mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-24 02:14:35 +00:00
llama : make model stateless and context stateful (llama_state) (#1797)
* llama : make model stateless and context stateful * llama : minor cleanup * llama : update internal API declaration * Apply suggestions from code review fix style Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Missing model memory release * Fix style * Add deprecated warning for public API function llama_init_from_file * Update public API use cases: move away from deprecated llama_init_from_file * Deprecate public API function llama_apply_lora_from_file --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Didzis Gosko
GitHub
parent
d7b7484f74
commit
527b6fba1d
@ -536,7 +536,7 @@ std::vector<llama_token> llama_tokenize(struct llama_context * ctx, const std::s
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return res;
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}
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struct llama_context * llama_init_from_gpt_params(const gpt_params & params) {
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std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_params(const gpt_params & params) {
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auto lparams = llama_context_default_params();
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lparams.n_ctx = params.n_ctx;
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@ -552,25 +552,33 @@ struct llama_context * llama_init_from_gpt_params(const gpt_params & params) {
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lparams.logits_all = params.perplexity;
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lparams.embedding = params.embedding;
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llama_context * lctx = llama_init_from_file(params.model.c_str(), lparams);
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if (lctx == NULL) {
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llama_model * model = llama_load_model_from_file(params.model.c_str(), lparams);
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if (model == NULL) {
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fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
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return NULL;
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return std::make_tuple(nullptr, nullptr);
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}
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llama_context * lctx = llama_new_context_with_model(model, lparams);
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if (lctx == NULL) {
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fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str());
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llama_free_model(model);
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return std::make_tuple(nullptr, nullptr);
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}
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if (!params.lora_adapter.empty()) {
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int err = llama_apply_lora_from_file(lctx,
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int err = llama_model_apply_lora_from_file(model,
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params.lora_adapter.c_str(),
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params.lora_base.empty() ? NULL : params.lora_base.c_str(),
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params.n_threads);
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if (err != 0) {
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fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__);
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return NULL;
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llama_free(lctx);
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llama_free_model(model);
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return std::make_tuple(nullptr, nullptr);
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}
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}
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return lctx;
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return std::make_tuple(model, lctx);
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}
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void console_init(console_state & con_st) {
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@ -9,6 +9,7 @@
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#include <random>
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#include <thread>
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#include <unordered_map>
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#include <tuple>
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#if !defined (_WIN32)
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#include <stdio.h>
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@ -95,7 +96,7 @@ std::vector<llama_token> llama_tokenize(struct llama_context * ctx, const std::s
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// Model utils
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//
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struct llama_context * llama_init_from_gpt_params(const gpt_params & params);
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std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_params(const gpt_params & params);
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//
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// Console utils
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@ -37,11 +37,12 @@ int main(int argc, char ** argv) {
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llama_init_backend();
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llama_model * model;
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llama_context * ctx;
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// load the model
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ctx = llama_init_from_gpt_params(params);
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if (ctx == NULL) {
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std::tie(model, ctx) = llama_init_from_gpt_params(params);
