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llama.cpp
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Merge branch 'ggml-org:master' into power-law-sampler

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ddh0 2025-12-16 20:44:52 -06:00 committed by GitHub
parent 58aa1c6f5a 2973a65ecb
commit 27dda80dd7
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3 changed files with 66 additions and 39 deletions
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2
gguf-py/gguf/utility.py
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@ -288,7 +288,7 @@ class LocalTensor:
data_range: LocalTensorRange
def mmap_bytes(self) -> np.ndarray:
return np.memmap(self.data_range.filename, offset=self.data_range.offset, shape=self.data_range.size)
return np.memmap(self.data_range.filename, mode='r', offset=self.data_range.offset, shape=self.data_range.size)
class SafetensorsLocal:

67
src/llama.cpp
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@ -71,8 +71,9 @@ static std::vector<llama_device_memory_data> llama_get_device_memory_data(
}, &ud);
llama_model_params mparams_copy = *mparams;
mparams_copy.no_alloc = true;
mparams_copy.use_mmap = false;
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) {
@ -180,11 +181,12 @@ static void llama_params_fit_impl(
}
}
int64_t sum_total = 0;
int64_t sum_projected_free = 0;
int64_t min_projected_free = INT64_MAX;
int64_t sum_projected_used = 0;
int64_t sum_projected_ctx = 0;
int64_t sum_total = 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;
int64_t sum_projected_ctx = 0;
if (nd > 1) {
LLAMA_LOG_INFO("%s: projected memory use with initial parameters [MiB]:\n", __func__);
@ -195,11 +197,12 @@ static void llama_params_fit_impl(
const int64_t projected_used = dmd.mb.total();
const int64_t projected_free = dmd.free - projected_used;
sum_total += dmd.total;
sum_projected_used += projected_used;
sum_projected_free += projected_free;
min_projected_free = std::min(min_projected_free, projected_free);
sum_projected_ctx += dmd.mb.context;
sum_total += dmd.total;
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;
sum_projected_ctx += dmd.mb.context;
if (nd > 1) {
LLAMA_LOG_INFO("%s: - %s: %6" PRId64 " total, %6" PRId64 " used, %6" PRId64 " %s\n",
@ -234,10 +237,24 @@ static void llama_params_fit_impl(
if (cparams->n_ctx == 0) {
if (hp_nct > n_ctx_min) {
const int64_t bytes_per_ctx = sum_projected_ctx / hp_nct;
const uint32_t ctx_reduction = std::min(
uint32_t((-global_surplus + bytes_per_ctx - 1) / bytes_per_ctx), hp_nct - n_ctx_min);
int64_t memory_reduction = -global_surplus;
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);
memory_reduction += (nd + 1) * model_per_layer / (hp_nex == 0 ? 2 : 6);
}
uint32_t ctx_reduction = std::min(uint32_t((memory_reduction + bytes_per_ctx - 1) / bytes_per_ctx), hp_nct - n_ctx_min);
cparams->n_ctx = hp_nct - ctx_reduction;
const int64_t memory_reduction = ctx_reduction * bytes_per_ctx;
cparams->n_ctx = std::max(cparams->n_ctx - cparams->n_ctx % 256, n_ctx_min); // round down context for CUDA backend
ctx_reduction = hp_nct - cparams->n_ctx;
memory_reduction = ctx_reduction * bytes_per_ctx;
global_surplus += memory_reduction;
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);
@ -481,8 +498,13 @@ static void llama_params_fit_impl(
} else {
LLAMA_LOG_INFO("%s: filling dense-only layers back-to-front:\n", __func__);
}
uint32_t n_unassigned = hp_ngl;
for (int id = nd - 1; id >= 0; id--) {
uint32_t n_unassigned = hp_ngl;
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) {
@ -491,7 +513,9 @@ static void llama_params_fit_impl(
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, partial_moe);
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));
@ -505,20 +529,19 @@ static void llama_params_fit_impl(
const std::vector<int64_t> mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts, partial_moe);
if (mem_test[id] <= targets[id]) {
ngl_per_device = ngl_per_device_test;
mem = mem_test;
n_unassigned -= ngl_per_device[id].n_layer;
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[id].n_layer);
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 {
ngl_per_device = ngl_per_device_high;
n_unassigned -= ngl_per_device[id].n_layer;
assert(ngl_per_device_high[id].n_layer == n_unassigned);
ngl_per_device = ngl_per_device_high;
LLAMA_LOG_DEBUG("%s: set ngl_per_device[%d].n_layer=%" PRIu32 "\n", __func__, id, ngl_per_device[id].n_layer);
}
}

36
tools/fit-params/fit-params.cpp
View File

@ -4,7 +4,11 @@
#include "common.h"
#include "log.h"
#include <iostream>
#include <chrono>
#include <cinttypes>
#include <thread>
using namespace std::chrono_literals;
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
@ -22,13 +26,17 @@ int main(int argc, char ** argv) {
llama_numa_init(params.numa);
auto mparams = common_model_params_to_llama(params);
auto cparams = common_context_params_to_llama(params);
llama_params_fit(params.model.path.c_str(), &mparams, &cparams,
const bool success = llama_params_fit(params.model.path.c_str(), &mparams, &cparams,
params.tensor_split, params.tensor_buft_overrides.data(), params.fit_params_target, params.fit_params_min_ctx,
params.verbosity >= 4 ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_ERROR);
if (!success) {
LOG_ERR("%s: failed to fit CLI arguments to free memory, exiting...\n", __func__);
exit(1);
}
LOG_INF("Printing fitted CLI arguments to stdout...\n");
std::cout << "-c " << cparams.n_ctx;
std::cout << " -ngl " << mparams.n_gpu_layers;
LOG_INF("%s: printing fitted CLI arguments to stdout...\n", __func__);
std::this_thread::sleep_for(10ms); // to avoid a race between stderr and stdout
printf("-c %" PRIu32 " -ngl %" PRIu32, cparams.n_ctx, mparams.n_gpu_layers);
size_t nd = llama_max_devices();
while (nd > 1 && mparams.tensor_split[nd - 1] == 0.0f) {
@ -37,26 +45,22 @@ int main(int argc, char ** argv) {
if (nd > 1) {
for (size_t id = 0; id < nd; id++) {
if (id == 0) {
std::cout << " -ts ";
printf(" -ts ");
}
if (id > 0) {
std::cout << ",";
}
std::cout << 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) {
std::cout << " -ot ";
printf(" -ot \"");
}
if (itbo > 0) {
std::cout << ",";
}
std::cout << mparams.tensor_buft_overrides[itbo].pattern << "=" << ggml_backend_buft_name(mparams.tensor_buft_overrides[itbo].buft);
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;
}
std::cout << "\n";
printf("%s\n", any_tbo ? "\"" : "");
return 0;
}