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GGML update to ec98e2002 (#13451)

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* Revert "add support for NVIDIA Nemotron 3 Nano"

This reverts commit e7d2ae9d69421012e9a8765c06a3fdf0e45b12f3.

* GGML update to 380b4c984

Remove MaskBatchPadding as GGML_KQ_MASK_PAD is no longer present (no
padding required)

* update to c45f89d55

* ec98e2002

solar pro needed more adjusting - needs verification

* review comments
This commit is contained in:
Daniel Hiltgen
2025-12-17 13:13:55 -08:00
committed by GitHub
parent 1c094038bc
commit 49a9c9ba6a
127 changed files with 8128 additions and 6710 deletions
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197
llama/llama.cpp/common/common.cpp vendored
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@@ -1013,31 +1013,40 @@ bool tty_can_use_colors() {
// Model utils
//
static inline void common_init_sampler_from_model(
// TODO: move to common/sampling
static void common_init_sampler_from_model(
const llama_model * model,
common_params_sampling & sparams) {
const uint64_t config = sparams.user_sampling_config;
auto get_int32 = [&](const char * key, int32_t & dst, uint64_t user_config) {
if (config & user_config) return;
if (config & user_config) {
return;
}
char buf[64] = {0};
if (llama_model_meta_val_str(model, key, buf, sizeof(buf)) > 0) {
char * end = nullptr;
int32_t v = strtol(buf, &end, 10);
if (end && end != buf) dst = v;
if (end && end != buf) {
dst = v;
}
}
};
auto get_float = [&](const char * key, float & dst, uint64_t user_config) {
if (config & user_config) return;
if (config & user_config) {
return;
}
char buf[128] = {0};
if (llama_model_meta_val_str(model, key, buf, sizeof(buf)) > 0) {
char * end = nullptr;
float v = strtof(buf, &end);
if (end && end != buf) dst = v;
if (end && end != buf) {
dst = v;
}
}
};
@@ -1065,31 +1074,125 @@ static inline void common_init_sampler_from_model(
get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_ETA), sparams.mirostat_eta, common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_ETA);
}
struct common_init_result common_init_from_params(common_params & params) {
common_init_result iparams;
struct common_init_result::impl {
impl() = default;
~impl() = default;
llama_model_ptr model;
llama_context_ptr context;
std::vector<llama_adapter_lora_ptr> lora;
std::vector<common_sampler_ptr> samplers;
};
common_init_result::common_init_result(common_params & params) :
pimpl(new impl{}) {
auto mparams = common_model_params_to_llama(params);
auto cparams = common_context_params_to_llama(params);
if (params.fit_params) {
LOG_INF("%s: fitting params to device memory, to report bugs during this step use -fit off (or --verbose if you can't)\n", __func__);
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);
}
llama_model * model = llama_model_load_from_file(params.model.path.c_str(), mparams);
if (model == NULL) {
LOG_ERR("%s: failed to load model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n",
__func__, params.model.path.c_str());
return iparams;
return;
}
common_init_sampler_from_model(model, params.sampling);
pimpl->model.reset(model);
const llama_vocab * vocab = llama_model_get_vocab(model);
auto cparams = common_context_params_to_llama(params);
// updates params.sampling
// TODO: fix naming
common_init_sampler_from_model(model, params.sampling);
if (params.sampling.ignore_eos && llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) {
LOG_WRN("%s: warning: vocab does not have an EOS token, ignoring --ignore-eos\n", __func__);
params.sampling.ignore_eos = false;
}
// initialize once
for (llama_token i = 0; i < llama_vocab_n_tokens(vocab); i++) {
if (llama_vocab_is_eog(vocab, i)) {
LOG_INF("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(vocab, i).c_str(), -INFINITY);
params.sampling.logit_bias_eog.push_back({i, -INFINITY});
}
}
if (params.sampling.ignore_eos) {
// add EOG biases to the active set of logit biases
params.sampling.logit_bias.insert(
params.sampling.logit_bias.end(),
params.sampling.logit_bias_eog.begin(), params.sampling.logit_bias_eog.end());
}
//if (params.sampling.penalty_last_n == -1) {
// LOG_INF("%s: setting penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
// params.sampling.penalty_last_n = llama_n_ctx(lctx);
//}
//if (params.sampling.dry_penalty_last_n == -1) {
// LOG_INF("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
// params.sampling.dry_penalty_last_n = llama_n_ctx(lctx);
//}
pimpl->samplers.resize(cparams.n_seq_max);
for (int i = 0; i < (int) cparams.n_seq_max; ++i) {
pimpl->samplers[i].reset(common_sampler_init(model, params.sampling));
}
llama_context * lctx = llama_init_from_model(model, cparams);
if (lctx == NULL) {
LOG_ERR("%s: failed to create context with model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n",