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if (model == NULL) {
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fprintf(stderr, "%s: error: unable to load model\n", __func__);
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return 1;
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}
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@ -90,6 +91,7 @@ int main(int argc, char ** argv) {
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llama_print_timings(ctx);
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llama_free(ctx);
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llama_free_model(model);
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return 0;
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}
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@ -107,12 +107,13 @@ int main(int argc, char ** argv) {
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llama_init_backend();
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llama_model * model;
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llama_context * ctx;
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g_ctx = &ctx;
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// load the model and apply lora adapter, if any
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ctx = llama_init_from_gpt_params(params);
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if (ctx == NULL) {
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std::tie(model, ctx) = llama_init_from_gpt_params(params);
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if (model == NULL) {
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fprintf(stderr, "%s: error: unable to load model\n", __func__);
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return 1;
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}
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@ -139,6 +140,7 @@ int main(int argc, char ** argv) {
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llama_print_timings(ctx);
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llama_free(ctx);
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llama_free_model(model);
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return 0;
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}
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@ -147,6 +149,7 @@ int main(int argc, char ** argv) {
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if (params.export_cgraph) {
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llama_eval_export(ctx, "llama.ggml");
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llama_free(ctx);
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llama_free_model(model);
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return 0;
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}
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@ -666,6 +669,7 @@ int main(int argc, char ** argv) {
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llama_print_timings(ctx);
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llama_free(ctx);
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llama_free_model(model);
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return 0;
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}
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@ -149,11 +149,12 @@ int main(int argc, char ** argv) {
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llama_init_backend();
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llama_model * model;
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llama_context * ctx;
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// load the model and apply lora adapter, if any
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ctx = llama_init_from_gpt_params(params);
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if (ctx == NULL) {
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std::tie(model, ctx) = llama_init_from_gpt_params(params);
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if (model == NULL) {
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fprintf(stderr, "%s: error: unable to load model\n", __func__);
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return 1;
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}
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@ -169,6 +170,7 @@ int main(int argc, char ** argv) {
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llama_print_timings(ctx);
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llama_free(ctx);
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llama_free_model(model);
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return 0;
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}
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@ -320,6 +320,7 @@ int main(int argc, char ** argv) {
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fprintf(stderr, "Loading model\n");
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const int64_t t_main_start_us = ggml_time_us();
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llama_model * model;
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llama_context * ctx;
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{
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@ -330,10 +331,18 @@ int main(int argc, char ** argv) {
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lparams.f16_kv = false;
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lparams.use_mlock = false;
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ctx = llama_init_from_file(params.model.c_str(), lparams);
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model = llama_load_model_from_file(params.model.c_str(), lparams);
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if (model == NULL) {
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fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
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return 1;
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}
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ctx = llama_new_context_with_model(model, lparams);
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if (ctx == NULL) {
