto & td : table_data) {
td[j].insert(j == 1 ? td[j].length() : 0, max_len - td[j].length(), ' ');
}
}
for (const auto & td : table_data) {
LLAMA_LOG_INFO(td[0].c_str(),
__func__, td[1].c_str(), td[2].c_str(), td[3].c_str(), td[4].c_str(), td[5].c_str(),
td[6].c_str(), td[7].c_str(), td[8].c_str());
}
}
//
// training
//
@ -3659,3 +3523,11 @@ void llama_opt_epoch(
callback_train,
callback_eval);
}
//
// ext
//
llama_memory_breakdown llama_get_memory_breakdown(const struct llama_context * ctx) {
return ctx->memory_breakdown();
}

14
src/llama-context.h
View file

@ -1,6 +1,7 @@
#pragma once
#include "llama.h"
#include "llama-ext.h"
#include "llama-cparams.h"
#include "llama-graph.h"
#include "llama-adapter.h"
@ -22,17 +23,6 @@ class llama_io_write_i;
struct llama_memory_i;
struct llama_memory_context_i;
// "memory" as in physical memory for a buffer type, in bytes
struct llama_memory_breakdown_data {
size_t model = 0; // memory allocated for the model
size_t context = 0; // memory allocated for the context
size_t compute = 0; // memory allocated for temporary compute buffers
size_t total() const {
return model + context + compute;
}
};
struct llama_context {
// init scheduler and compute buffers, reserve worst-case graphs
llama_context(
@ -172,7 +162,7 @@ struct llama_context {
llama_perf_context_data perf_get_data() const;
void perf_reset();
std::map<ggml_backend_buffer_type_t, llama_memory_breakdown_data> memory_breakdown() const;
llama_memory_breakdown memory_breakdown() const;
//
// training

36
src/llama-ext.h
View file

@ -1,8 +1,12 @@
#pragma once
// this is a staging header for new llama.cpp API
// breaking changes and C++ are allowed. everything here should be considered WIP
#include "llama.h"
#include <cstdint>
#include <map>
// Reserve a new compute graph. It is valid until the next call to llama_graph_reserve.
LLAMA_API struct ggml_cgraph * llama_graph_reserve(
@ -14,7 +18,6 @@ LLAMA_API struct ggml_cgraph * llama_graph_reserve(
// Get the default ggml_type for a given ftype.
LLAMA_API ggml_type llama_ftype_get_default_type(llama_ftype ftype);
// Quantization state.
struct quantize_state_impl;
LLAMA_API quantize_state_impl * llama_quant_init(
@ -54,3 +57,34 @@ LLAMA_API void llama_quant_compute_types(
ggml_tensor ** tensors,
ggml_type * result_types,
size_t n_tensors);
//
// device memory querying
//
// "memory" as in physical memory for a buffer type, in bytes
struct llama_memory_breakdown_data {
size_t model = 0; // memory allocated for the model
size_t context = 0; // memory allocated for the context
size_t compute = 0; // memory allocated for temporary compute buffers
size_t total() const {
return model + context + compute;
}
};
struct llama_device_memory_data {
int64_t total;
int64_t free;
llama_memory_breakdown_data mb;
};
// TODO: convert to C-style data structure
using llama_memory_breakdown = std::map<ggml_backend_buffer_type_t, llama_memory_breakdown_data>;
int32_t llama_model_n_expert (const struct llama_model * model);
int32_t llama_model_n_devices(const struct llama_model * model);
ggml_backend_dev_t llama_model_get_device(const struct llama_model * model, int i);
llama_memory_breakdown llama_get_memory_breakdown(const struct llama_context * ctx);

16
src/llama-model.cpp
View file

@ -1,6 +1,7 @@
#include "llama-model.h"
#include "llama-arch.h"
#include "llama-ext.h"
#include "llama-hparams.h"
#include "llama-impl.h"
#include "llama-mmap.h"
@ -9449,3 +9450,18 @@ bool llama_model_is_diffusion(const llama_model * model) {
const std::vector<std::pair<std::string, ggml_tensor *>> & llama_internal_get_tensor_map(const llama_model * model) {
return model->tensors_by_name;
}
int32_t llama_model_n_expert(const struct llama_model * model) {
return model->hparams.n_expert;
}
int32_t llama_model_n_devices(const struct llama_model * model) {
return (int32_t)model->devices.size();
}
ggml_backend_dev_t llama_model_get_device(const struct llama_model * model, int i) {
if (i < 0 || i >= (int)model->devices.size()) {
return nullptr;
}
return model->devices[i].dev;
}

