diff --git a/common/common.cpp b/common/common.cpp index 2c05a4d4a17c1..be0656c2a5260 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -654,6 +654,18 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa params.hf_file = argv[i]; return true; } + if (arg == "-mpa" || arg == "--model-path-alias") { + CHECK_ARG + std::string model_derived_alias = argv[i]; + size_t equals_pos = model_derived_alias.find('='); + if (equals_pos != std::string::npos) { + std::string alias = model_derived_alias.substr(0, equals_pos); + std::string model_path = model_derived_alias.substr(equals_pos + 1); + params.derived_model_paths.emplace_back(alias, model_path); + } + + return true; + } if (arg == "--lora") { CHECK_ARG params.lora_adapter.emplace_back(argv[i], 1.0f); @@ -2045,6 +2057,21 @@ std::tuple llama_init_from_gpt_par } } + for (unsigned int i = 0; i < params.derived_model_paths.size(); ++i) { + const auto & derived_model_path = params.derived_model_paths[i]; + const std::string & derived_model_name = std::get<0>(derived_model_path); + const std::string & derived_model_file = std::get<1>(derived_model_path); + + llama_model * derived_model = llama_load_model_from_file(derived_model_file.c_str(), mparams); + + if (derived_model == NULL) { + fprintf(stderr, "%s: error: failed to load derived model '%s'\n", __func__, derived_model_file.c_str()); + } + + llama_model_set_name(derived_model, derived_model_name.c_str()); + llama_ctx_set_derived_model(lctx, derived_model); + } + for (unsigned int i = 0; i < params.lora_adapter.size(); ++i) { const std::string & lora_adapter = std::get<0>(params.lora_adapter[i]); float lora_scale = std::get<1>(params.lora_adapter[i]); diff --git a/common/common.h b/common/common.h index 65c0ef81adf7c..c440760d929a4 100644 --- a/common/common.h +++ b/common/common.h @@ -124,6 +124,9 @@ struct gpt_params { std::vector antiprompt; // strings upon which more user input is prompted (a.k.a. reverse prompts) std::vector kv_overrides; + // multiple derived models paths map + std::vector> derived_model_paths; // derived model paths + // TODO: avoid tuple, use struct std::vector> lora_adapter; // lora adapter path with user defined scale std::string lora_base = ""; // base model path for the lora adapter diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index 7d9ab34572b74..4fd5400965427 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -33,6 +33,7 @@ else() add_subdirectory(lookahead) add_subdirectory(lookup) add_subdirectory(main) + add_subdirectory(multi-adaptation) add_subdirectory(parallel) add_subdirectory(passkey) add_subdirectory(perplexity) diff --git a/examples/gguf-split/gguf-split.cpp b/examples/gguf-split/gguf-split.cpp index 881f0451c1455..553a4f1d819a3 100644 --- a/examples/gguf-split/gguf-split.cpp +++ b/examples/gguf-split/gguf-split.cpp @@ -32,8 +32,10 @@ struct split_params { int n_split_tensors = 128; std::string input; std::string output; + std::string tensor_set_file; bool no_tensor_first_split = false; bool dry_run = false; + bool customized_split = false; }; static void split_print_usage(const char * executable) { @@ -47,6 +49,7 @@ static void split_print_usage(const char * executable) { printf(" -h, --help show this help message and exit\n"); printf(" --version show version and build info\n"); printf(" --split split GGUF to multiple GGUF (enabled by default)\n"); + printf(" --tensor-set customize tensor set used to split. File contains modules, e.g. 'ffn_up.weight'"); printf(" --merge merge multiple GGUF to a single GGUF\n"); printf(" --split-max-tensors max tensors in each split (default: %d)\n", default_params.n_split_tensors); printf(" --split-max-size N(M|G) max size per split\n"); @@ -121,6 +124,16 @@ static void split_params_parse_ex(int argc, const char ** argv, split_params & p params.operation = SPLIT_OP_SPLIT; } + if (arg == "--tensor-set") { + arg_found = true; + if (++arg_idx >= argc) { + invalid_param = true; + break; + } + params.tensor_set_file = argv[arg_idx]; + params.customized_split = true; + } + if (is_mode_set) { throw std::invalid_argument("error: either --split-max-tensors or --split-max-size can be specified, but not both"); } @@ -180,12 +193,27 @@ static void zeros(std::ofstream & file, size_t n) { } } +static std::vector read_customized_tensors(const std::string & tensor_set_file) { + std::vector tensor_set; + std::ifstream f_tensor_set(tensor_set_file); + if (!f_tensor_set.is_open()) { + fprintf(stderr, "error: failed to open tensor set file %s\n", tensor_set_file.c_str()); + exit(EXIT_FAILURE); + } + std::string line; + while (std::getline(f_tensor_set, line)) { + tensor_set.push_back(line); + } + return tensor_set; +} + struct split_strategy { const split_params params; std::ifstream & f_input; struct gguf_context * ctx_gguf; struct ggml_context * ctx_meta = NULL; const int n_tensors; + std::string tensor_set_file; // one ctx_out per one output file std::vector ctx_outs; @@ -233,20 +261,45 @@ struct split_strategy { new_ctx_out(true); } - // process tensors one by one - size_t curr_tensors_size = 0; // current size by counting only tensors size (without metadata) - for (int i = 0; i < n_tensors; ++i) { - struct ggml_tensor * t = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i)); - // calculate the "imaginary" size = the current size + next tensor size - size_t n_bytes = GGML_PAD(ggml_nbytes(t), GGUF_DEFAULT_ALIGNMENT); - size_t next_tensors_size = curr_tensors_size + n_bytes; - if (should_split(i, next_tensors_size)) { - new_ctx_out(false); - curr_tensors_size = n_bytes; - } else { - curr_tensors_size = next_tensors_size; + if (!params.customized_split) { + // process tensors one by one + size_t curr_tensors_size = 0; // current size by counting only tensors size (without metadata) + for (int i = 0; i < n_tensors; ++i) { + struct ggml_tensor * t = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i)); + // calculate the "imaginary" size = the current size + next tensor size + size_t n_bytes = GGML_PAD(ggml_nbytes(t), GGUF_DEFAULT_ALIGNMENT); + size_t next_tensors_size = curr_tensors_size + n_bytes; + if (should_split(i, next_tensors_size)) { + new_ctx_out(false); + curr_tensors_size = n_bytes; + } else { + curr_tensors_size = next_tensors_size; + } + gguf_add_tensor(ctx_out, t); + } + } else { + // custom split based on tensor set + std::vector tensor_set = read_customized_tensors(params.tensor_set_file); + if(tensor_set.empty()) { + fprintf(stderr, "error: tensor set is empty\n"); + exit(EXIT_FAILURE); + } + for (int i = 0; i < n_tensors; ++i) { + const char * t_name = gguf_get_tensor_name(ctx_gguf, i); + if (is_tensor_in_customized_set(t_name, tensor_set)) { + struct ggml_tensor * t = ggml_get_tensor(ctx_meta, t_name); + gguf_add_tensor(ctx_out, t); + } + } + new_ctx_out(false); + // add left tensors to the next split + for (int i = 0; i < n_tensors; ++i) { + const char * t_name = gguf_get_tensor_name(ctx_gguf, i); + if (!is_tensor_in_customized_set(t_name, tensor_set)) { + struct ggml_tensor * t = ggml_get_tensor(ctx_meta, t_name); + gguf_add_tensor(ctx_out, t); + } } - gguf_add_tensor(ctx_out, t); } // push the last ctx_out @@ -274,6 +327,16 @@ struct split_strategy { } } + bool is_tensor_in_customized_set(const char * t_name, std::vector tensor_set) { + for (auto & s : tensor_set) { + if (strstr(t_name, s.c_str()) != NULL) { + return true; + } + } + + return false; + } + void print_info() { printf("n_split: %ld\n", ctx_outs.size()); int i_split = 0; diff --git a/examples/multi-adaptation/CMakeLists.txt b/examples/multi-adaptation/CMakeLists.txt new file mode 100644 index 0000000000000..deca866ea0257 --- /dev/null +++ b/examples/multi-adaptation/CMakeLists.txt @@ -0,0 +1,5 @@ +set(TARGET llama_multi-adaptation) +add_executable(${TARGET} multi-adaptation.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_11) diff --git a/examples/multi-adaptation/README.md b/examples/multi-adaptation/README.md new file mode 100644 index 0000000000000..acd25a5f3ea5b --- /dev/null +++ b/examples/multi-adaptation/README.md @@ -0,0 +1,33 @@ +# Server Multi Adaptations for Different Scenarios + +## Goal +Service multiple scenarios on memory-constrained devices. The GGUF models are stored in the same folder. + +## Usage +Use the `-mpa` parameter to pass the alias and model path. + +### Flag to Switch Derived Model +```c +llama_ctx_switch_derived_model(ctx, "summarize"); +``` + +### Pass Model Path and Alias for Derived Models +```sh +llama_multi-adaptation.exe -m models\Phi-3-mini-4k-instruct-adaptor-base.gguf \ + -mpa code_writer=models\Phi-3-mini-4k-instruct-adaptor-code_writer.gguf \ + -mpa summarize=models\Phi-3-mini-4k-instruct-adaptor-summarization.gguf +``` + +## Foundation Model +The **foundation** GGUF contains the weights shared across models. +The **adaptor** GGUF contains the task-specific weights. + +Here are the combinations for hosting three models: +- `model-adaptor-base.gguf (0.77GB) + model-foundation.gguf (1.56GB)` +- `model-adaptor-taskA.gguf + model-foundation.gguf` +- `model-adaptor-taskB.gguf + model-foundation.gguf` + +The benefit is that it supports hosting multiple scenarios while keeping only one copy of the shared weights in memory. With the benefit of `mmap`, the task-specific GGUF is only loaded when the corresponding task is called. + +## Example +Use the GGUF splits in this model repository: [Phi-3-mini-4k-instruct_multi-adaptor_gguf](https://huggingface.co/zhhan/Phi-3-mini-4k-instruct_multi-adaptor_gguf) diff --git a/examples/multi-adaptation/multi-adaptation.cpp b/examples/multi-adaptation/multi-adaptation.cpp new file mode 100644 index 0000000000000..0dd31a6023bc3 --- /dev/null +++ b/examples/multi-adaptation/multi-adaptation.cpp @@ -0,0 +1,927 @@ +#include "common.h" + +#include "console.h" +#include "llama.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) +#include +#include +#elif defined (_WIN32) +#define WIN32_LEAN_AND_MEAN +#ifndef NOMINMAX +#define NOMINMAX +#endif +#include +#include +#endif + +#if defined(_MSC_VER) +#pragma warning(disable: 4244 4267) // possible loss of data +#endif + +static llama_context ** g_ctx; +static llama_model ** g_model; +static gpt_params * g_params; +static std::vector * g_input_tokens; +static std::ostringstream * g_output_ss; +static std::vector * g_output_tokens; +static bool is_interacting = false; + +static bool file_exists(const std::string & path) { + std::ifstream f(path.c_str()); + return f.good(); +} + +static bool file_is_empty(const std::string & path) { + std::ifstream f; + f.exceptions(std::ifstream::failbit | std::ifstream::badbit); + f.open(path.c_str(), std::ios::in | std::ios::binary | std::ios::ate); + return f.tellg() == 0; +} + +static void write_logfile( + const llama_context * ctx, const gpt_params & params, const llama_model * model, + const std::vector & input_tokens, const std::string & output, + const std::vector & output_tokens +) { + if (params.logdir.empty()) { + return; + } + + const std::string timestamp = string_get_sortable_timestamp(); + + const bool success = fs_create_directory_with_parents(params.logdir); + if (!success) { + fprintf(stderr, "%s: warning: failed to create logdir %s, cannot write logfile\n", + __func__, params.logdir.c_str()); + return; + } + + const std::string logfile_path = params.logdir + timestamp + ".yml"; + FILE * logfile = fopen(logfile_path.c_str(), "w"); + + if (logfile == NULL) { + fprintf(stderr, "%s: failed to open logfile %s\n", __func__, logfile_path.c_str()); + return; + } + + fprintf(logfile, "binary: main\n"); + char