diff options
Diffstat (limited to 'llama.cpp')
| -rw-r--r-- | llama.cpp | 172 |
1 files changed, 112 insertions, 60 deletions
@@ -182,6 +182,19 @@ struct llama_kv_cache { } }; +struct llama_vocab { + using id = int32_t; + using token = std::string; + + struct token_score { + token tok; + float score; + }; + + std::unordered_map<token, id> token_to_id; + std::vector<token_score> id_to_token; +}; + struct llama_model { e_model type = MODEL_UNKNOWN; @@ -198,10 +211,6 @@ struct llama_model { // context struct ggml_context * ctx = NULL; - // key + value cache for the self attention - // TODO: move to llama_state - struct llama_kv_cache kv_self; - // the model memory buffer llama_ctx_buffer buf; @@ -215,6 +224,11 @@ struct llama_model { // for quantize-stats only std::vector<std::pair<std::string, struct ggml_tensor *>> tensors_by_name; + int64_t t_load_us = 0; + int64_t t_start_us = 0; + + llama_vocab vocab; + ~llama_model() { if (ctx) { ggml_free(ctx); @@ -233,24 +247,11 @@ struct llama_model { } }; -struct llama_vocab { - using id = int32_t; - using token = std::string; - - struct token_score { - token tok; - float score; - }; - - std::unordered_map<token, id> token_to_id; - std::vector<token_score> id_to_token; -}; - struct llama_context { + 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) {} + std::mt19937 rng; - int64_t t_load_us = 0; - int64_t t_start_us = 0; bool has_evaluated_once = false; int64_t t_sample_us = 0; @@ -261,8 +262,16 @@ struct llama_context { int32_t n_eval = 0; // number of eval calls int32_t n_p_eval = 0; // number of tokens in eval calls for the prompt (with batch size > 1) - llama_model model; - llama_vocab vocab; + const llama_model & model; + const llama_vocab & vocab; + + bool model_owner = false; + + int64_t t_load_us; + int64_t t_start_us; + + // key + value cache for the self attention + struct llama_kv_cache kv_self; size_t mem_per_token = 0; @@ -1033,7 +1042,8 @@ static const char *llama_model_type_name(e_model type) { static void llama_model_load_internal( const std::string & fname, - llama_context & lctx, + 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; } |