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fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
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fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str());
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llama_free_model(model);
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return 1;
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}
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}
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@ -357,6 +366,7 @@ int main(int argc, char ** argv) {
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fprintf(stderr, "%s: error: Quantization should be tested with a float model, "
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"this model contains already quantized layers (%s is type %d)\n", __func__, kv_tensor.first.c_str(), kv_tensor.second->type);
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llama_free(ctx);
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llama_free_model(model);
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return 1;
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}
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included_layers++;
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@ -415,6 +425,7 @@ int main(int argc, char ** argv) {
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llama_free(ctx);
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llama_free_model(model);
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// report timing
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{
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const int64_t t_main_end_us = ggml_time_us();
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@ -35,12 +35,22 @@ int main(int argc, char ** argv) {
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auto last_n_tokens_data = std::vector<llama_token>(params.repeat_last_n, 0);
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// init
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auto ctx = llama_init_from_file(params.model.c_str(), lparams);
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auto model = llama_load_model_from_file(params.model.c_str(), lparams);
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if (model == nullptr) {
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return 1;
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}
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auto ctx = llama_new_context_with_model(model, lparams);
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if (ctx == nullptr) {
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llama_free_model(model);
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return 1;
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}
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auto tokens = std::vector<llama_token>(params.n_ctx);
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auto n_prompt_tokens = llama_tokenize(ctx, params.prompt.c_str(), tokens.data(), int(tokens.size()), true);
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if (n_prompt_tokens < 1) {
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fprintf(stderr, "%s : failed to tokenize prompt\n", __func__);
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llama_free(ctx);
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llama_free_model(model);
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return 1;
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}
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@ -84,6 +94,8 @@ int main(int argc, char ** argv) {
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printf("%s", next_token_str);
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if (llama_eval(ctx, &next_token, 1, n_past, params.n_threads)) {
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fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
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llama_free(ctx);
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llama_free_model(model);
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return 1;
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}
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n_past += 1;
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@ -91,23 +103,27 @@ int main(int argc, char ** argv) {
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printf("\n\n");
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// free old model
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// free old context
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llama_free(ctx);
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// load new model
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auto ctx2 = llama_init_from_file(params.model.c_str(), lparams);
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// make new context
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auto ctx2 = llama_new_context_with_model(model, lparams);
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// Load state (rng, logits, embedding and kv_cache) from file
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{
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FILE *fp_read = fopen("dump_state.bin", "rb");
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if (state_size != llama_get_state_size(ctx2)) {
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fprintf(stderr, "\n%s : failed to validate state size\n", __func__);
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llama_free(ctx2);
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llama_free_model(model);
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return 1;
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}
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const size_t ret = fread(state_mem, 1, state_size, fp_read);
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if (ret != state_size) {
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fprintf(stderr, "\n%s : failed to read state\n", __func__);
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llama_free(ctx2);
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llama_free_model(model);
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return 1;