model_desc[128]; + llama_model_desc(model, model_desc, sizeof(model_desc)); + yaml_dump_non_result_info(logfile, params, ctx, timestamp, input_tokens, model_desc); + + fprintf(logfile, "\n"); + fprintf(logfile, "######################\n"); + fprintf(logfile, "# Generation Results #\n"); + fprintf(logfile, "######################\n"); + fprintf(logfile, "\n"); + + yaml_dump_string_multiline(logfile, "output", output.c_str()); + yaml_dump_vector_int(logfile, "output_tokens", output_tokens); + + llama_dump_timing_info_yaml(logfile, ctx); + fclose(logfile); +} + +#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32) +static void sigint_handler(int signo) { + if (signo == SIGINT) { + if (!is_interacting && g_params->interactive) { + is_interacting = true; + } else { + console::cleanup(); + printf("\n"); + llama_print_timings(*g_ctx); + write_logfile(*g_ctx, *g_params, *g_model, *g_input_tokens, g_output_ss->str(), *g_output_tokens); + _exit(130); + } + } +} +#endif + +static void llama_log_callback_logTee(ggml_log_level level, const char * text, void * user_data) { + (void) level; + (void) user_data; + LOG_TEE("%s", text); +} + +static std::string chat_add_and_format(struct llama_model * model, std::vector & chat_msgs, std::string role, std::string content) { + llama_chat_msg new_msg{role, content}; + auto formatted = llama_chat_format_single( + model, g_params->chat_template, chat_msgs, new_msg, role == "user"); + chat_msgs.push_back({role, content}); + return formatted; +} + +int main(int argc, char ** argv) { + gpt_params params; + g_params = ¶ms; + + if (!gpt_params_parse(argc, argv, params)) { + gpt_params_print_usage(argc, argv, params); + return 1; + } + + llama_sampling_params & sparams = params.sparams; + +#ifndef LOG_DISABLE_LOGS + log_set_target(log_filename_generator("main", "log")); + LOG_TEE("Log start\n"); + log_dump_cmdline(argc, argv); + llama_log_set(llama_log_callback_logTee, nullptr); +#endif // LOG_DISABLE_LOGS + + // TODO: Dump params ? + //LOG("Params perplexity: %s\n", LOG_TOSTR(params.perplexity)); + + // save choice to use color for later + // (note for later: this is a slightly awkward choice) + console::init(params.simple_io, params.use_color); + atexit([]() { console::cleanup(); }); + + if (params.logits_all) { + printf("\n************\n"); + printf("%s: please use the 'perplexity' tool for perplexity calculations\n", __func__); + printf("************\n\n"); + + return 0; + } + + if (params.embedding) { + printf("\n************\n"); + printf("%s: please use the 'embedding' tool for embedding calculations\n", __func__); + printf("************\n\n"); + + return 0; + } + + if (params.n_ctx != 0 && params.n_ctx < 8) { + LOG_TEE("%s: warning: minimum context size is 8, using minimum size.\n", __func__); + params.n_ctx = 8; + } + + if (params.rope_freq_base != 0.0) { + LOG_TEE("%s: warning: changing RoPE frequency base to %g.\n", __func__, params.rope_freq_base); + } + + if (params.rope_freq_scale != 0.0) { + LOG_TEE("%s: warning: scaling RoPE frequency by %g.\n", __func__, params.rope_freq_scale); + } + + LOG_TEE("%s: build = %d (%s)\n", __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT); + LOG_TEE("%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET); + + if (params.seed == LLAMA_DEFAULT_SEED) { + params.seed = time(NULL); + } + + LOG_TEE("%s: seed = %u\n", __func__, params.seed); + + std::mt19937 rng(params.seed); + + LOG("%s: llama backend init\n", __func__); + llama_backend_init(); + llama_numa_init(params.numa); + + llama_model * model; + llama_context * ctx; + llama_context * ctx_guidance = NULL; + std::vector chat_msgs; + g_model = &model; + g_ctx = &ctx; + + // load the model and apply lora adapter, if any + LOG("%s: load the model and apply lora adapter, if any\n", __func__); + std::tie(model, ctx) = llama_init_from_gpt_params(params); + if (sparams.cfg_scale > 1.f) { + struct llama_context_params lparams = llama_context_params_from_gpt_params(params); + ctx_guidance = llama_new_context_with_model(model, lparams); + } + + if (model == NULL) { + LOG_TEE("%s: error: unable to load model\n", __func__); + return 1; + } + + const int n_ctx_train = llama_n_ctx_train(model); + const int n_ctx = llama_n_ctx(ctx); + LOG("n_ctx: %d\n", n_ctx); + + if (n_ctx > n_ctx_train) { + LOG_TEE("%s: warning: model was trained on only %d context tokens (%d specified)\n", + __func__, n_ctx_train, n_ctx); + } + + LOG_TEE("%s: chat template example: %s\n", __func__, llama_chat_format_example(model, params.chat_template).c_str()); + + // print system information + { + LOG_TEE("\n"); + LOG_TEE("%s\n", gpt_params_get_system_info(params).c_str()); + } + + { + LOG_TEE("\n"); + llama_print_derived_models(ctx); + } + llama_ctx_switch_derived_model(ctx, "summarize"); + + std::string path_session = params.path_prompt_cache; + std::vector session_tokens; + + if (!path_session.empty()) { + LOG_TEE("%s: attempting to load saved session from '%s'\n", __func__, path_session.c_str()); + if (!file_exists(path_session)) { + LOG_TEE("%s: session file does not exist, will create.\n", __func__); + } else if (file_is_empty(path_session)) { + LOG_TEE("%s: The session file is empty. A new session will be initialized.\n", __func__); + } else { + // The file exists and is not empty + session_tokens.resize(n_ctx); + size_t n_token_count_out = 0; + if (!llama_state_load_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.capacity(), &n_token_count_out)) { + LOG_TEE("%s: error: failed to load session file '%s'\n", __func__, path_session.c_str()); + return 1; + } + session_tokens.resize(n_token_count_out); + LOG_TEE("%s: loaded a session with prompt size of %d tokens\n", __func__, (int)session_tokens.size()); + } + } + + const bool add_bos = llama_should_add_bos_token(model); + GGML_ASSERT(llama_add_eos_token(model) != 1); + LOG("add_bos: %d\n", add_bos); + + std::vector embd_inp; + + params.prompt = "<|user|>\nhelp summarize the microsoft products.