__func__, params.model.path.c_str());
llama_model_free(model);
return iparams;
LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.path.c_str());
return;
}
pimpl->context.reset(lctx);
}
llama_model * common_init_result::model() {
return pimpl->model.get();
}
llama_context * common_init_result::context() {
return pimpl->context.get();
}
common_sampler * common_init_result::sampler(llama_seq_id seq_id) {
return pimpl->samplers[seq_id].get();
}
std::vector<llama_adapter_lora_ptr> & common_init_result::lora() {
return pimpl->lora;
}
void common_init_result::free_context() {
pimpl->context.reset();
}
common_init_result_ptr common_init_from_params(common_params & params) {
common_init_result_ptr res(new common_init_result(params));
llama_model * model = res->model();
if (model == NULL) {
LOG_ERR("%s: failed to load model '%s'\n", __func__, params.model.path.c_str());
return res;
}
llama_context * lctx = res->context();
if (lctx == NULL) {
LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.path.c_str());
return res;
}
const llama_vocab * vocab = llama_model_get_vocab(model);
if (params.ctx_shift && !llama_memory_can_shift(llama_get_memory(lctx))) {
LOG_WRN("%s: KV cache shifting is not supported for this context, disabling KV cache shifting\n", __func__);
params.ctx_shift = false;
@@ -1101,10 +1204,7 @@ struct common_init_result common_init_from_params(common_params & params) {
const auto cvec = common_control_vector_load(params.control_vectors);
if (cvec.n_embd == -1) {
llama_free(lctx);
llama_model_free(model);
return iparams;
return res;
}
int err = llama_apply_adapter_cvec(
@@ -1115,10 +1215,7 @@ struct common_init_result common_init_from_params(common_params & params) {
params.control_vector_layer_start,
params.control_vector_layer_end);
if (err) {
llama_free(lctx);
llama_model_free(model);
return iparams;
return res;
}
}
@@ -1142,10 +1239,7 @@ struct common_init_result common_init_from_params(common_params & params) {
}
if (!ok) {
llama_free(lctx);
llama_model_free(model);
return iparams;
return res;
}
}
@@ -1155,9 +1249,7 @@ struct common_init_result common_init_from_params(common_params & params) {
lora.reset(llama_adapter_lora_init(model, la.path.c_str()));
if (lora == nullptr) {
LOG_ERR("%s: failed to apply lora adapter '%s'\n", __func__, la.path.c_str());
llama_free(lctx);
llama_model_free(model);
return iparams;
return res;
}
char buf[1024];
@@ -1166,43 +1258,13 @@ struct common_init_result common_init_from_params(common_params & params) {
la.task_name = buf;
llama_adapter_meta_val_str(la.ptr, "adapter.lora.prompt_prefix", buf, sizeof(buf));
la.prompt_prefix = buf;
iparams.lora.emplace_back(std::move(lora)); // copy to list of loaded adapters
res->lora().emplace_back(std::move(lora)); // copy to list of loaded adapters
}
if (!params.lora_init_without_apply) {
common_set_adapter_lora(lctx, params.lora_adapters);
}
if (params.sampling.ignore_eos && llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) {
LOG_WRN("%s: warning: vocab does not have an EOS token, ignoring --ignore-eos\n", __func__);
params.sampling.ignore_eos = false;
}
// initialize once
for (llama_token i = 0; i < llama_vocab_n_tokens(vocab); i++) {
if (llama_vocab_is_eog(vocab, i)) {
LOG_INF("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(lctx, i).c_str(), -INFINITY);
params.sampling.logit_bias_eog.push_back({i, -INFINITY});
}
}
if (params.sampling.ignore_eos) {
// add EOG biases to the active set of logit biases
params.sampling.logit_bias.insert(
params.sampling.logit_bias.end(),
params.sampling.logit_bias_eog.begin(), params.sampling.logit_bias_eog.end());
}
if (params.sampling.penalty_last_n == -1) {
LOG_INF("%s: setting penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
params.sampling.penalty_last_n = llama_n_ctx(lctx);
}
if (params.sampling.dry_penalty_last_n == -1) {
LOG_INF("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
params.sampling.dry_penalty_last_n = llama_n_ctx(lctx);
}
if (params.warmup) {
LOG_WRN("%s: warming up the model with an empty run - please wait ... (--no-warmup to disable)\n", __func__);
@@ -1241,12 +1303,11 @@ struct common_init_result common_init_from_params(common_params & params) {
llama_set_warmup(lctx, false);
}
iparams.model.reset(model);
iparams.context.reset(lctx);
return iparams;
return res;
}
common_init_result::~common_init_result() = default;
std::string get_model_endpoint() {
const char * model_endpoint_env = getenv("MODEL_ENDPOINT");
// We still respect the use of environment-variable "HF_ENDPOINT" for backward-compatibility.
@@ -1255,7 +1316,9 @@ std::string get_model_endpoint() {
std::string model_endpoint = "https://huggingface.co/";
if (endpoint_env) {
model_endpoint = endpoint_env;
if (model_endpoint.back() != '/') model_endpoint += '/';
if (model_endpoint.back() != '/') {
model_endpoint += '/';
}
}
return model_endpoint;
}