760
src/llama.cpp
View file

@ -46,766 +46,6 @@ const char * llama_flash_attn_type_name(enum llama_flash_attn_type flash_attn_ty
GGML_ABORT("fatal error");
}
struct llama_device_memory_data {
int64_t total;
int64_t free;
llama_memory_breakdown_data mb;
};
static std::vector<llama_device_memory_data> llama_get_device_memory_data(
const char * path_model, const llama_model_params * mparams, const llama_context_params * cparams,
std::vector<llama_device> & devs, uint32_t & hp_ngl, uint32_t & hp_n_ctx_train, uint32_t & hp_n_expert,
const ggml_log_level log_level) {
struct user_data_t {
struct {
ggml_log_callback callback;
void * user_data;
} original_logger;
ggml_log_level min_level; // prints below this log level go to debug log
};
user_data_t ud;
llama_log_get(&ud.original_logger.callback, &ud.original_logger.user_data);
ud.min_level = log_level;
llama_log_set([](ggml_log_level level, const char * text, void * user_data) {
const user_data_t * ud = (const user_data_t *) user_data;
const ggml_log_level level_eff = level >= ud->min_level ? level : GGML_LOG_LEVEL_DEBUG;
ud->original_logger.callback(level_eff, text, ud->original_logger.user_data);
}, &ud);
llama_model_params mparams_copy = *mparams;
mparams_copy.no_alloc = true;
mparams_copy.use_mmap = false;
mparams_copy.use_mlock = false;
llama_model * model = llama_model_load_from_file(path_model, mparams_copy);
if (model == nullptr) {
llama_log_set(ud.original_logger.callback, ud.original_logger.user_data);
throw std::runtime_error("failed to load model");
}
llama_context * ctx = llama_init_from_model(model, *cparams);
if (ctx == nullptr) {
llama_model_free(model);
llama_log_set(ud.original_logger.callback, ud.original_logger.user_data);
throw std::runtime_error("failed to create llama_context from model");
}
const size_t nd = model->n_devices();
std::vector<llama_device_memory_data> ret(nd + 1);
std::map<ggml_backend_buffer_type_t, llama_memory_breakdown_data> memory_breakdown = ctx->memory_breakdown();
for (const auto & [buft, mb] : memory_breakdown) {
if (ggml_backend_buft_is_host(buft)) {
ret.back().mb.model += mb.model;
ret.back().mb.context += mb.context;
ret.back().mb.compute += mb.compute;
continue;
}
ggml_backend_dev_t dev = ggml_backend_buft_get_device(buft);
if (!dev) {
continue;
}
for (size_t i = 0; i < nd; i++) {
if (model->devices[i].dev == dev) {
ret[i].mb.model += mb.model;
ret[i].mb.context += mb.context;
ret[i].mb.compute += mb.compute;
break;
}
}
}
{
ggml_backend_dev_t cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
if (cpu_dev == nullptr) {
throw std::runtime_error(format("%s: no CPU backend found", __func__));
}
size_t free;
size_t total;
ggml_backend_dev_memory(cpu_dev, &free, &total);
ret.back().free = free;
ret.back().total = total;
}
for (size_t i = 0; i < nd; i++) {
size_t free;
size_t total;
ggml_backend_dev_memory(model->devices[i].dev, &free, &total);
// devices can return 0 bytes for free and total memory if they do not
// have any to report. in this case, we will use the host memory as a fallback
// fixes: https://github.com/ggml-org/llama.cpp/issues/18577
if (free == 0 && total == 0) {
free = ret.back().free;
total = ret.back().total;
}
ret[i].free = free;
ret[i].total = total;
}
devs = model->devices;
hp_ngl = model->hparams.n_layer;
hp_n_ctx_train = model->hparams.n_ctx_train;
hp_n_expert = model->hparams.n_expert;
llama_memory_breakdown_print(ctx); // goes to debug log
llama_free(ctx);
llama_model_free(model);
llama_log_set(ud.original_logger.callback, ud.original_logger.user_data);
return ret;
}
// enum to identify part of a layer for distributing its tensors:
enum layer_fraction_t {
LAYER_FRACTION_NONE = 0, // nothing
LAYER_FRACTION_ATTN = 1, // attention
LAYER_FRACTION_UP = 2, // attention + up
LAYER_FRACTION_GATE = 3, // attention + up + gate
LAYER_FRACTION_MOE = 4, // everything but sparse MoE weights
};
// this enum is only used in llama_params_fit_impl but needs to be defined outside of it to fix a Windows compilation issue
class llama_params_fit_exception : public std::runtime_error {
using std::runtime_error::runtime_error;
};
static void llama_params_fit_impl(
const char * path_model, struct llama_model_params * mparams, struct llama_context_params * cparams,
float * tensor_split, struct llama_model_tensor_buft_override * tensor_buft_overrides,
size_t * margins_s, uint32_t n_ctx_min, enum ggml_log_level log_level) {
if (mparams->split_mode == LLAMA_SPLIT_MODE_TENSOR) {
throw llama_params_fit_exception("llama_params_fit is not implemented for SPLIT_MODE_TENSOR, abort");
}
constexpr int64_t MiB = 1024*1024;
typedef std::vector<llama_device_memory_data> dmds_t;
const llama_model_params default_mparams = llama_model_default_params();
std::vector<llama_device> devs;
uint32_t hp_ngl = 0; // hparams.n_gpu_layers
uint32_t hp_nct = 0; // hparams.n_ctx_train
uint32_t hp_nex = 0; // hparams.n_expert
// step 1: get data for default parameters and check whether any changes are necessary in the first place
LLAMA_LOG_DEBUG("%s: getting device memory data for initial parameters:\n", __func__);