<|end|>\n<|assistant|>\n"; + { + auto prompt = (params.conversation && params.enable_chat_template) + ? chat_add_and_format(model, chat_msgs, "system", params.prompt) // format the system prompt in conversation mode + : params.prompt; + if (params.interactive_first || !params.prompt.empty() || session_tokens.empty()) { + LOG("tokenize the prompt\n"); + embd_inp = ::llama_tokenize(ctx, prompt, true, true); + } else { + LOG("use session tokens\n"); + embd_inp = session_tokens; + } + + LOG("prompt: \"%s\"\n", log_tostr(prompt)); + LOG("tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str()); + } + + // Should not run without any tokens + if (embd_inp.empty()) { + embd_inp.push_back(llama_token_bos(model)); + LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str()); + } + + // Tokenize negative prompt + std::vector guidance_inp; + int guidance_offset = 0; + int original_prompt_len = 0; + + if ((int) embd_inp.size() > n_ctx - 4) { + LOG_TEE("%s: error: prompt is too long (%d tokens, max %d)\n", __func__, (int) embd_inp.size(), n_ctx - 4); + return 1; + } + + // debug message about similarity of saved session, if applicable + size_t n_matching_session_tokens = 0; + if (!session_tokens.empty()) { + for (llama_token id : session_tokens) { + if (n_matching_session_tokens >= embd_inp.size() || id != embd_inp[n_matching_session_tokens]) { + break; + } + n_matching_session_tokens++; + } + if (params.prompt.empty() && n_matching_session_tokens == embd_inp.size()) { + LOG_TEE("%s: using full prompt from session file\n", __func__); + } else if (n_matching_session_tokens >= embd_inp.size()) { + LOG_TEE("%s: session file has exact match for prompt!\n", __func__); + } else if (n_matching_session_tokens < (embd_inp.size() / 2)) { + LOG_TEE("%s: warning: session file has low similarity to prompt (%zu / %zu tokens); will mostly be reevaluated\n", + __func__, n_matching_session_tokens, embd_inp.size()); + } else { + LOG_TEE("%s: session file matches %zu / %zu tokens of prompt\n", + __func__, n_matching_session_tokens, embd_inp.size()); + } + + // remove any "future" tokens that we might have inherited from the previous session + llama_kv_cache_seq_rm(ctx, -1, n_matching_session_tokens, -1); + } + + LOGLN( + "recalculate the cached logits (check): embd_inp.empty() %s, n_matching_session_tokens %zu, embd_inp.size() %zu, session_tokens.size() %zu, embd_inp.size() %zu", + log_tostr(embd_inp.empty()), n_matching_session_tokens, embd_inp.size(), session_tokens.size(), embd_inp.size()); + + // if we will use the cache for the full prompt without reaching the end of the cache, force + // reevaluation of the last token to recalculate the cached logits + if (!embd_inp.empty() && n_matching_session_tokens == embd_inp.size() && session_tokens.size() > embd_inp.size()) { + LOGLN("recalculate the cached logits (do): session_tokens.resize( %zu )", embd_inp.size() - 1); + + session_tokens.resize(embd_inp.size() - 1); + } + + // number of tokens to keep when resetting context + if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size()) { + params.n_keep = (int)embd_inp.size(); + } else { + params.n_keep += add_bos; // always keep the BOS token + } + + if (params.conversation) { + params.interactive_first = true; + } + + // enable interactive mode if interactive start is specified + if (params.interactive_first) { + params.interactive = true; + } + + if (params.verbose_prompt) { + LOG_TEE("\n"); + LOG_TEE("%s: prompt: '%s'\n", __func__, params.prompt.c_str()); + LOG_TEE("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size()); + for (int i = 0; i < (int) embd_inp.size(); i++) { + LOG_TEE("%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str()); + } + + if (params.n_keep > add_bos) { + LOG_TEE("%s: static prompt based on n_keep: '", __func__); + for (int i = 0; i < params.n_keep; i++) { + LOG_TEE("%s", llama_token_to_piece(ctx, embd_inp[i]).c_str()); + } + LOG_TEE("'\n"); + } + LOG_TEE("\n"); + } + + // ctrl+C handling + { +#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) + struct sigaction sigint_action; + sigint_action.sa_handler = sigint_handler; + sigemptyset (&sigint_action.sa_mask); + sigint_action.sa_flags = 0; + sigaction(SIGINT, &sigint_action, NULL); +#elif defined (_WIN32) + auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL { + return (ctrl_type == CTRL_C_EVENT) ? (sigint_handler(SIGINT), true) : false; + }; + SetConsoleCtrlHandler(reinterpret_cast(console_ctrl_handler), true); +#endif + } + + if (params.interactive) { + LOG_TEE("%s: interactive mode on.\n", __func__); + + if (!params.antiprompt.empty()) { + for (const auto & antiprompt : params.antiprompt) { + LOG_TEE("Reverse prompt: '%s'\n", antiprompt.c_str()); + if (params.verbose_prompt) { + auto tmp = ::llama_tokenize(ctx, antiprompt, false, true); + for (int i = 0; i < (int) tmp.size(); i++) { + LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str()); + } + } + } + } + + if (params.input_prefix_bos) { + LOG_TEE("Input prefix with BOS\n"); + } + + if (!params.input_prefix.empty()) { + LOG_TEE("Input prefix: '%s'\n", params.input_prefix.c_str()); + if (params.verbose_prompt) { + auto tmp = ::llama_tokenize(ctx, params.input_prefix, true, true); + for (int i = 0; i < (int) tmp.size(); i++) { + LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str()); + } + } + } + + if (!params.input_suffix.empty()) { + LOG_TEE("Input suffix: '%s'\n", params.input_suffix.c_str()); + if (params.verbose_prompt) { + auto tmp = ::llama_tokenize(ctx, params.input_suffix, false, true); + for (int i = 0; i < (int) tmp.size(); i++) { + LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str()); + } + } + } + } + LOG_TEE("sampling: \n%s\n", llama_sampling_print(sparams).c_str()); + LOG_TEE("sampling order: \n%s\n", llama_sampling_order_print(sparams).c_str()); + LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep); + + // group-attention state + // number of grouped KV tokens so far (used only if params.grp_attn_n > 1) + int ga_i = 0; + + const int ga_n = params.grp_attn_n; + const int ga_w = params.grp_attn_w; + + if (params.interactive) { + const char * control_message; + if (params.multiline_input) { + control_message = " - To return control to the AI, end your input with '\\'.