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}
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@ -138,6 +154,8 @@ int main(int argc, char ** argv) {
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printf("%s", next_token_str);
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if (llama_eval(ctx2, &next_token, 1, n_past, params.n_threads)) {
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fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
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llama_free(ctx2);
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llama_free_model(model);
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return 1;
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}
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n_past += 1;
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@ -145,5 +163,8 @@ int main(int argc, char ** argv) {
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printf("\n\n");
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llama_free(ctx2);
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llama_free_model(model);
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return 0;
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}
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@ -115,6 +115,7 @@ struct llama_server_context {
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std::vector<llama_token> embd;
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std::vector<llama_token> last_n_tokens;
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llama_model * model = nullptr;
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llama_context * ctx = nullptr;
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gpt_params params;
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@ -130,6 +131,10 @@ struct llama_server_context {
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llama_free(ctx);
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ctx = nullptr;
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}
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if (model) {
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llama_free_model(model);
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model = nullptr;
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}
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}
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void rewind() {
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@ -150,8 +155,8 @@ struct llama_server_context {
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bool loadModel(const gpt_params & params_) {
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params = params_;
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ctx = llama_init_from_gpt_params(params);
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if (ctx == nullptr) {
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std::tie(model, ctx) = llama_init_from_gpt_params(params);
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if (model == nullptr) {
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LOG_ERROR("unable to load model", { { "model", params_.model } });
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return false;
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}
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@ -68,11 +68,12 @@ int main(int argc, char ** argv)
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llama_init_backend();
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llama_context * ctx ;
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llama_model * model;
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llama_context * ctx;
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ctx = llama_init_from_gpt_params( params );
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std::tie(model, ctx) = llama_init_from_gpt_params( params );
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if ( ctx == NULL )
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if ( model == NULL )
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{
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fprintf( stderr , "%s: error: unable to load model\n" , __func__ );
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return 1;
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@ -170,6 +171,7 @@ int main(int argc, char ** argv)
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} // wend of main loop
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llama_free( ctx );
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llama_free_model( model );
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return 0;
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}
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@ -3054,7 +3054,8 @@ int main(int argc, char ** argv) {
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struct llama_context_params llama_params = llama_context_default_params();
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llama_params.vocab_only = true;
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struct llama_context * lctx = llama_init_from_file(params.fn_vocab_model, llama_params);
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struct llama_model * lmodel = llama_load_model_from_file(params.fn_vocab_model, llama_params);
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struct llama_context * lctx = llama_new_context_with_model(lmodel, llama_params);
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struct llama_vocab vocab;
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{
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@ -3395,6 +3396,8 @@ int main(int argc, char ** argv) {
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delete[] compute_addr;
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delete[] compute_buf_0;
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delete[] compute_buf_1;
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llama_free(lctx);
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llama_free_model(lmodel);
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ggml_free(model.ctx);
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return 0;