const dmds_t dmds_full = llama_get_device_memory_data(path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
const size_t nd = devs.size(); // number of devices
std::vector<int64_t> margins; // this function uses int64_t rather than size_t for memory sizes to more conveniently handle deficits
margins.reserve(nd);
if (nd == 0) {
margins.push_back(margins_s[0]);
} else {
for (size_t id = 0; id < nd; id++) {
margins.push_back(margins_s[id]);
}
}
std::vector<std::string> dev_names;
{
dev_names.reserve(nd);
size_t max_length = 0;
for (const llama_device & dev : devs) {
std::string name = ggml_backend_dev_name(dev.dev);
name += " (";
name += ggml_backend_dev_description(dev.dev);
name += ")";
dev_names.push_back(name);
max_length = std::max(max_length, name.length());
}
for (std::string & dn : dev_names) {
dn.insert(dn.end(), max_length - dn.length(), ' ');
}
}
int64_t sum_free = 0;
int64_t sum_projected_free = 0;
int64_t sum_projected_used = 0;
int64_t sum_projected_model = 0;
std::vector<int64_t> projected_free_per_device;
projected_free_per_device.reserve(nd);
if (nd == 0) {
sum_projected_used = dmds_full.back().mb.total();
sum_free = dmds_full.back().total;
sum_projected_free = sum_free - sum_projected_used;
LLAMA_LOG_INFO("%s: projected to use %" PRId64 " MiB of host memory vs. %" PRId64 " MiB of total host memory\n",
__func__, sum_projected_used/MiB, sum_free/MiB);
if (sum_projected_free >= margins[0]) {
LLAMA_LOG_INFO("%s: will leave %" PRId64 " >= %" PRId64 " MiB of system memory, no changes needed\n",
__func__, sum_projected_free/MiB, margins[0]/MiB);
return;
}
} else {
if (nd > 1) {
LLAMA_LOG_INFO("%s: projected memory use with initial parameters [MiB]:\n", __func__);
}
for (size_t id = 0; id < nd; id++) {
const llama_device_memory_data & dmd = dmds_full[id];
const int64_t projected_used = dmd.mb.total();
const int64_t projected_free = dmd.free - projected_used;
projected_free_per_device.push_back(projected_free);
sum_free += dmd.free;
sum_projected_used += projected_used;
sum_projected_free += projected_free;
sum_projected_model += dmd.mb.model;
if (nd > 1) {
LLAMA_LOG_INFO("%s: - %s: %6" PRId64 " total, %6" PRId64 " used, %6" PRId64 " free vs. target of %6" PRId64 "\n",
__func__, dev_names[id].c_str(), dmd.total/MiB, projected_used/MiB, projected_free/MiB, margins[id]/MiB);
}
}
assert(sum_free >= 0 && sum_projected_used >= 0);
LLAMA_LOG_INFO("%s: projected to use %" PRId64 " MiB of device memory vs. %" PRId64 " MiB of free device memory\n",
__func__, sum_projected_used/MiB, sum_free/MiB);
if (nd == 1) {
if (projected_free_per_device[0] >= margins[0]) {
LLAMA_LOG_INFO("%s: will leave %" PRId64 " >= %" PRId64 " MiB of free device memory, no changes needed\n",
__func__, projected_free_per_device[0]/MiB, margins[0]/MiB);
return;
}
} else {
bool changes_needed = false;
for (size_t id = 0; id < nd; id++) {
if (projected_free_per_device[id] < margins[id]) {
changes_needed = true;
break;
}
}
if (!changes_needed) {
LLAMA_LOG_INFO("%s: targets for free memory can be met on all devices, no changes needed\n", __func__);
return;
}
}
}
// step 2: try reducing memory use by reducing the context size
{
int64_t global_surplus = sum_projected_free;
if (nd == 0) {
global_surplus -= margins[0];
} else {
for (size_t id = 0; id < nd; id++) {
global_surplus -= margins[id];
}
}
if (global_surplus < 0) {
if (nd <= 1) {
LLAMA_LOG_INFO("%s: cannot meet free memory target of %" PRId64 " MiB, need to reduce device memory by %" PRId64 " MiB\n",
__func__, margins[0]/MiB, -global_surplus/MiB);
} else {
LLAMA_LOG_INFO(
"%s: cannot meet free memory targets on all devices, need to use %" PRId64 " MiB less in total\n",
__func__, -global_surplus/MiB);
}
if (cparams->n_ctx == 0) {
if (hp_nct > n_ctx_min) {
int64_t sum_used_target = sum_free;
if (nd == 0) {
sum_used_target -= margins[0];
} else {
for (size_t id = 0; id < nd; id++) {
sum_used_target -= margins[id];
}
}
if (nd > 1) {
// for multiple devices we need to be more conservative in terms of how much context we think can fit:
// - for dense models only whole layers can be assigned to devices
// - for MoE models only whole tensors can be assigned to devices, which we estimate to be <= 1/3 of a layer
// - on average we expect a waste of 0.5 layers/tensors per device
// - use slightly more than the expected average for nd devices to be safe
const int64_t model_per_layer = sum_projected_model / std::min(uint32_t(mparams->n_gpu_layers), hp_ngl);
sum_used_target -= (nd + 1) * model_per_layer / (hp_nex == 0 ? 2 : 6);
}
int64_t sum_projected_used_min_ctx = 0;
cparams->n_ctx = n_ctx_min;
const dmds_t dmds_min_ctx = llama_get_device_memory_data(path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
if (nd == 0) {
sum_projected_used_min_ctx = dmds_min_ctx.back().mb.total();
} else {
for (size_t id = 0; id < nd; id++) {
sum_projected_used_min_ctx += dmds_min_ctx[id].mb.total();
}
}
if (sum_used_target > sum_projected_used_min_ctx) {
// linear interpolation between minimum and maximum context size:
cparams->n_ctx += (hp_nct - n_ctx_min) * (sum_used_target - sum_projected_used_min_ctx)
/ (sum_projected_used - sum_projected_used_min_ctx);
