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
13 changed files with 244 additions and 92 deletions
172
llama.cpp
172
llama.cpp
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@ -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,
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llama_vocab & vocab,
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int n_ctx,
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int n_batch,
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int n_gpu_layers,
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@ -1047,12 +1057,11 @@ static void llama_model_load_internal(
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llama_progress_callback progress_callback,
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void * progress_callback_user_data) {
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lctx.t_start_us = ggml_time_us();
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model.t_start_us = ggml_time_us();
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std::unique_ptr<llama_model_loader> ml(new llama_model_loader(fname, use_mmap, vocab_only));
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lctx.vocab = std::move(ml->file_loaders.at(0)->vocab);
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auto & model = lctx.model;
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vocab = std::move(ml->file_loaders.at(0)->vocab);
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model.hparams = ml->file_loaders.at(0)->hparams;
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model.n_gpu_layers = n_gpu_layers;
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llama_file_version file_version = ml->file_loaders.at(0)->file_version;
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@ -1122,15 +1131,15 @@ static void llama_model_load_internal(
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// create the ggml context
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{
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lctx.model.buf.resize(ctx_size);
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model.buf.resize(ctx_size);
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if (use_mlock) {
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lctx.model.mlock_buf.init(lctx.model.buf.addr);
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lctx.model.mlock_buf.grow_to(lctx.model.buf.size);
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model.mlock_buf.init(model.buf.addr);
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model.mlock_buf.grow_to(model.buf.size);
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}
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struct ggml_init_params params = {
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/*.mem_size =*/ lctx.model.buf.size,
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/*.mem_buffer =*/ lctx.model.buf.addr,
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/*.mem_size =*/ model.buf.size,
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/*.mem_buffer =*/ model.buf.addr,
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/*.no_alloc =*/ ml->use_mmap,
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};
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@ -1311,7 +1320,7 @@ static void llama_model_load_internal(
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}
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#endif
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ml->load_all_data(progress_callback, progress_callback_user_data, use_mlock ? &lctx.model.mlock_mmap : NULL);
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ml->load_all_data(progress_callback, progress_callback_user_data, use_mlock ? &model.mlock_mmap : NULL);
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if (progress_callback) {
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progress_callback(1.0f, progress_callback_user_data);
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@ -1321,12 +1330,13 @@ static void llama_model_load_internal(
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// loading time will be recalculate after the first eval, so
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// we take page faults deferred by mmap() into consideration
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lctx.t_load_us = ggml_time_us() - lctx.t_start_us;
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model.t_load_us = ggml_time_us() - model.t_start_us;
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}
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static bool llama_model_load(
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const std::string & fname,
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llama_context & lctx,
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llama_model & model,
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llama_vocab & vocab,
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int n_ctx,
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int n_batch,
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int n_gpu_layers,
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@ -1340,7 +1350,7 @@ static bool llama_model_load(
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llama_progress_callback progress_callback,
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void *progress_callback_user_data) {
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try {
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llama_model_load_internal(fname, lctx, n_ctx, n_batch, n_gpu_layers, main_gpu, tensor_split, low_vram, memory_type,
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llama_model_load_internal(fname, model, vocab, n_ctx, n_batch, n_gpu_layers, main_gpu, tensor_split, low_vram, memory_type,
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use_mmap, use_mlock, vocab_only, progress_callback, progress_callback_user_data);
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return true;
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} catch (const std::exception & err) {
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@ -1378,7 +1388,7 @@ static bool llama_eval_internal(
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const auto & model = lctx.model;
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const auto & hparams = model.hparams;
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const auto & kv_self = model.kv_self;
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const auto & kv_self = lctx.kv_self;
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LLAMA_ASSERT(!!kv_self.ctx);
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@ -1726,7 +1736,7 @@ static bool llama_eval_internal(
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//memcpy(embd_w.data(), ggml_get_data(cur), sizeof(float)*n_vocab*N);
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// update kv token count
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lctx.model.kv_self.n = n_past + N;
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lctx.kv_self.n = n_past + N;
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// extract logits
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{
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@ -2634,12 +2644,39 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