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172
llama.cpp
172
llama.cpp
@ -182,6 +182,19 @@ struct llama_kv_cache {
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}
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};
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struct llama_vocab {
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using id = int32_t;
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using token = std::string;
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struct token_score {
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token tok;
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float score;
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};
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std::unordered_map<token, id> token_to_id;
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std::vector<token_score> id_to_token;
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};
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struct llama_model {
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e_model type = MODEL_UNKNOWN;
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@ -198,10 +211,6 @@ struct llama_model {
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// context
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struct ggml_context * ctx = NULL;
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// key + value cache for the self attention
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// TODO: move to llama_state
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struct llama_kv_cache kv_self;
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// the model memory buffer
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llama_ctx_buffer buf;
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@ -215,6 +224,11 @@ struct llama_model {
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// for quantize-stats only
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std::vector<std::pair<std::string, struct ggml_tensor *>> tensors_by_name;
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int64_t t_load_us = 0;
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int64_t t_start_us = 0;
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llama_vocab vocab;
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~llama_model() {
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if (ctx) {
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ggml_free(ctx);
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@ -233,24 +247,11 @@ struct llama_model {
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}
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};
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struct llama_vocab {
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using id = int32_t;
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using token = std::string;
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struct token_score {
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token tok;
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float score;
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};
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std::unordered_map<token, id> token_to_id;
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std::vector<token_score> id_to_token;
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};
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struct llama_context {
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llama_context(const llama_model & model, const llama_vocab & vocab) : model(model), vocab(vocab), t_load_us(model.t_load_us), t_start_us(model.t_start_us) {}
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std::mt19937 rng;
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int64_t t_load_us = 0;
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int64_t t_start_us = 0;
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bool has_evaluated_once = false;
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int64_t t_sample_us = 0;
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@ -261,8 +262,16 @@ struct llama_context {
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int32_t n_eval = 0; // number of eval calls
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int32_t n_p_eval = 0; // number of tokens in eval calls for the prompt (with batch size > 1)
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llama_model model;
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llama_vocab vocab;
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const llama_model & model;
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const llama_vocab & vocab;
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bool model_owner = false;
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int64_t t_load_us;
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int64_t t_start_us;
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// key + value cache for the self attention
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struct llama_kv_cache kv_self;
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size_t mem_per_token = 0;
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@ -1033,7 +1042,8 @@ static const char *llama_model_type_name(e_model type) {
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static void llama_model_load_internal(
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const std::string & fname,
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llama_context & lctx,
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llama_model & model,
|
||||
llama_vocab & vocab,
|
||||
int n_ctx,
|
||||
int n_batch,
|
||||
int n_gpu_layers,
|
||||
@ -1047,12 +1057,11 @@ static void llama_model_load_internal(
|
||||
llama_progress_callback progress_callback,
|
||||
void * progress_callback_user_data) {
|
||||
|
||||
lctx.t_start_us = ggml_time_us();
|
||||
model.t_start_us = ggml_time_us();
|
||||
|
||||
std::unique_ptr<llama_model_loader> ml(new llama_model_loader(fname, use_mmap, vocab_only));
|
||||
|
||||
lctx.vocab = std::move(ml->file_loaders.at(0)->vocab);
|
||||
auto & model = lctx.model;
|
||||
vocab = std::move(ml->file_loaders.at(0)->vocab);