cparams->n_ctx = std::max(cparams->n_ctx - cparams->n_ctx % 256, n_ctx_min); // round down context for CUDA backend
const int64_t bytes_per_ctx = (sum_projected_used - sum_projected_used_min_ctx) / (hp_nct - n_ctx_min);
const int64_t memory_reduction = (hp_nct - cparams->n_ctx) * bytes_per_ctx;
LLAMA_LOG_INFO("%s: context size reduced from %" PRIu32 " to %" PRIu32 " -> need %" PRId64 " MiB less memory in total\n",
__func__, hp_nct, cparams->n_ctx, memory_reduction/MiB);
if (nd <= 1) {
LLAMA_LOG_INFO("%s: entire model can be fit by reducing context\n", __func__);
return;
}
LLAMA_LOG_INFO("%s: entire model should be fit across devices by reducing context\n", __func__);
} else {
const int64_t memory_reduction = sum_projected_used - sum_projected_used_min_ctx;
LLAMA_LOG_INFO("%s: context size reduced from %" PRIu32 " to %" PRIu32 " -> need %" PRId64 " MiB less memory in total\n",
__func__, hp_nct, cparams->n_ctx, memory_reduction/MiB);
}
} else {
if (n_ctx_min == UINT32_MAX) {
LLAMA_LOG_INFO("%s: user has requested full context size of %" PRIu32 " -> no change\n", __func__, hp_nct);
} else {
LLAMA_LOG_INFO("%s: default model context size is %" PRIu32 " which is <= the min. context size of %" PRIu32 " -> no change\n",
__func__, hp_nct, n_ctx_min);
}
}
} else {
LLAMA_LOG_INFO("%s: context size set by user to %" PRIu32 " -> no change\n", __func__, cparams->n_ctx);
}
}
}
if (nd == 0) {
throw llama_params_fit_exception("was unable to fit model into system memory by reducing context, abort");
}
if (mparams->n_gpu_layers != default_mparams.n_gpu_layers) {
throw llama_params_fit_exception("n_gpu_layers already set by user to " + std::to_string(mparams->n_gpu_layers) + ", abort");
}
if (nd > 1) {
if (!tensor_split) {
throw llama_params_fit_exception("did not provide a buffer to write the tensor_split to, abort");
}
if (mparams->tensor_split) {
for (size_t id = 0; id < nd; id++) {
if (mparams->tensor_split[id] != 0.0f) {
throw llama_params_fit_exception("model_params::tensor_split already set by user, abort");
}
}
}
if (mparams->split_mode == LLAMA_SPLIT_MODE_ROW) {
throw llama_params_fit_exception("changing weight allocation for LLAMA_SPLIT_MODE_ROW not implemented, abort");
}
}
if (!tensor_buft_overrides) {
throw llama_params_fit_exception("did not provide buffer to set tensor_buft_overrides, abort");
}
if (mparams->tensor_buft_overrides && (mparams->tensor_buft_overrides->pattern || mparams->tensor_buft_overrides->buft)) {
throw llama_params_fit_exception("model_params::tensor_buft_overrides already set by user, abort");
}
// step 3: iteratively fill the back to front with "dense" layers
// - for a dense model simply fill full layers, giving each device a contiguous slice of the model
// - for a MoE model, same as dense model but with all MoE tensors in system memory
// utility function that returns a static C string matching the tensors for a specific layer index and layer fraction:
auto get_overflow_pattern = [&](const size_t il, const layer_fraction_t lf) -> const char * {
constexpr size_t n_strings = 1000;
if (il >= n_strings) {
throw std::runtime_error("at most " + std::to_string(n_strings) + " model layers are supported");
}
switch (lf) {
case LAYER_FRACTION_ATTN: {
static std::array<std::string, n_strings> patterns;
if (patterns[il].empty()) {
patterns[il] = "blk\\." + std::to_string(il) + "\\.ffn_(gate|up|gate_up|down).*";
}
return patterns[il].c_str();
}
case LAYER_FRACTION_UP: {
static std::array<std::string, n_strings> patterns;
if (patterns[il].empty()) {
patterns[il] = "blk\\." + std::to_string(il) + "\\.ffn_(gate|gate_up|down).*";
}
return patterns[il].c_str();
}
case LAYER_FRACTION_GATE: {
static std::array<std::string, n_strings> patterns;
if (patterns[il].empty()) {
patterns[il] = "blk\\." + std::to_string(il) + "\\.ffn_down.*";
}
return patterns[il].c_str();
}
case LAYER_FRACTION_MOE: {
static std::array<std::string, n_strings> patterns;
if (patterns[il].empty()) {
patterns[il] = "blk\\." + std::to_string(il) + "\\.ffn_(up|down|gate_up|gate)_(ch|)exps";
}
return patterns[il].c_str();
}
default:
GGML_ABORT("fatal error");
}
};
struct ngl_t {
uint32_t n_layer = 0; // number of total layers
uint32_t n_part = 0; // number of partial layers, <= n_layer
// for the first partial layer varying parts can overflow, all further layers use LAYER_FRACTION_MOE:
layer_fraction_t overflow_type = LAYER_FRACTION_MOE;
uint32_t n_full() const {
assert(n_layer >= n_part);
return n_layer - n_part;
}
};
const size_t ntbo = llama_max_tensor_buft_overrides();
// utility function to set n_gpu_layers and tensor_split
auto set_ngl_tensor_split_tbo = [&](
const std::vector<ngl_t> & ngl_per_device,
const std::vector<ggml_backend_buffer_type_t> & overflow_bufts,
llama_model_params & mparams) {
mparams.n_gpu_layers = 0;
for (size_t id = 0; id < nd; id++) {
mparams.n_gpu_layers += ngl_per_device[id].n_layer;
if (nd > 1) {
tensor_split[id] = ngl_per_device[id].n_layer;
}
}
assert(uint32_t(mparams.n_gpu_layers) <= hp_ngl + 1);
uint32_t il0 = hp_ngl + 1 - mparams.n_gpu_layers; // start index for tensor buft overrides
mparams.tensor_split = tensor_split;
size_t itbo = 0;