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// interface implementation
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//
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struct llama_context * llama_init_from_file(
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struct llama_model * llama_load_model_from_file(
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const char * path_model,
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struct llama_context_params params) {
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ggml_time_init();
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llama_context * ctx = new llama_context;
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llama_model * model = new llama_model;
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ggml_type memory_type = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32;
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if (!llama_model_load(path_model, *model, model->vocab, params.n_ctx, params.n_batch, params.n_gpu_layers,
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params.main_gpu, params.tensor_split, params.low_vram, memory_type, params.use_mmap, params.use_mlock,
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params.vocab_only, params.progress_callback, params.progress_callback_user_data)) {
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delete model;
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fprintf(stderr, "%s: failed to load model\n", __func__);
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return nullptr;
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}
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return model;
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}
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void llama_free_model(struct llama_model * model) {
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delete model;
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}
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struct llama_context * llama_new_context_with_model(
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struct llama_model * model,
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struct llama_context_params params) {
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if (!model) {
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return nullptr;
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}
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llama_context * ctx = new llama_context(*model, model->vocab);
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if (params.seed < 0) {
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params.seed = time(NULL);
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@ -2667,24 +2704,16 @@ struct llama_context * llama_init_from_file(
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ggml_type memory_type = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32;
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if (!llama_model_load(path_model, *ctx, params.n_ctx, params.n_batch, params.n_gpu_layers, params.main_gpu,
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params.tensor_split, params.low_vram, memory_type, params.use_mmap, params.use_mlock,
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params.vocab_only, params.progress_callback, params.progress_callback_user_data)) {
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fprintf(stderr, "%s: failed to load model\n", __func__);
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llama_free(ctx);
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return nullptr;
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}
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// reserve memory for context buffers
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if (!params.vocab_only) {
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if (!kv_cache_init(ctx->model.hparams, ctx->model.kv_self, memory_type, ctx->model.hparams.n_ctx, params.n_gpu_layers)) {
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if (!kv_cache_init(ctx->model.hparams, ctx->kv_self, memory_type, ctx->model.hparams.n_ctx, params.n_gpu_layers)) {
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fprintf(stderr, "%s: kv_cache_init() failed for self-attention cache\n", __func__);
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llama_free(ctx);
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return nullptr;
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}
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{
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const size_t memory_size = ggml_nbytes(ctx->model.kv_self.k) + ggml_nbytes(ctx->model.kv_self.v);
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const size_t memory_size = ggml_nbytes(ctx->kv_self.k) + ggml_nbytes(ctx->kv_self.v);
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fprintf(stderr, "%s: kv self size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
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}
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@ -2736,8 +2765,8 @@ struct llama_context * llama_init_from_file(
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LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "data", data_ptr, data_size, max_size));
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LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "eval", ctx->buf_compute.addr, ctx->buf_compute.size, 0));
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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));
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LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "eval", ctx->buf_compute.addr, ctx->buf_compute.size, 0));
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LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "kv", ctx->kv_self.buf.addr, ctx->kv_self.buf.size, 0));
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LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "scr0", ctx->buf_scratch[0].addr, ctx->buf_scratch[0].size, 0));
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LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "scr1", ctx->buf_scratch[1].addr, ctx->buf_scratch[1].size, 0));
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@ -2748,7 +2777,23 @@ struct llama_context * llama_init_from_file(
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return ctx;
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}
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struct llama_context * llama_init_from_file(
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const char * path_model,
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struct llama_context_params params) {
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struct llama_model * model = llama_load_model_from_file(path_model, params);
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if (!model) {
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return nullptr;
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}
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struct llama_context * ctx = llama_new_context_with_model(model, params);
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ctx->model_owner = true;
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return ctx;