|
||||
model.hparams = ml->file_loaders.at(0)->hparams;
|
||||
model.n_gpu_layers = n_gpu_layers;
|
||||
llama_file_version file_version = ml->file_loaders.at(0)->file_version;
|
||||
@ -1122,15 +1131,15 @@ static void llama_model_load_internal(
|
||||
|
||||
// create the ggml context
|
||||
{
|
||||
lctx.model.buf.resize(ctx_size);
|
||||
model.buf.resize(ctx_size);
|
||||
if (use_mlock) {
|
||||
lctx.model.mlock_buf.init(lctx.model.buf.addr);
|
||||
lctx.model.mlock_buf.grow_to(lctx.model.buf.size);
|
||||
model.mlock_buf.init(model.buf.addr);
|
||||
model.mlock_buf.grow_to(model.buf.size);
|
||||
}
|
||||
|
||||
struct ggml_init_params params = {
|
||||
/*.mem_size =*/ lctx.model.buf.size,
|
||||
/*.mem_buffer =*/ lctx.model.buf.addr,
|
||||
/*.mem_size =*/ model.buf.size,
|
||||
/*.mem_buffer =*/ model.buf.addr,
|
||||
/*.no_alloc =*/ ml->use_mmap,
|
||||
};
|
||||
|
||||
@ -1311,7 +1320,7 @@ static void llama_model_load_internal(
|
||||
}
|
||||
#endif
|
||||
|
||||
ml->load_all_data(progress_callback, progress_callback_user_data, use_mlock ? &lctx.model.mlock_mmap : NULL);
|
||||
ml->load_all_data(progress_callback, progress_callback_user_data, use_mlock ? &model.mlock_mmap : NULL);
|
||||
|
||||
if (progress_callback) {
|
||||
progress_callback(1.0f, progress_callback_user_data);
|
||||
@ -1321,12 +1330,13 @@ static void llama_model_load_internal(
|
||||
|
||||
// loading time will be recalculate after the first eval, so
|
||||
// we take page faults deferred by mmap() into consideration
|
||||
lctx.t_load_us = ggml_time_us() - lctx.t_start_us;
|
||||
model.t_load_us = ggml_time_us() - model.t_start_us;
|
||||
}
|
||||
|
||||
static bool llama_model_load(
|
||||
const std::string & fname,
|
||||
llama_context & lctx,
|
||||
llama_model & model,
|
||||
llama_vocab & vocab,
|
||||
int n_ctx,
|
||||
int n_batch,
|
||||
int n_gpu_layers,
|
||||
@ -1340,7 +1350,7 @@ static bool llama_model_load(
|
||||
llama_progress_callback progress_callback,
|
||||
void *progress_callback_user_data) {
|
||||
try {
|
||||
llama_model_load_internal(fname, lctx, n_ctx, n_batch, n_gpu_layers, main_gpu, tensor_split, low_vram, memory_type,
|
||||
llama_model_load_internal(fname, model, vocab, n_ctx, n_batch, n_gpu_layers, main_gpu, tensor_split, low_vram, memory_type,
|
||||
use_mmap, use_mlock, vocab_only, progress_callback, progress_callback_user_data);
|
||||
return true;
|
||||
} catch (const std::exception & err) {
|
||||
@ -1378,7 +1388,7 @@ static bool llama_eval_internal(
|
||||
const auto & model = lctx.model;
|
||||
const auto & hparams = model.hparams;
|
||||
|
||||
const auto & kv_self = model.kv_self;
|
||||
const auto & kv_self = lctx.kv_self;
|
||||
|
||||
LLAMA_ASSERT(!!kv_self.ctx);
|
||||
|
||||
@ -1726,7 +1736,7 @@ static bool llama_eval_internal(
|
||||
//memcpy(embd_w.data(), ggml_get_data(cur), sizeof(float)*n_vocab*N);
|
||||
|
||||
// update kv token count
|
||||
lctx.model.kv_self.n = n_past + N;
|
||||
lctx.kv_self.n = n_past + N;
|
||||
|
||||
// extract logits
|
||||
{
|
||||
@ -2634,12 +2644,39 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
||||
// interface implementation
|
||||
//
|
||||
|
||||
struct llama_context * llama_init_from_file(
|
||||
struct llama_model * llama_load_model_from_file(
|
||||
const char * path_model,
|
||||
struct llama_context_params params) {
|
||||
ggml_time_init();
|
||||
|
||||
llama_context * ctx = new llama_context;
|
||||
llama_model * model = new llama_model;
|
||||
|
||||
ggml_type memory_type = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32;
|
||||
|
||||
if (!llama_model_load(path_model, *model, model->vocab, params.n_ctx, params.n_batch, params.n_gpu_layers,
|
||||
params.main_gpu, params.tensor_split, params.low_vram, memory_type, params.use_mmap, params.use_mlock,
|
||||
params.vocab_only, params.progress_callback, params.progress_callback_user_data)) {
|
||||
delete model;
|
||||
fprintf(stderr, "%s: failed to load model\n", __func__);
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
return model;
|
||||
}
|
||||
|
||||
void llama_free_model(struct llama_model * model) {
|
||||
delete model;
|
||||
}
|
||||
|
||||
struct llama_context * llama_new_context_with_model(
|
||||
struct llama_model * model,
|
||||
struct llama_context_params params) {
|
||||
|
||||
if (!model) {
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
llama_context * ctx = new llama_context(*model, model->vocab);
|
||||
|
||||
if (params.seed < 0) {
|
||||
params.seed = time(NULL);
|
||||
@ -2667,24 +2704,16 @@ struct llama_context * llama_init_from_file(
|
||||
|
||||
ggml_type memory_type = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32;
|
||||
|
||||
if (!llama_model_load(path_model, *ctx, params.n_ctx, params.n_batch, params.n_gpu_layers, params.main_gpu,
|
||||
params.tensor_split, params.low_vram, memory_type, params.use_mmap, params.use_mlock,
|
||||
params.vocab_only, params.progress_callback, params.progress_callback_user_data)) {
|
||||
fprintf(stderr, "%s: failed to load model\n", __func__);
|
||||
llama_free(ctx);
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
// reserve memory for context buffers
|
||||
if (!params.vocab_only) {
|
||||
if (!kv_cache_init(ctx->model.hparams, ctx->model.kv_self, memory_type, ctx->model.hparams.n_ctx, params.n_gpu_layers)) {
|
||||
if (!kv_cache_init(ctx->model.hparams, ctx->kv_self, memory_type, ctx->model.hparams.n_ctx, params.n_gpu_layers)) {
|
||||
fprintf(stderr, "%s: kv_cache_init() failed for self-attention cache\n", __func__);
|
||||
llama_free(ctx);
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
{
|
||||
const size_t memory_size = ggml_nbytes(ctx->model.kv_self.k) + ggml_nbytes(ctx->model.kv_self.v);
|
||||
const size_t memory_size = ggml_nbytes(ctx->kv_self.k) + ggml_nbytes(ctx->kv_self.v);
|
||||
fprintf(stderr, "%s: kv self size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
|
||||
}
|
||||
|
||||
@ -2736,8 +2765,8 @@ struct llama_context * llama_init_from_file(
|
||||
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "data", data_ptr, data_size, max_size));
|
||||
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "eval", ctx->buf_compute.addr, ctx->buf_compute.size, 0));
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "kv", ctx->model.kv_self.buf.addr, ctx->model.kv_self.buf.size, 0));