for (size_t id = 0; id < nd; id++) {
il0 += ngl_per_device[id].n_full();
for (uint32_t il = il0; il < il0 + ngl_per_device[id].n_part; il++) {
if (itbo + 1 >= ntbo) {
tensor_buft_overrides[itbo].pattern = nullptr;
tensor_buft_overrides[itbo].buft = nullptr;
itbo++;
mparams.tensor_buft_overrides = tensor_buft_overrides;
throw llama_params_fit_exception("llama_max_tensor_buft_overrides() == "
+ std::to_string(ntbo) + " is insufficient for model");
}
tensor_buft_overrides[itbo].pattern = get_overflow_pattern(il, il == il0 ? ngl_per_device[id].overflow_type : LAYER_FRACTION_MOE);
tensor_buft_overrides[itbo].buft = il == il0 ? overflow_bufts[id] : ggml_backend_cpu_buffer_type();
itbo++;
}
il0 += ngl_per_device[id].n_part;
}
tensor_buft_overrides[itbo].pattern = nullptr;
tensor_buft_overrides[itbo].buft = nullptr;
itbo++;
mparams.tensor_buft_overrides = tensor_buft_overrides;
};
// utility function that returns the memory use per device for given numbers of layers per device
auto get_memory_for_layers = [&](
const char * func_name,
const std::vector<ngl_t> & ngl_per_device,
const std::vector<ggml_backend_buffer_type_t> & overflow_bufts) -> std::vector<int64_t> {
llama_model_params mparams_copy = *mparams;
set_ngl_tensor_split_tbo(ngl_per_device, overflow_bufts, mparams_copy);
const dmds_t dmd_nl = llama_get_device_memory_data(
path_model, &mparams_copy, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
LLAMA_LOG_DEBUG("%s: memory for test allocation by device:\n", func_name);
for (size_t id = 0; id < nd; id++) {
const ngl_t & n = ngl_per_device[id];
LLAMA_LOG_DEBUG(
"%s: id=%zu, n_layer=%2" PRIu32 ", n_part=%2" PRIu32 ", overflow_type=%d, mem=%6" PRId64 " MiB\n",
func_name, id, n.n_layer, n.n_part, int(n.overflow_type), dmd_nl[id].mb.total()/MiB);
}
std::vector<int64_t> ret;
ret.reserve(nd);
for (size_t id = 0; id < nd; id++) {
ret.push_back(dmd_nl[id].mb.total());
}
return ret;
};
int64_t global_surplus_cpu_moe = 0;
if (hp_nex > 0) {
const static std::string pattern_moe_all = "blk\\.\\d+\\.ffn_(up|down|gate_up|gate)_(ch|)exps"; // matches all MoE tensors
ggml_backend_buffer_type_t cpu_buft = ggml_backend_cpu_buffer_type();
tensor_buft_overrides[0] = {pattern_moe_all.c_str(), cpu_buft};
tensor_buft_overrides[1] = {nullptr, nullptr};
mparams->tensor_buft_overrides = tensor_buft_overrides;
LLAMA_LOG_DEBUG("%s: getting device memory data with all MoE tensors moved to system memory:\n", __func__);
const dmds_t dmds_cpu_moe = llama_get_device_memory_data(
path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
for (size_t id = 0; id < nd; id++) {
global_surplus_cpu_moe += dmds_cpu_moe[id].free;
global_surplus_cpu_moe -= int64_t(dmds_cpu_moe[id].mb.total()) + margins[id];
}
if (global_surplus_cpu_moe > 0) {
LLAMA_LOG_INFO("%s: with only dense weights in device memory there is a total surplus of %" PRId64 " MiB\n",
__func__, global_surplus_cpu_moe/MiB);
} else {
LLAMA_LOG_INFO("%s: with only dense weights in device memory there is still a total deficit of %" PRId64 " MiB\n",
__func__, -global_surplus_cpu_moe/MiB);
}
// reset
tensor_buft_overrides[0] = {nullptr, nullptr};
mparams->tensor_buft_overrides = tensor_buft_overrides;
}
std::vector<int64_t> targets; // maximum acceptable memory use per device
targets.reserve(nd);
for (size_t id = 0; id < nd; id++) {
targets.push_back(dmds_full[id].free - margins[id]);
LLAMA_LOG_DEBUG("%s: id=%zu, target=%" PRId64 " MiB\n", __func__, id, targets[id]/MiB);
}
std::vector<ggml_backend_buffer_type_t> overflow_bufts; // which bufts the first partial layer of a device overflows to:
overflow_bufts.reserve(nd);
for (size_t id = 0; id < nd; id++) {
overflow_bufts.push_back(ggml_backend_cpu_buffer_type());
}
std::vector<ngl_t> ngl_per_device(nd);
std::vector<int64_t> mem = get_memory_for_layers(__func__, ngl_per_device, overflow_bufts);
// optimize the number of layers per device using the method of false position:
// - ngl_per_device has 0 layers for each device, lower bound
// - try a "high" configuration where a device is given all unassigned layers
// - interpolate the memory use / layer between low and high linearly to get a guess where it meets our target
// - check memory use of our guess, replace either the low or high bound
// - once we only have a difference of a single layer, stop and return the lower bound that just barely still fits
// - the last device has the output layer, which cannot be a partial layer
if (hp_nex == 0) {
LLAMA_LOG_INFO("%s: filling dense layers back-to-front:\n", __func__);
} else {
LLAMA_LOG_INFO("%s: filling dense-only layers back-to-front:\n", __func__);
}
for (int id = nd - 1; id >= 0; id--) {
uint32_t n_unassigned = hp_ngl + 1;
for (size_t jd = id + 1; jd < nd; ++jd) {
assert(n_unassigned >= ngl_per_device[jd].n_layer);
n_unassigned -= ngl_per_device[jd].n_layer;
}
std::vector<ngl_t> ngl_per_device_high = ngl_per_device;
ngl_per_device_high[id].n_layer = n_unassigned;
if (hp_nex > 0) {
ngl_per_device_high[id].n_part = size_t(id) < nd - 1 ? ngl_per_device_high[id].n_layer : ngl_per_device_high[id].n_layer - 1;
}
if (ngl_per_device_high[id].n_layer > 0) {