\n" + " - To return control without starting a new line, end your input with '/'.\n"; + } else { + control_message = " - Press Return to return control to the AI.\n" + " - To return control without starting a new line, end your input with '/'.\n" + " - If you want to submit another line, end your input with '\\'.\n"; + } + LOG_TEE("== Running in interactive mode. ==\n"); +#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32) + LOG_TEE( " - Press Ctrl+C to interject at any time.\n"); +#endif + LOG_TEE( "%s\n", control_message); + + is_interacting = params.interactive_first; + } + + bool is_antiprompt = false; + bool input_echo = true; + bool display = true; + bool need_to_save_session = !path_session.empty() && n_matching_session_tokens < embd_inp.size(); + + int n_past = 0; + int n_remain = params.n_predict; + int n_consumed = 0; + int n_session_consumed = 0; + int n_past_guidance = 0; + + std::vector input_tokens; g_input_tokens = &input_tokens; + std::vector output_tokens; g_output_tokens = &output_tokens; + std::ostringstream output_ss; g_output_ss = &output_ss; + std::ostringstream assistant_ss; // for storing current assistant message, used in conversation mode + + // the first thing we will do is to output the prompt, so set color accordingly + console::set_display(console::prompt); + display = params.display_prompt; + + std::vector embd; + std::vector embd_guidance; + + // tokenized antiprompts + std::vector> antiprompt_ids; + + antiprompt_ids.reserve(params.antiprompt.size()); + for (const std::string & antiprompt : params.antiprompt) { + antiprompt_ids.emplace_back(::llama_tokenize(ctx, antiprompt, false, true)); + } + + struct llama_sampling_context * ctx_sampling = llama_sampling_init(sparams); + if (!ctx_sampling) { + fprintf(stderr, "%s: failed to initialize sampling subsystem\n", __func__); + exit(1); + } + + while ((n_remain != 0 && !is_antiprompt) || params.interactive) { + // predict + if (!embd.empty()) { + // Note: (n_ctx - 4) here is to match the logic for commandline prompt handling via + // --prompt or --file which uses the same value. + int max_embd_size = n_ctx - 4; + + // Ensure the input doesn't exceed the context size by truncating embd if necessary. + if ((int) embd.size() > max_embd_size) { + const int skipped_tokens = (int) embd.size() - max_embd_size; + embd.resize(max_embd_size); + + console::set_display(console::error); + printf("<>", skipped_tokens, skipped_tokens != 1 ? "s" : ""); + console::set_display(console::reset); + fflush(stdout); + } + + if (ga_n == 1) { + // infinite text generation via context shifting + // if we run out of context: + // - take the n_keep first tokens from the original prompt (via n_past) + // - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches + if (n_past + (int) embd.size() + std::max(0, guidance_offset) >= n_ctx) { + if (params.n_predict == -2) { + LOG_TEE("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict); + break; + } + + const int n_left = n_past - params.n_keep; + const int n_discard = n_left/2; + + LOG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n", + n_past, n_left, n_ctx, params.n_keep, n_discard); + + llama_kv_cache_seq_rm (ctx, 0, params.n_keep , params.n_keep + n_discard); + llama_kv_cache_seq_add(ctx, 0, params.n_keep + n_discard, n_past, -n_discard); + + n_past -= n_discard; + + if (ctx_guidance) { + n_past_guidance -= n_discard; + } + + LOG("after swap: n_past = %d, n_past_guidance = %d\n", n_past, n_past_guidance); + + LOG("embd: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd).c_str()); + + LOG("clear session path\n"); + path_session.clear(); + } + } else { + // context extension via Self-Extend + while (n_past >= ga_i + ga_w) { + const int ib = (ga_n*ga_i)/ga_w; + const int bd = (ga_w/ga_n)*(ga_n - 1); + const int dd = (ga_w/ga_n) - ib*bd - ga_w; + + LOG("\n"); + LOG("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", ga_i, n_past, ib*bd, ga_i + ib*bd, n_past + ib*bd); + LOG("div: [%6d, %6d] / %6d -> [%6d, %6d]\n", ga_i + ib*bd, ga_i + ib*bd + ga_w, ga_n, (ga_i + ib*bd)/ga_n, (ga_i + ib*bd + ga_w)/ga_n); + LOG("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", ga_i + ib*bd + ga_w, n_past + ib*bd, dd, ga_i + ib*bd + ga_w + dd, n_past + ib*bd + dd); + + llama_kv_cache_seq_add(ctx, 0, ga_i, n_past, ib*bd); + llama_kv_cache_seq_div(ctx, 0, ga_i + ib*bd, ga_i + ib*bd + ga_w, ga_n); + llama_kv_cache_seq_add(ctx, 0, ga_i + ib*bd + ga_w, n_past + ib*bd, dd); + + n_past -= bd; + + ga_i += ga_w/ga_n; + + LOG("\nn_past_old = %d, n_past = %d, ga_i = %d\n\n", n_past + bd, n_past, ga_i); + } + } + + // try to reuse a matching prefix from the loaded session instead of re-eval (via n_past) + if (n_session_consumed < (int) session_tokens.size()) { + size_t i = 0; + for ( ; i < embd.size(); i++) { + if (embd[i] != session_tokens[n_session_consumed]) { + session_tokens.resize(n_session_consumed); + break; + } + + n_past++; + n_session_consumed++; + + if (n_session_consumed >= (int) session_tokens.size()) { + ++i; + break; + } + } + if (i > 0) { + embd.erase(embd.begin(), embd.begin() + i); + } + } + + // evaluate tokens in batches + // embd is typically prepared beforehand to fit within a batch, but not always + if (ctx_guidance) { + int input_size = 0; + llama_token * input_buf = NULL; + + if (n_past_guidance < (int) guidance_inp.size()) { + // Guidance context should have the same data