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}
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void llama_free(struct llama_context * ctx) {
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if (ctx->model_owner) {
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delete &ctx->model;
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}
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delete ctx;
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}
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@ -2765,11 +2810,9 @@ int llama_model_quantize(
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}
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}
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int llama_apply_lora_from_file_internal(struct llama_context * ctx, const char * path_lora, const char * path_base_model, int n_threads) {
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int llama_apply_lora_from_file_internal(const struct llama_model & model, const char * path_lora, const char * path_base_model, int n_threads) {
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fprintf(stderr, "%s: applying lora adapter from '%s' - please wait ...\n", __func__, path_lora);
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auto & model = ctx->model;
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const int64_t t_start_lora_us = ggml_time_us();
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auto fin = std::ifstream(path_lora, std::ios::binary);
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@ -3012,7 +3055,16 @@ int llama_apply_lora_from_file_internal(struct llama_context * ctx, const char *
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int llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lora, const char * path_base_model, int n_threads) {
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try {
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return llama_apply_lora_from_file_internal(ctx, path_lora, path_base_model, n_threads);
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return llama_apply_lora_from_file_internal(ctx->model, path_lora, path_base_model, n_threads);
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} catch (const std::exception & err) {
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fprintf(stderr, "%s: failed to apply lora adapter: %s\n", __func__, err.what());
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return 1;
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}
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}
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int llama_model_apply_lora_from_file(const struct llama_model * model, const char * path_lora, const char * path_base_model, int n_threads) {
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try {
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return llama_apply_lora_from_file_internal(*model, path_lora, path_base_model, n_threads);
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} catch (const std::exception & err) {
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fprintf(stderr, "%s: failed to apply lora adapter: %s\n", __func__, err.what());
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return 1;
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@ -3020,7 +3072,7 @@ int llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lor
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}
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int llama_get_kv_cache_token_count(const struct llama_context * ctx) {
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return ctx->model.kv_self.n;
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return ctx->kv_self.n;
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}
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#define LLAMA_MAX_RNG_STATE (64*1024)
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@ -3045,7 +3097,7 @@ size_t llama_get_state_size(const struct llama_context * ctx) {
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const size_t s_embedding = ctx->embedding.size() * sizeof(float);
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const size_t s_kv_size = sizeof(size_t);
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const size_t s_kv_ntok = sizeof(int);
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const size_t s_kv = ctx->model.kv_self.buf.size;
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const size_t s_kv = ctx->kv_self.buf.size;
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const size_t s_total = (
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+ s_rng_size
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@ -3111,7 +3163,7 @@ size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
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// copy kv cache
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{
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const auto & kv_self = ctx->model.kv_self;
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const auto & kv_self = ctx->kv_self;
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const auto & hparams = ctx->model.hparams;
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const int n_layer = hparams.n_layer;
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const int n_embd = hparams.n_embd;
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@ -3215,7 +3267,7 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
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// set kv cache
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{
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const auto & kv_self = ctx->model.kv_self;
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const auto & kv_self = ctx->kv_self;
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const auto & hparams = ctx->model.hparams;
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const int n_layer = hparams.n_layer;
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const int n_embd = hparams.n_embd;
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@ -3259,7 +3311,7 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
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ggml_free(cpy_ctx);
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}
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ctx->model.kv_self.n = kv_ntok;
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ctx->kv_self.n = kv_ntok;
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}
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const size_t nread = inp - src;
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@ -3506,6 +3558,6 @@ const char * llama_print_system_info(void) {
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}
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// For internal test use
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std::vector<std::pair<std::string, struct ggml_tensor *>>& llama_internal_get_tensor_map(struct llama_context * ctx) {
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const std::vector<std::pair<std::string, struct ggml_tensor *>>& llama_internal_get_tensor_map(struct llama_context * ctx) {
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return ctx->model.tensors_by_name;
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}
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