std::vector<int64_t> mem_high = get_memory_for_layers(__func__, ngl_per_device_high, overflow_bufts);
if (mem_high[id] > targets[id]) {
assert(ngl_per_device_high[id].n_layer > ngl_per_device[id].n_layer);
uint32_t delta = ngl_per_device_high[id].n_layer - ngl_per_device[id].n_layer;
LLAMA_LOG_DEBUG("%s: start filling device %" PRIu32 ", delta=%" PRIu32 "\n", __func__, id, delta);
while (delta > 1) {
uint32_t step_size = int64_t(delta) * (targets[id] - mem[id]) / (mem_high[id] - mem[id]);
step_size = std::max(step_size, uint32_t(1));
step_size = std::min(step_size, delta - 1);
std::vector<ngl_t> ngl_per_device_test = ngl_per_device;
ngl_per_device_test[id].n_layer += step_size;
if (hp_nex) {
ngl_per_device_test[id].n_part += size_t(id) == nd - 1 && ngl_per_device_test[id].n_part == 0 ?
step_size - 1 : step_size; // the first layer is the output layer which must always be full
}
const std::vector<int64_t> mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts);
if (mem_test[id] <= targets[id]) {
ngl_per_device = ngl_per_device_test;
mem = mem_test;
LLAMA_LOG_DEBUG("%s: set ngl_per_device[%d].n_layer=%" PRIu32 "\n", __func__, id, ngl_per_device[id].n_layer);
} else {
ngl_per_device_high = ngl_per_device_test;
mem_high = mem_test;
LLAMA_LOG_DEBUG("%s: set ngl_per_device_high[%d].n_layer=%" PRIu32 "\n", __func__, id, ngl_per_device_high[id].n_layer);
}
delta = ngl_per_device_high[id].n_layer - ngl_per_device[id].n_layer;
}
} else {
assert(ngl_per_device_high[id].n_layer == n_unassigned);
ngl_per_device = ngl_per_device_high;
mem = mem_high;
LLAMA_LOG_DEBUG("%s: set ngl_per_device[%d].n_layer=%" PRIu32 "\n", __func__, id, ngl_per_device[id].n_layer);
}
}
const int64_t projected_margin = dmds_full[id].free - mem[id];
LLAMA_LOG_INFO(
"%s: - %s: %2" PRIu32 " layers, %6" PRId64 " MiB used, %6" PRId64 " MiB free\n",
__func__, dev_names[id].c_str(), ngl_per_device[id].n_layer, mem[id]/MiB, projected_margin/MiB);
}
if (hp_nex == 0 || global_surplus_cpu_moe <= 0) {
set_ngl_tensor_split_tbo(ngl_per_device, overflow_bufts, *mparams);
return;
}
// step 4: for a MoE model where all dense tensors fit,
// convert the dense-only layers in the back to full layers in the front until all devices are full
// essentially the same procedure as for the dense-only layers except front-to-back
// also, try fitting at least part of one more layer to reduce waste for "small" GPUs with e.g. 24 GiB VRAM
size_t id_dense_start = nd;
for (int id = nd - 1; id >= 0; id--) {
if (ngl_per_device[id].n_layer > 0) {
id_dense_start = id;
continue;
}
break;
}
assert(id_dense_start < nd);
LLAMA_LOG_INFO("%s: converting dense-only layers to full layers and filling them front-to-back with overflow to next device/system memory:\n", __func__);
for (size_t id = 0; id <= id_dense_start && id_dense_start < nd; id++) {
std::vector<ngl_t> ngl_per_device_high = ngl_per_device;
for (size_t jd = id_dense_start; jd < nd; jd++) {
const uint32_t n_layer_move = jd < nd - 1 ? ngl_per_device_high[jd].n_layer : ngl_per_device_high[jd].n_layer - 1;
ngl_per_device_high[id].n_layer += n_layer_move;
ngl_per_device_high[jd].n_layer -= n_layer_move;
ngl_per_device_high[jd].n_part = 0;
}
size_t id_dense_start_high = nd - 1;
std::vector<int64_t> mem_high = get_memory_for_layers(__func__, ngl_per_device_high, overflow_bufts);
if (mem_high[id] > targets[id]) {
assert(ngl_per_device_high[id].n_full() >= ngl_per_device[id].n_full());
uint32_t delta = ngl_per_device_high[id].n_full() - ngl_per_device[id].n_full();
while (delta > 1) {
uint32_t step_size = int64_t(delta) * (targets[id] - mem[id]) / (mem_high[id] - mem[id]);
step_size = std::max(step_size, uint32_t(1));
step_size = std::min(step_size, delta - 1);
std::vector<ngl_t> ngl_per_device_test = ngl_per_device;
size_t id_dense_start_test = id_dense_start;
uint32_t n_converted_test = 0;
for (;id_dense_start_test < nd; id_dense_start_test++) {
const uint32_t n_convert_jd = std::min(step_size - n_converted_test, ngl_per_device_test[id_dense_start_test].n_part);
ngl_per_device_test[id_dense_start_test].n_layer -= n_convert_jd;
ngl_per_device_test[id_dense_start_test].n_part -= n_convert_jd;
ngl_per_device_test[id].n_layer += n_convert_jd;
n_converted_test += n_convert_jd;
if (ngl_per_device_test[id_dense_start_test].n_part > 0) {
break;
}
}
const std::vector<int64_t> mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts);
if (mem_test[id] <= targets[id]) {
ngl_per_device = ngl_per_device_test;
mem = mem_test;
id_dense_start = id_dense_start_test;
LLAMA_LOG_DEBUG("%s: set ngl_per_device[%zu].(n_layer, n_part)=(%" PRIu32 ", %" PRIu32 "), id_dense_start=%zu\n",
__func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start);
} else {
ngl_per_device_high = ngl_per_device_test;
mem_high = mem_test;
id_dense_start_high = id_dense_start_test;
LLAMA_LOG_DEBUG("%s: set ngl_per_device_high[%zu].(n_layer, n_part)=(%" PRIu32 ", %" PRIu32 "), id_dense_start_high=%zu\n",
__func__, id, ngl_per_device_high[id].n_layer, ngl_per_device_high[id].n_part, id_dense_start_high);
}
assert(ngl_per_device_high[id].n_full() >= ngl_per_device[id].n_full());