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "eval", ctx->buf_compute.addr, ctx->buf_compute.size, 0));
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "kv", ctx->kv_self.buf.addr, ctx->kv_self.buf.size, 0));
|
||||
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "scr0", ctx->buf_scratch[0].addr, ctx->buf_scratch[0].size, 0));
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "scr1", ctx->buf_scratch[1].addr, ctx->buf_scratch[1].size, 0));
|
||||
@ -2748,7 +2777,23 @@ struct llama_context * llama_init_from_file(
|
||||
return ctx;
|
||||
}
|
||||
|
||||
struct llama_context * llama_init_from_file(
|
||||
const char * path_model,
|
||||
struct llama_context_params params) {
|
||||
|
||||
struct llama_model * model = llama_load_model_from_file(path_model, params);
|
||||
if (!model) {
|
||||
return nullptr;
|
||||
}
|
||||
struct llama_context * ctx = llama_new_context_with_model(model, params);
|
||||
ctx->model_owner = true;
|
||||
return ctx;
|
||||
}
|
||||
|
||||
void llama_free(struct llama_context * ctx) {
|
||||
if (ctx->model_owner) {
|
||||
delete &ctx->model;
|
||||
}
|
||||
delete ctx;
|
||||
}
|
||||
|
||||
@ -2765,11 +2810,9 @@ int llama_model_quantize(
|
||||
}
|
||||
}
|
||||
|
||||
int llama_apply_lora_from_file_internal(struct llama_context * ctx, const char * path_lora, const char * path_base_model, int n_threads) {
|
||||
int llama_apply_lora_from_file_internal(const struct llama_model & model, const char * path_lora, const char * path_base_model, int n_threads) {
|
||||
fprintf(stderr, "%s: applying lora adapter from '%s' - please wait ...\n", __func__, path_lora);
|
||||
|
||||
auto & model = ctx->model;
|
||||
|
||||
const int64_t t_start_lora_us = ggml_time_us();
|
||||
|
||||
auto fin = std::ifstream(path_lora, std::ios::binary);
|
||||
@ -3012,7 +3055,16 @@ int llama_apply_lora_from_file_internal(struct llama_context * ctx, const char *
|
||||
|
||||
int llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lora, const char * path_base_model, int n_threads) {
|
||||
try {
|
||||
return llama_apply_lora_from_file_internal(ctx, path_lora, path_base_model, n_threads);
|
||||
return llama_apply_lora_from_file_internal(ctx->model, path_lora, path_base_model, n_threads);
|
||||
} catch (const std::exception & err) {
|
||||
fprintf(stderr, "%s: failed to apply lora adapter: %s\n", __func__, err.what());
|
||||
return 1;
|
||||
}
|
||||
}
|
||||
|
||||
int llama_model_apply_lora_from_file(const struct llama_model * model, const char * path_lora, const char * path_base_model, int n_threads) {
|
||||
try {
|
||||
return llama_apply_lora_from_file_internal(*model, path_lora, path_base_model, n_threads);
|
||||
} catch (const std::exception & err) {
|
||||
fprintf(stderr, "%s: failed to apply lora adapter: %s\n", __func__, err.what());
|
||||
return 1;
|
||||
@ -3020,7 +3072,7 @@ int llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lor
|
||||
}
|
||||
|
||||
int llama_get_kv_cache_token_count(const struct llama_context * ctx) {
|
||||
return ctx->model.kv_self.n;
|
||||
return ctx->kv_self.n;
|
||||
}
|
||||
|
||||
#define LLAMA_MAX_RNG_STATE (64*1024)
|
||||
@ -3045,7 +3097,7 @@ size_t llama_get_state_size(const struct llama_context * ctx) {
|
||||
const size_t s_embedding = ctx->embedding.size() * sizeof(float);
|
||||
const size_t s_kv_size = sizeof(size_t);
|
||||
const size_t s_kv_ntok = sizeof(int);
|
||||
const size_t s_kv = ctx->model.kv_self.buf.size;
|
||||
const size_t s_kv = ctx->kv_self.buf.size;
|
||||
|
||||
const size_t s_total = (
|
||||
+ s_rng_size
|
||||
@ -3111,7 +3163,7 @@ size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
|
||||
|
||||
// copy kv cache
|
||||
{
|
||||
const auto & kv_self = ctx->model.kv_self;
|
||||
const auto & kv_self = ctx->kv_self;
|
||||
const auto & hparams = ctx->model.hparams;
|
||||
const int n_layer = hparams.n_layer;
|
||||
const int n_embd = hparams.n_embd;
|
||||
@ -3215,7 +3267,7 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
|
||||
|
||||
// set kv cache
|
||||
{
|
||||
const auto & kv_self = ctx->model.kv_self;
|
||||
const auto & kv_self = ctx->kv_self;
|
||||
const auto & hparams = ctx->model.hparams;
|
||||
const int n_layer = hparams.n_layer;
|
||||
const int n_embd = hparams.n_embd;
|
||||
@ -3259,7 +3311,7 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
|
||||
ggml_free(cpy_ctx);
|
||||
}
|
||||
|
||||
ctx->model.kv_self.n = kv_ntok;
|
||||
ctx->kv_self.n = kv_ntok;
|
||||
}
|
||||
|
||||
const size_t nread = inp - src;
|
||||
@ -3506,6 +3558,6 @@ const char * llama_print_system_info(void) {
|
||||
}
|
||||
|
||||
// For internal test use
|
||||
std::vector<std::pair<std::string, struct ggml_tensor *>>& llama_internal_get_tensor_map(struct llama_context * ctx) {
|
||||
const std::vector<std::pair<std::string, struct ggml_tensor *>>& llama_internal_get_tensor_map(struct llama_context * ctx) {
|
||||
return ctx->model.tensors_by_name;
|
||||
}
|
||||
|
||||
35
llama.h
35
llama.h
@ -26,6 +26,14 @@
|
||||
# define LLAMA_API
|
||||
#endif
|
||||
|
||||
#ifdef __GNUC__
|
||||
# define DEPRECATED(func, hint) func __attribute__((deprecated(hint)))
|
||||
#elif defined(_MSC_VER)
|
||||
# define DEPRECATED(func, hint) __declspec(deprecated(hint)) func
|
||||
#else
|
||||
# define DEPRECATED(func, hint) func
|
||||
#endif
|
||||
|
||||
#define LLAMA_FILE_MAGIC_GGJT 0x67676a74u // 'ggjt'
|
||||
#define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla'
|
||||
#define LLAMA_FILE_MAGIC_GGMF 0x67676d66u // 'ggmf'
|
||||
@ -53,6 +61,7 @@ extern "C" {
|
||||
// TODO: show sample usage
|
||||
//
|
||||
|
||||
struct llama_model;
|
||||
struct llama_context;
|
||||
|
||||
typedef int llama_token;
|
||||
@ -136,12 +145,23 @@ extern "C" {
|
||||
|
||||
LLAMA_API int64_t llama_time_us();
|
||||
|
||||
LLAMA_API struct llama_model * llama_load_model_from_file(
|
||||
const char * path_model,
|
||||
struct llama_context_params params);
|
||||
|
||||
LLAMA_API void llama_free_model(struct llama_model * model);
|
||||
|
||||
LLAMA_API struct llama_context * llama_new_context_with_model(
|
||||
struct llama_model * model,
|
||||
struct llama_context_params params);
|
||||
|
||||
// Various functions for loading a ggml llama model.