with these modifications: + // + // * Replace the initial prompt + // * Shift everything by guidance_offset + embd_guidance = guidance_inp; + if (embd.begin() + original_prompt_len < embd.end()) { + embd_guidance.insert( + embd_guidance.end(), + embd.begin() + original_prompt_len, + embd.end() + ); + } + + input_buf = embd_guidance.data(); + input_size = embd_guidance.size(); + + LOG("guidance context: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_guidance).c_str()); + } else { + input_buf = embd.data(); + input_size = embd.size(); + } + + for (int i = 0; i < input_size; i += params.n_batch) { + int n_eval = std::min(input_size - i, params.n_batch); + if (llama_decode(ctx_guidance, llama_batch_get_one(input_buf + i, n_eval, n_past_guidance, 0))) { + LOG_TEE("%s : failed to eval\n", __func__); + return 1; + } + + n_past_guidance += n_eval; + } + } + + for (int i = 0; i < (int) embd.size(); i += params.n_batch) { + int n_eval = (int) embd.size() - i; + if (n_eval > params.n_batch) { + n_eval = params.n_batch; + } + + LOG("eval: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd).c_str()); + + if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval, n_past, 0))) { + LOG_TEE("%s : failed to eval\n", __func__); + return 1; + } + + n_past += n_eval; + + LOG("n_past = %d\n", n_past); + // Display total tokens alongside total time + if (params.n_print > 0 && n_past % params.n_print == 0) { + LOG_TEE("\n\033[31mTokens consumed so far = %d / %d \033[0m\n", n_past, n_ctx); + } + } + + if (!embd.empty() && !path_session.empty()) { + session_tokens.insert(session_tokens.end(), embd.begin(), embd.end()); + n_session_consumed = session_tokens.size(); + } + } + + embd.clear(); + embd_guidance.clear(); + + if ((int) embd_inp.size() <= n_consumed && !is_interacting) { + // optionally save the session on first sample (for faster prompt loading next time) + if (!path_session.empty() && need_to_save_session && !params.prompt_cache_ro) { + need_to_save_session = false; + llama_state_save_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size()); + + LOG("saved session to %s\n", path_session.c_str()); + } + + const llama_token id = llama_sampling_sample(ctx_sampling, ctx, ctx_guidance); + + llama_sampling_accept(ctx_sampling, ctx, id, /* apply_grammar= */ true); + + LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, ctx_sampling->prev).c_str()); + + embd.push_back(id); + + // echo this to console + input_echo = true; + + // decrement remaining sampling budget + --n_remain; + + LOG("n_remain: %d\n", n_remain); + } else { + // some user input remains from prompt or interaction, forward it to processing + LOG("embd_inp.size(): %d, n_consumed: %d\n", (int) embd_inp.size(), n_consumed); + while ((int) embd_inp.size() > n_consumed) { + embd.push_back(embd_inp[n_consumed]); + + // push the prompt in the sampling context in order to apply repetition penalties later + // for the prompt, we don't apply grammar rules + llama_sampling_accept(ctx_sampling, ctx, embd_inp[n_consumed], /* apply_grammar= */ false); + + ++n_consumed; + if ((int) embd.size() >= params.n_batch) { + break; + } + } + } + + // display text + if (input_echo && display) { + for (auto id : embd) { + const std::string token_str = llama_token_to_piece(ctx, id, params.special); + + // Console/Stream Output + fprintf(stdout, "%s", token_str.c_str()); + + // Record Displayed Tokens To Log + // Note: Generated tokens are created one by one hence this check + if (embd.size() > 1) { + // Incoming Requested Tokens + input_tokens.push_back(id); + } else { + // Outgoing Generated Tokens + output_tokens.push_back(id); + output_ss << token_str; + } + + fflush(stdout); + } + } + + // reset color to default if there is no pending user input + if (input_echo && (int) embd_inp.size() == n_consumed) { + console::set_display(console::reset); + display = true; + } + + // if not currently processing queued inputs; + if ((int) embd_inp.size() <= n_consumed) { + // check for reverse prompt in the last n_prev tokens + if (!params.antiprompt.empty()) { + const int n_prev = 32; + const std::string last_output = llama_sampling_prev_str(ctx_sampling, ctx, n_prev); + + is_antiprompt = false; + // Check if each of the reverse prompts appears at the end of the output. + // If we're not running interactively, the reverse prompt might be tokenized with some following characters + // so we'll compensate for that by widening the search window a bit. + for (std::string & antiprompt : params.antiprompt) { + size_t extra_padding = params.interactive ? 0 : 2; + size_t search_start_pos = last_output.length() > static_cast(antiprompt.length() + extra_padding) + ? last_output.length() - static_cast(antiprompt.length() + extra_padding) + : 0; + + if (last_output.find(antiprompt, search_start_pos) != std::string::npos) { + if (params.interactive) { + is_interacting = true; + } + is_antiprompt = true; + break; + } + } + + // check for reverse prompt using special tokens + llama_token last_token = llama_sampling_last(ctx_sampling); + for (std::vector ids : antiprompt_ids) { + if (ids.size() == 1 && last_token == ids[0]) { + if (params.interactive) { + is_interacting = true; + } + is_antiprompt = true; + break; + } + } + + if (is_antiprompt) { + LOG("found antiprompt: %s\n", last_output.c_str()); + } + } + + // deal with end of generation tokens in interactive mode + if (llama_token_is_eog(model, llama_sampling_last(ctx_sampling))) { + LOG("found an EOG token\n"); + + if (params.interactive) { + if (!params.antiprompt.empty()) { + // tokenize and inject first reverse prompt + const auto first_antiprompt = ::llama_tokenize(ctx, params.antiprompt.front(), false, true); + embd_inp.insert(embd_inp.end(), first_antiprompt.begin(), first_antiprompt.end()); + is_antiprompt = true; + } + + if (params.enable_chat_template) { + chat_add_and_format(model, chat_msgs, "assistant", assistant_ss.str()); + } + is_interacting = true; + printf("\n"); + } + } + + // if current token is not EOG, we add it to current assistant message + if (params.conversation) { + auto id = llama_sampling_last(ctx_sampling); + assistant_ss << llama_token_to_piece(ctx, id, false); + } + + if (n_past > 0 && is_interacting) { + LOG("waiting for user input\n"); + + if (params.conversation) { + printf("\n> "); + } + + if (params.input_prefix_bos) { + LOG("adding input prefix BOS token\n"); + embd_inp.push_back(llama_token_bos(model)); + } + + std::string buffer; + if (!params.input_prefix.empty() && !params.conversation) { + LOG("appending input prefix: '%s'\n", params.input_prefix.c_str()); + printf("%s", params.input_prefix.c_str()); + } + + // color user input only + console::set_display(console::user_input); + display = params.display_prompt; + + std::string line; + bool another_line = true; + do { + another_line = console::readline(line, params.multiline_input); + buffer += line; + } while (another_line); + + // done taking input, reset color + console::set_display(console::reset); + display = true; + + // Add tokens to embd only if the input buffer is non-empty + // Entering a empty line lets the user pass control back + if (buffer.length() > 1) { + // append input suffix if any + if (!params.input_suffix.empty() && !params.conversation) { + LOG("appending input suffix: '%s'\n", params.input_suffix.c_str()); + printf("%s", params.input_suffix.c_str()); + } + + LOG("buffer: '%s'\n", buffer.c_str()); + + const size_t original_size = embd_inp.size(); + + if (params.escape) { + string_process_escapes(buffer); + } + + bool format_chat = params.conversation && params.enable_chat_template; + std::string user_inp = format_chat + ? chat_add_and_format(model, chat_msgs, "user", std::move(buffer)) + : std::move(buffer); + // TODO: one inconvenient of current chat template implementation is that we can't distinguish between user input and special tokens (prefix/postfix) + const auto line_pfx = ::llama_tokenize(ctx, params.input_prefix, false, true); + const auto line_inp = ::llama_tokenize(ctx, user_inp, false, format_chat); + const auto line_sfx = ::llama_tokenize(ctx, params.input_suffix, false, true); + + LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp).c_str()); + + embd_inp.insert(embd_inp.end(), line_pfx.begin(), line_pfx.end()); + embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end()); + embd_inp.insert(embd_inp.end(), line_sfx.begin(), line_sfx.end()); + + for (size_t i = original_size; i < embd_inp.size(); ++i) { + const llama_token token = embd_inp[i]; + output_tokens.push_back(token); + output_ss << llama_token_to_piece(ctx, token); + } + + // reset assistant message + assistant_ss.str(""); + + n_remain -= line_inp.size(); + LOG("n_remain: %d\n", n_remain); + } else { + LOG("empty line, passing control back\n"); + } + + input_echo = false; // do not echo this again + } + + if (n_past > 0) { + if (is_interacting) { + llama_sampling_reset(ctx_sampling); + } + is_interacting = false; + } + } + + // end of generation + if (!embd.empty() && llama_token_is_eog(model, embd.back()) && !(params.interactive)) { + LOG_TEE(" [end of text]\n"); + break; + } + + // In interactive mode, respect the maximum number of tokens and drop back to user input when reached. + // We skip this logic when n_predict == -1 (infinite) or -2 (stop at context size). + if (params.interactive && n_remain <= 0 && params.n_predict >= 0) { + n_remain = params.n_predict; + is_interacting = true; + } + } + + if (!path_session.empty() && params.prompt_cache_all && !params.prompt_cache_ro) { + LOG_TEE("\n%s: saving final output to session file '%s'\n", __func__, path_session.c_str()); + llama_state_save_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size()); + } + + llama_print_timings(ctx); + write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens); + + if (ctx_guidance) { llama_free(ctx_guidance); } + llama_free(ctx); + llama_free_model(model); + + llama_sampling_free(ctx_sampling); + llama_backend_free(); + +#ifndef LOG_DISABLE_LOGS + LOG_TEE("Log end\n"); +#endif // LOG_DISABLE_LOGS + + return 0; +} diff --git a/include/llama.h b/include/llama.h index c5b6182920428..a4e40bf23689f 100644 --- a/include/llama.h +++ b/include/llama.h @@ -425,6 +425,20 @@ extern "C" { struct llama_model * model, struct llama_context_params params); + static const char* BASE_MODEL = "base"; + + LLAMA_API void llama_print_derived_models(const struct llama_context* ctx); + + LLAMA_API void llama_model_set_name(struct llama_model * model, const char* name); + + LLAMA_API void llama_ctx_set_derived_model( + struct llama_context * ctx, + struct llama_model * derived_model); + + LLAMA_API bool llama_ctx_switch_derived_model( + struct llama_context* ctx, + const char * derived_model_name); + // Frees all allocated memory LLAMA_API void llama_free(struct llama_context * ctx); @@ -1087,6 +1101,11 @@ extern "C" { // Returns the split_prefix length. LLAMA_API int llama_split_prefix(char * split_prefix, size_t maxlen, const char * split_path, int split_no, int split_count); + LLAMA_API int llama_foundation_split_path(char* split_path, size_t maxlen, const char* path_prefix); + + LLAMA_API int llama_foundation_prefix(char* split_path, size_t maxlen, const char* path_prefix); + + // Performance information LLAMA_API struct llama_timings llama_get_timings(struct llama_context * ctx); diff --git a/src/llama.cpp b/src/llama.cpp index 73f52435a503e..d475f48f8ebbb 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -2248,6 +2248,8 @@ struct llama_cparams { bool offload_kqv; bool flash_attn; + std::string derived_model_name = BASE_MODEL; + enum llama_pooling_type pooling_type; ggml_backend_sched_eval_callback