delta = ngl_per_device_high[id].n_full() - ngl_per_device[id].n_full();
}
} else {
ngl_per_device = ngl_per_device_high;
mem = mem_high;
id_dense_start = id_dense_start_high;
LLAMA_LOG_DEBUG("%s: set ngl_per_device[%zu].(n_layer, n_part)=(%" PRIu32 ", %" PRIu32 "), id_dense_start=%zu\n",
__func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start);
}
// try to fit at least part of one more layer
if (ngl_per_device[id_dense_start].n_layer > (id < nd - 1 ? 0 : 1)) {
std::vector<ngl_t> ngl_per_device_test = ngl_per_device;
size_t id_dense_start_test = id_dense_start;
ngl_per_device_test[id_dense_start_test].n_layer--;
ngl_per_device_test[id_dense_start_test].n_part--;
ngl_per_device_test[id].n_layer++;
ngl_per_device_test[id].n_part++;
if (ngl_per_device_test[id_dense_start_test].n_part == 0) {
id_dense_start_test++;
}
ngl_per_device_test[id].overflow_type = LAYER_FRACTION_UP;
std::vector<ggml_backend_buffer_type_t> overflow_bufts_test = overflow_bufts;
if (id < nd - 1) {
overflow_bufts_test[id] = ggml_backend_dev_buffer_type(devs[id + 1].dev);
}
LLAMA_LOG_DEBUG("%s: trying to fit one extra layer with overflow_type=LAYER_FRACTION_UP\n", __func__);
std::vector<int64_t> mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts_test);
if (mem_test[id] < targets[id] && (id + 1 == nd || mem_test[id + 1] < targets[id + 1])) {
ngl_per_device = ngl_per_device_test;
overflow_bufts = overflow_bufts_test;
mem = mem_test;
id_dense_start = id_dense_start_test;
LLAMA_LOG_DEBUG("%s: set ngl_per_device[%zu].(n_layer, n_part, overflow_type)=(%" PRIu32 ", %" PRIu32 ", UP), id_dense_start=%zu\n",
__func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start);
ngl_per_device_test[id].overflow_type = LAYER_FRACTION_GATE;
LLAMA_LOG_DEBUG("%s: trying to fit one extra layer with overflow_type=LAYER_FRACTION_GATE\n", __func__);
mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts_test);
if (mem_test[id] < targets[id] && (id + 1 == nd || mem_test[id + 1] < targets[id + 1])) {
ngl_per_device = ngl_per_device_test;
overflow_bufts = overflow_bufts_test;
mem = mem_test;
id_dense_start = id_dense_start_test;
LLAMA_LOG_DEBUG("%s: set ngl_per_device[%zu].(n_layer, n_part, overflow_type)=(%" PRIu32 ", %" PRIu32 ", GATE), id_dense_start=%zu\n",
__func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start);
}
} else {
ngl_per_device_test[id].overflow_type = LAYER_FRACTION_ATTN;
LLAMA_LOG_DEBUG("%s: trying to fit one extra layer with overflow_type=LAYER_FRACTION_ATTN\n", __func__);
mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts_test);
if (mem_test[id] < targets[id] && (id + 1 == nd || mem_test[id + 1] < targets[id + 1])) {
ngl_per_device = ngl_per_device_test;
overflow_bufts = overflow_bufts_test;
mem = mem_test;
id_dense_start = id_dense_start_test;
LLAMA_LOG_DEBUG("%s: set ngl_per_device[%zu].(n_layer, n_part, overflow_type)=(%" PRIu32 ", %" PRIu32 ", ATTN), id_dense_start=%zu\n",
__func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start);
}
}
}
const int64_t projected_margin = dmds_full[id].free - mem[id];
LLAMA_LOG_INFO(
"%s: - %s: %2" PRIu32 " layers (%2" PRIu32 " overflowing), %6" PRId64 " MiB used, %6" PRId64 " MiB free\n",
__func__, dev_names[id].c_str(), ngl_per_device[id].n_layer, ngl_per_device[id].n_part, mem[id]/MiB, projected_margin/MiB);
}
// print info for devices that were not changed during the conversion from dense only to full layers:
for (size_t id = id_dense_start + 1; id < nd; id++) {
const int64_t projected_margin = dmds_full[id].free - mem[id];
LLAMA_LOG_INFO(
"%s: - %s: %2" PRIu32 " layers (%2" PRIu32 " overflowing), %6" PRId64 " MiB used, %6" PRId64 " MiB free\n",
__func__, dev_names[id].c_str(), ngl_per_device[id].n_layer, ngl_per_device[id].n_part, mem[id]/MiB, projected_margin/MiB);
}
set_ngl_tensor_split_tbo(ngl_per_device, overflow_bufts, *mparams);
}
enum llama_params_fit_status llama_params_fit(
const char * path_model, struct llama_model_params * mparams, struct llama_context_params * cparams,
float * tensor_split, struct llama_model_tensor_buft_override * tensor_buft_overrides,
size_t * margins, uint32_t n_ctx_min, enum ggml_log_level log_level) {
const int64_t t0_us = llama_time_us();
llama_params_fit_status status = LLAMA_PARAMS_FIT_STATUS_SUCCESS;
try {
llama_params_fit_impl(path_model, mparams, cparams, tensor_split, tensor_buft_overrides, margins, n_ctx_min, log_level);
LLAMA_LOG_INFO("%s: successfully fit params to free device memory\n", __func__);
} catch (const llama_params_fit_exception & e) {
LLAMA_LOG_WARN("%s: failed to fit params to free device memory: %s\n", __func__, e.what());
status = LLAMA_PARAMS_FIT_STATUS_FAILURE;
} catch (const std::runtime_error & e) {
LLAMA_LOG_ERROR("%s: encountered an error while trying to fit params to free device memory: %s\n", __func__, e.what());
status = LLAMA_PARAMS_FIT_STATUS_ERROR;
}
const int64_t t1_us = llama_time_us();
LLAMA_LOG_INFO("%s: fitting params to free memory took %.2f seconds\n", __func__, (t1_us - t0_us) * 1e-6);
return status;
}
struct llama_sampler_chain_params llama_sampler_chain_default_params() {
struct llama_sampler_chain_params result = {
/*.no_perf =*/ true,