|
||||
// Allocate (almost) all memory needed for the model.
|
||||
// Return NULL on failure
|
||||
LLAMA_API struct llama_context * llama_init_from_file(
|
||||
LLAMA_API DEPRECATED(struct llama_context * llama_init_from_file(
|
||||
const char * path_model,
|
||||
struct llama_context_params params);
|
||||
struct llama_context_params params),
|
||||
"please use llama_load_model_from_file combined with llama_new_context_with_model instead");
|
||||
|
||||
// Frees all allocated memory
|
||||
LLAMA_API void llama_free(struct llama_context * ctx);
|
||||
@ -158,8 +178,15 @@ extern "C" {
|
||||
// The model needs to be reloaded before applying a new adapter, otherwise the adapter
|
||||
// will be applied on top of the previous one
|
||||
// Returns 0 on success
|
||||
LLAMA_API int llama_apply_lora_from_file(
|
||||
LLAMA_API DEPRECATED(int llama_apply_lora_from_file(
|
||||
struct llama_context * ctx,
|
||||
const char * path_lora,
|
||||
const char * path_base_model,
|
||||
int n_threads),
|
||||
"please use llama_model_apply_lora_from_file instead");
|
||||
|
||||
LLAMA_API int llama_model_apply_lora_from_file(
|
||||
const struct llama_model * model,
|
||||
const char * path_lora,
|
||||
const char * path_base_model,
|
||||
int n_threads);
|
||||
@ -310,7 +337,7 @@ extern "C" {
|
||||
#include <string>
|
||||
struct ggml_tensor;
|
||||
|
||||
std::vector<std::pair<std::string, struct ggml_tensor *>>& llama_internal_get_tensor_map(struct llama_context * ctx);
|
||||
const std::vector<std::pair<std::string, struct ggml_tensor *>>& llama_internal_get_tensor_map(struct llama_context * ctx);
|
||||
|
||||
#endif
|
||||
|
||||
|
||||
@ -28,6 +28,7 @@ int main(int argc, char **argv) {
|
||||
|
||||
fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str());
|
||||
|
||||
llama_model * model;
|
||||
llama_context * ctx;
|
||||
|
||||
// load the vocab
|
||||
@ -36,10 +37,18 @@ int main(int argc, char **argv) {
|
||||
|
||||
lparams.vocab_only = true;
|
||||
|
||||
ctx = llama_init_from_file(fname.c_str(), lparams);
|
||||
model = llama_load_model_from_file(fname.c_str(), lparams);
|
||||
|
||||
if (model == NULL) {
|
||||
fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
|
||||
return 1;
|
||||
}
|
||||
|
||||
ctx = llama_new_context_with_model(model, lparams);
|
||||
|
||||
if (ctx == NULL) {
|
||||
fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
|
||||
llama_free_model(model);
|
||||
return 1;
|
||||
}
|
||||
}
|
||||
@ -48,6 +57,8 @@ int main(int argc, char **argv) {
|
||||
|
||||
if (n_vocab != 32000) {
|
||||
fprintf(stderr, "%s : expected 32000 tokens, got %d\n", __func__, n_vocab);
|
||||
llama_free_model(model);
|
||||
llama_free(ctx);
|
||||
return 2;
|
||||
}
|
||||
|
||||
@ -77,10 +88,13 @@ int main(int argc, char **argv) {
|
||||
}
|
||||
fprintf(stderr, "\n");
|
||||
|
||||
llama_free_model(model);
|
||||
llama_free(ctx);
|
||||
return 3;
|
||||
}
|
||||
}
|
||||
|
||||
llama_free_model(model);
|
||||
llama_free(ctx);
|
||||
|
||||
return 0;
|
||||
|
||||
Loading…
Reference in New Issue
Block a user