vrr/koboldcpp
2
0
Fork 0
mirror of https://github.com/LostRuins/koboldcpp.git synced 2026-05-09 19:46:11 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

Merge branch 'upstream' into concedo_experimental

Browse source 
# Conflicts:
#	.devops/vulkan.Dockerfile
#	.github/workflows/build.yml
#	ci/run.sh
#	examples/model-conversion/Makefile
#	examples/model-conversion/README.md
#	examples/model-conversion/scripts/causal/compare-logits.py
#	examples/model-conversion/scripts/embedding/run-converted-model.sh
#	examples/model-conversion/scripts/utils/common.py
#	examples/model-conversion/scripts/utils/semantic_check.py
#	ggml/src/ggml-cann/ggml-cann.cpp
#	ggml/src/ggml-cuda/CMakeLists.txt
#	ggml/src/ggml-opencl/CMakeLists.txt
#	ggml/src/ggml-opencl/ggml-opencl.cpp
#	ggml/src/ggml-sycl/ggml-sycl.cpp
#	ggml/src/ggml-webgpu/ggml-webgpu.cpp
#	scripts/pr2wt.sh
#	scripts/sync_vendor.py
#	tests/test-arg-parser.cpp
This commit is contained in:
Concedo 2026-01-09 01:23:10 +08:00
commit 983baac46b
28 changed files with 2516 additions and 757 deletions
Download patch file Download diff file Expand all files Collapse all files

76
src/llama.cpp
View file

@ -171,9 +171,8 @@ class llama_params_fit_exception : public std::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 margin_s, uint32_t n_ctx_min, enum ggml_log_level log_level) {
size_t * margins_s, uint32_t n_ctx_min, enum ggml_log_level log_level) {
constexpr int64_t MiB = 1024*1024;
const int64_t margin = margin_s; // this function uses int64_t rather than size_t for memory sizes to more conveniently handle deficits
typedef std::vector<llama_device_memory_data> dmds_t;
const llama_model_params default_mparams = llama_model_default_params();
@ -192,6 +191,12 @@ static void llama_params_fit_impl(
return;
}
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);
for (size_t id = 0; id < nd; id++) {
margins.push_back(margins_s[id]);
}
std::vector<std::string> dev_names;
{
dev_names.reserve(nd);
@ -211,9 +216,10 @@ static void llama_params_fit_impl(
int64_t sum_free = 0;
int64_t sum_projected_free = 0;
int64_t min_projected_free = INT64_MAX;
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 > 1) {
LLAMA_LOG_INFO("%s: projected memory use with initial parameters [MiB]:\n", __func__);
@ -223,45 +229,63 @@ static void llama_params_fit_impl(
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;
min_projected_free = std::min(min_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 " %s\n",
__func__, dev_names[id].c_str(), dmd.total/MiB, projected_used/MiB, std::abs(projected_free)/MiB,
projected_free >= 0 ? "surplus" : "deficit");
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 (min_projected_free >= margin) {
if (nd == 1) {
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__, min_projected_free/MiB, margin/MiB);
__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;
}
LLAMA_LOG_INFO("%s: will leave at least %" PRId64 " >= %" PRId64 " MiB of free memory on all devices, no changes needed\n",
__func__, min_projected_free/MiB, margin/MiB);
return;
}
// step 2: try reducing memory use by reducing the context size
{
int64_t global_surplus = sum_projected_free - int64_t(nd)*margin;
int64_t global_surplus = sum_projected_free;
for (size_t id = 0; id < nd; id++) {
global_surplus -= margins[id];
}
if (global_surplus < 0) {
LLAMA_LOG_INFO(nd == 1 ?
"%s: cannot fulfill margin of %" PRId64 " MiB, need to reduce device memory by %" PRId64 " MiB\n" :
"%s: cannot fulfill margin of %" PRId64 " MiB on all devices, need to use %" PRId64 " MiB less in total\n",
__func__, margin/MiB, -global_surplus/MiB);
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 - nd*margin_s;
int64_t sum_used_target = sum_free;
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
@ -472,9 +496,9 @@ static void llama_params_fit_impl(
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 (const llama_device_memory_data & dmd : dmds_cpu_moe) {
global_surplus_cpu_moe += dmd.free;
global_surplus_cpu_moe -= int64_t(dmd.mb.total()) + margin;
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) {
@ -493,7 +517,7 @@ static void llama_params_fit_impl(
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 - margin);
targets.push_back(dmds_full[id].free - margins[id]);
LLAMA_LOG_DEBUG("%s: id=%zu, target=%" PRId64 " MiB\n", __func__, id, targets[id]/MiB);
}
@ -725,11 +749,11 @@ static void llama_params_fit_impl(
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 margin_s, uint32_t n_ctx_min, enum ggml_log_level log_level) {
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, margin_s, n_ctx_min, log_level);
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());
@ -822,7 +846,7 @@ static int llama_model_load(const std::string & fname, std::vector<std::string>
model.t_start_us = tm.t_start_us;
try {
llama_model_loader ml(fname, splits, params.use_mmap, params.check_tensors, params.no_alloc, params.kv_overrides, params.tensor_buft_overrides);
llama_model_loader ml(fname, splits, params.use_mmap, params.use_direct_io, params.check_tensors, params.no_alloc, params.kv_overrides, params.tensor_buft_overrides);
ml.print_info();