3
tools/cli/cli.cpp
View file

@ -2,6 +2,7 @@
#include "common.h"
#include "arg.h"
#include "console.h"
#include "fit.h"
// #include "log.h"
#include "server-common.h"
@ -647,7 +648,7 @@ int main(int argc, char ** argv) {
// bump the log level to display timings
common_log_set_verbosity_thold(LOG_LEVEL_INFO);
llama_memory_breakdown_print(ctx_cli.ctx_server.get_llama_context());
common_memory_breakdown_print(ctx_cli.ctx_server.get_llama_context());
return 0;
}

73
tools/fit-params/fit-params.cpp
View file

@ -1,14 +1,12 @@
#include "llama.h"
#include "../src/llama-ext.h"
#include "arg.h"
#include "common.h"
#include "fit.h"
#include "log.h"
#include <chrono>
#include <cinttypes>
#include <thread>
using namespace std::chrono_literals;
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
@ -19,49 +17,58 @@ int main(int argc, char ** argv) {
common_init();
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_FIT_PARAMS)) {
return 1;
}
llama_backend_init();
llama_numa_init(params.numa);
auto mparams = common_model_params_to_llama(params);
auto cparams = common_context_params_to_llama(params);
const llama_params_fit_status status = llama_params_fit(params.model.path.c_str(), &mparams, &cparams,
params.tensor_split, params.tensor_buft_overrides.data(), params.fit_params_target.data(), params.fit_params_min_ctx,
params.verbosity >= 4 ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_ERROR);
if (status != LLAMA_PARAMS_FIT_STATUS_SUCCESS) {
LOG_ERR("%s: failed to fit CLI arguments to free memory, exiting...\n", __func__);
exit(1);
}
LOG_INF("%s: printing fitted CLI arguments to stdout...\n", __func__);
common_log_flush(common_log_main());
printf("-c %" PRIu32 " -ngl %" PRIi32, cparams.n_ctx, mparams.n_gpu_layers);
if (!params.fit_params_print) {
const common_params_fit_status status = common_fit_params(params.model.path.c_str(), &mparams, &cparams,
params.tensor_split, params.tensor_buft_overrides.data(), params.fit_params_target.data(), params.fit_params_min_ctx,
params.verbosity >= 4 ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_ERROR);
if (status != COMMON_PARAMS_FIT_STATUS_SUCCESS) {
LOG_ERR("%s: failed to fit CLI arguments to free memory, exiting...\n", __func__);
exit(1);
}
size_t nd = llama_max_devices();
while (nd > 1 && mparams.tensor_split[nd - 1] == 0.0f) {
nd--;
}
if (nd > 1) {
for (size_t id = 0; id < nd; id++) {
if (id == 0) {
printf(" -ts ");
LOG_INF("%s: printing fitted CLI arguments to stdout...\n", __func__);
common_log_flush(common_log_main());
printf("-c %" PRIu32 " -ngl %" PRIi32, cparams.n_ctx, mparams.n_gpu_layers);
size_t nd = llama_max_devices();
while (nd > 1 && mparams.tensor_split[nd - 1] == 0.0f) {
nd--;
}
if (nd > 1) {
for (size_t id = 0; id < nd; id++) {
if (id == 0) {
printf(" -ts ");
}
printf("%s%" PRIu32, id > 0 ? "," : "", uint32_t(mparams.tensor_split[id]));
}
printf("%s%" PRIu32, id > 0 ? "," : "", uint32_t(mparams.tensor_split[id]));
}
}
const size_t ntbo = llama_max_tensor_buft_overrides();
bool any_tbo = false;
for (size_t itbo = 0; itbo < ntbo && mparams.tensor_buft_overrides[itbo].pattern != nullptr; itbo++) {
if (itbo == 0) {
printf(" -ot \"");
const size_t ntbo = llama_max_tensor_buft_overrides();
bool any_tbo = false;
for (size_t itbo = 0; itbo < ntbo && mparams.tensor_buft_overrides[itbo].pattern != nullptr; itbo++) {
if (itbo == 0) {
printf(" -ot \"");
}
printf("%s%s=%s", itbo > 0 ? "," : "", mparams.tensor_buft_overrides[itbo].pattern, ggml_backend_buft_name(mparams.tensor_buft_overrides[itbo].buft));
any_tbo = true;
}
printf("%s%s=%s", itbo > 0 ? "," : "", mparams.tensor_buft_overrides[itbo].pattern, ggml_backend_buft_name(mparams.tensor_buft_overrides[itbo].buft));
any_tbo = true;
printf("%s\n", any_tbo ? "\"" : "");
} else {
LOG_INF("%s: printing estimated memory in MiB to stdout (device, model, context, compute) ...\n", __func__);
common_log_flush(common_log_main());
common_fit_print(params.model.path.c_str(), &mparams, &cparams);
}
printf("%s\n", any_tbo ? "\"" : "");
return 0;
}

5
tools/llama-bench/llama-bench.cpp
View file

@ -22,6 +22,7 @@
#include "build-info.h"
#include "common.h"
#include "download.h"
#include "fit.h"
#include "ggml.h"
#include "llama.h"
@ -2225,7 +2226,7 @@ int main(int argc, char ** argv) {
prev_inst = nullptr;
}
// use default n_gpu_layers and n_ctx so llama_params_fit can adjust them
// use default n_gpu_layers and n_ctx so common_fit_params can adjust them
mparams.n_gpu_layers = llama_model_default_params().n_gpu_layers;
mparams.tensor_split = fit_tensor_split.data();
mparams.tensor_buft_overrides = fit_overrides.data();
@ -2236,7 +2237,7 @@ int main(int argc, char ** argv) {
uint32_t n_ctx_needed = inst.n_prompt + inst.n_gen + inst.n_depth;
cparams.n_ctx = std::max(cparams.n_ctx, n_ctx_needed);
llama_params_fit(inst.model.c_str(), &mparams, &cparams,
common_fit_params(inst.model.c_str(), &mparams, &cparams,
fit_tensor_split.data(),
fit_overrides.data(),
margins.data(),

3
tools/perplexity/perplexity.cpp
View file

@ -1,5 +1,6 @@
#include "arg.h"
#include "common.h"
#include "fit.h"
#include "log.h"
#include "llama.h"
@ -2087,7 +2088,7 @@ int main(int argc, char ** argv) {
LOG("\n");
llama_perf_context_print(ctx);
llama_memory_breakdown_print(ctx);
common_memory_breakdown_print(ctx);
llama_backend_free();

3
tools/server/server.cpp
View file

@ -7,6 +7,7 @@
#include "arg.h"
#include "build-info.h"
#include "common.h"
#include "fit.h"
#include "llama.h"
#include "log.h"
@ -344,7 +345,7 @@ int main(int argc, char ** argv) {
auto * ll_ctx = ctx_server.get_llama_context();
if (ll_ctx != nullptr) {
llama_memory_breakdown_print(ll_ctx);
common_memory_breakdown_print(ll_ctx);
}
}