cb_eval; @@ -2621,6 +2623,9 @@ struct llama_context { const llama_model & model; + // derived models + std::vector derived_models; + // key + value cache for the self attention struct llama_kv_cache kv_self; @@ -3543,8 +3548,18 @@ struct llama_model_loader { } char split_prefix[PATH_MAX] = {0}; + char foundation_prefix[PATH_MAX] = { 0 }; + + // // model-foundation.gguf, model-adaptor-task-x.gguf, model-adaptor-task-y.gguf + bool foundation_mode = false; + if (!llama_split_prefix(split_prefix, sizeof(split_prefix), fname.c_str(), idx, n_split)) { - throw std::runtime_error(format("invalid split file: %s", fname.c_str())); + if (llama_foundation_prefix(foundation_prefix, sizeof(foundation_prefix), fname.c_str()) && n_split == 2) { + foundation_mode = true; + } + else { + throw std::runtime_error(format("invalid split file: %s", fname.c_str())); + } } if (trace > 0) { @@ -3553,7 +3568,11 @@ struct llama_model_loader { char split_path[PATH_MAX] = {0}; for (idx = 1; idx < n_split; idx++) { - llama_split_path(split_path, sizeof(split_path), split_prefix, idx, n_split); + if (foundation_mode) { + llama_foundation_split_path(split_path, sizeof(split_path), foundation_prefix); + } else { + llama_split_path(split_path, sizeof(split_path), split_prefix, idx, n_split); + } struct gguf_init_params split_params = { /*.no_alloc = */ true, @@ -7804,10 +7823,11 @@ struct llm_build_context { // TODO: consider making the entire interface noexcept llm_build_context( llama_context & lctx, + const llama_model & model, const llama_batch & batch, const llm_build_cb & cb, bool worst_case) : - model (lctx.model), + model (model), lctx (lctx), hparams (model.hparams), cparams (lctx.cparams), @@ -12525,7 +12545,7 @@ static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { }; - struct llm_build_context llm(lctx, dummy, cb, false); + struct llm_build_context llm(lctx, lctx.model, dummy, cb, false); llm.init(); @@ -12542,7 +12562,7 @@ static struct ggml_cgraph * llama_build_graph_k_shift(llama_context & lctx) { llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { }; - struct llm_build_context llm(lctx, dummy, cb, false); + struct llm_build_context llm(lctx, lctx.model, dummy, cb, false); llm.init(); @@ -12559,7 +12579,7 @@ static struct ggml_cgraph * llama_build_graph_s_copy(llama_context & lctx) { llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { }; - struct llm_build_context llm(lctx, dummy, cb, false); + struct llm_build_context llm(lctx, lctx.model, dummy, cb, false); llm.init(); @@ -12574,7 +12594,19 @@ static struct ggml_cgraph * llama_build_graph( llama_context & lctx, const llama_batch & batch, bool worst_case) { - const auto & model = lctx.model; + const auto& foundation_model = lctx.model; + const char* model_name = lctx.cparams.derived_model_name.c_str(); + const llama_model* model_ptr = nullptr; + + for (const auto& model : lctx.derived_models) { + if (model->name == model_name) { + model_ptr = model; + break; + } + } + + model_ptr = model_ptr ? model_ptr : &foundation_model; + const llama_model& model = *model_ptr; // this callback allows us to apply custom logic to each tensor (e.g. ggml-alloc, offloading, etc.) llm_build_cb cb = [&](struct ggml_tensor * cur, const char * name, int il) { @@ -12609,7 +12641,7 @@ static struct ggml_cgraph * llama_build_graph( struct ggml_cgraph * result = NULL; - struct llm_build_context llm(lctx, batch, cb, worst_case); + struct llm_build_context llm(lctx, model, batch, cb, worst_case); llm.init(); @@ -17963,6 +17995,44 @@ const llama_model * llama_get_model(const struct llama_context * ctx) { return &ctx->model; } +void llama_model_set_name(struct llama_model * model, const char * model_name) { + model->name = model_name; +} + +void llama_print_derived_models(const struct llama_context * ctx) { + for (const auto & derived_model : ctx->derived_models) { + if (!derived_model->name.empty()) { + LLAMA_LOG_INFO("%s: %s\n", __func__, derived_model->name.c_str()); + } + } +} + +void llama_ctx_set_derived_model(struct llama_context * ctx, struct llama_model * derived_model) { + ctx->derived_models.emplace_back(derived_model); +} + +bool llama_ctx_switch_derived_model(struct llama_context* ctx, const char * derived_model_name) { + llama_synchronize(ctx); + auto& cparams = ctx->cparams; + int found = 0; + + const llama_model* model_ptr = nullptr; + + bool is_derived = false; + for (const auto& model : ctx->derived_models) { + if (model->name == derived_model_name) { + model_ptr = model; + is_derived = true; + break; + } + } + + cparams.derived_model_name = is_derived ? derived_model_name : BASE_MODEL; + LLAMA_LOG_INFO("%s: %s\n", __func__, cparams.derived_model_name.c_str()); + + return true; +} + uint32_t llama_n_ctx(const struct llama_context * ctx) { return ctx->cparams.n_ctx; } @@ -20111,6 +20181,27 @@ LLAMA_API int32_t llama_chat_apply_template( return res; } +LLAMA_API int llama_foundation_split_path(char* split_path, size_t maxlen, const char* path_prefix) { + static const char* const SHARED_SPLIT_PATH_FORMAT = "%s-foundation.gguf"; + if (snprintf(split_path, maxlen, SHARED_SPLIT_PATH_FORMAT, path_prefix)) { + return strlen(split_path); + } + return 0; +} + +int llama_foundation_prefix(char* dest, size_t maxlen, const char* split_path) { + const char* keyword = "-adaptor-"; + const char* pos = strstr(split_path, keyword); + + if (pos != NULL) { + size_t size_prefix = pos - split_path; + snprintf(dest, std::min((size_t)size_prefix + 1, maxlen), "%s", split_path); + return size_prefix; + } + + return 0; +} + LLAMA_API int llama_split_path(char * split_path, size_t maxlen, const char * path_prefix, int split_no, int split_count) { static const char * const SPLIT_PATH_FORMAT = "%s-%05d-of-%05d.gguf"; if (snprintf(split_path, maxlen, SPLIT_PATH_FORMAT, path_prefix, split_no + 1, split_count)) {