mirror of
https://github.com/LostRuins/koboldcpp.git
synced 2025-09-11 01:24:36 +00:00
still refactoring, but need a checkpoint to prepare build for 1.0.7
This commit is contained in:
Concedo
parent
6b86f5ea22
commit
801b178f2a
11 changed files with 452 additions and 44 deletions
403
gptj_adapter.cpp
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403
gptj_adapter.cpp
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//This is Concedo's shitty adapter for adding python bindings for llama
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//Considerations:
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//Don't want to use pybind11 due to dependencies on MSVCC
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//ZERO or MINIMAL changes as possible to main.cpp - do not move their function declarations here!
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//Leave main.cpp UNTOUCHED, We want to be able to update the repo and pull any changes automatically.
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//No dynamic memory allocation! Setup structs with FIXED (known) shapes and sizes for ALL output fields
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//Python will ALWAYS provide the memory, we just write to it.
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#include <time.h>
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#include "model_adapter.h"
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#include "otherarch/otherarch.h"
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//return val: 0=fail, 1=(original ggml, alpaca), 2=(ggmf), 3=(ggjt)
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static FileFormat file_format = FileFormat::FAIL;
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static llama_context_params ctx_params;
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static gpt_params params;
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static int n_past = 0;
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static int n_threads = 4;
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static int n_batch = 8;
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static std::string model;
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static llama_context *ctx;
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static std::vector<llama_token> last_n_tokens;
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static std::vector<llama_token> current_context_tokens;
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void nnn()
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{
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ggml_time_init();
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const int64_t t_main_start_us = ggml_time_us();
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gpt_params params;
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params.model = "models/gpt-j-6B/ggml-model.bin";
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if (params.seed < 0) {
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params.seed = time(NULL);
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}
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printf("%s: seed = %d\n", __func__, params.seed);
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std::mt19937 rng(params.seed);
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int64_t t_load_us = 0;
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gpt_vocab vocab;
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gptj_model model;
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// load the model
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{
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const int64_t t_start_us = ggml_time_us();
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if (!legacy_gptj_model_load(params.model, model, vocab)) {
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fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
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return 1;
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}
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t_load_us = ggml_time_us() - t_start_us;
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}
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int n_past = 0;
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int64_t t_sample_us = 0;
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int64_t t_predict_us = 0;
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std::vector<float> logits;
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// tokenize the prompt
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std::vector<gpt_vocab::id> embd_inp = ::gpt_tokenize(vocab, params.prompt);
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params.n_predict = std::min(params.n_predict, model.hparams.n_ctx - (int) embd_inp.size());
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printf("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
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printf("\n");
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std::vector<gpt_vocab::id> embd;
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// determine the required inference memory per token:
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size_t mem_per_token = 0;
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legacy_gptj_eval(model, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token);
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for (int i = embd.size(); i < embd_inp.size() + params.n_predict; i++) {
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// predict
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if (embd.size() > 0) {
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const int64_t t_start_us = ggml_time_us();
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if (!legacy_gptj_eval(model, params.n_threads, n_past, embd, logits, mem_per_token)) {
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printf("Failed to predict\n");
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return 1;
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}
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t_predict_us += ggml_time_us() - t_start_us;
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}
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n_past += embd.size();
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embd.clear();
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if (i >= embd_inp.size()) {
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// sample next token
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const int top_k = params.top_k;
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const float top_p = params.top_p;
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const float temp = params.temp;
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const int n_vocab = model.hparams.n_vocab;
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gpt_vocab::id id = 0;
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{
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const int64_t t_start_sample_us = ggml_time_us();
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id = gpt_sample_top_k_top_p(vocab, logits.data() + (logits.size() - n_vocab), top_k, top_p, temp, rng);
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t_sample_us += ggml_time_us() - t_start_sample_us;
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}
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// add it to the context
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embd.push_back(id);
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} else {
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// if here, it means we are still processing the input prompt
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for (int k = i; k < embd_inp.size(); k++) {
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embd.push_back(embd_inp[k]);
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if (embd.size() > params.n_batch) {
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break;
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}
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}
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i += embd.size() - 1;
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}
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// display text
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for (auto id : embd) {
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printf("%s", vocab.id_to_token[id].c_str());
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}
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fflush(stdout);
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// end of text token
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if (embd.back() == 50256) {
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break;
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}
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}
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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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printf("\n\n");
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printf("%s: mem per token = %8zu bytes\n", __func__, mem_per_token);
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printf("%s: load time = %8.2f ms\n", __func__, t_load_us/1000.0f);
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printf("%s: sample time = %8.2f ms\n", __func__, t_sample_us/1000.0f);
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printf("%s: predict time = %8.2f ms / %.2f ms per token\n", __func__, t_predict_us/1000.0f, t_predict_us/1000.0f/n_past);
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printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);
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}
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ggml_free(model.ctx);
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return 0;
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}
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bool llama_load_model(const load_model_inputs inputs, FileFormat in_file_format)
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{
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printf("System Info: %s\n", llama_print_system_info());
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ctx_params = llama_context_default_params();
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n_threads = inputs.threads;
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n_batch = inputs.batch_size;
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model = inputs.model_filename;
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ctx_params.n_ctx = inputs.max_context_length;
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ctx_params.n_parts = inputs.n_parts_overwrite;
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ctx_params.seed = -1;
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ctx_params.f16_kv = inputs.f16_kv;
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ctx_params.logits_all = false;
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file_format = in_file_format;
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if (file_format == FileFormat::GGML || file_format == FileFormat::GGHF)
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{
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ctx = legacy_llama_init_from_file(model.c_str(), ctx_params);
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}
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else
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{
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ctx = llama_init_from_file(model.c_str(), ctx_params);
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}
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if (ctx == NULL)
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{
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fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, model.c_str());
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return false;
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}
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if (file_format < FileFormat::GGJT)
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{
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printf("\n---\nWarning: Your model has an INVALID or OUTDATED format (ver %d). Please reconvert it for better results!\n---\n", file_format);
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}
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//determine mem per token
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const std::vector<llama_token> tmp = {0, 1, 2, 3};
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llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads);
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return true;
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}
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generation_outputs llama_generate(const generation_inputs inputs, generation_outputs &output)
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{
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params.prompt = inputs.prompt;
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params.seed = inputs.seed;
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params.n_predict = inputs.max_length;
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params.top_k = inputs.top_k;
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params.top_p = inputs.top_p;
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params.temp = inputs.temperature;
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params.repeat_last_n = inputs.rep_pen_range;
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params.repeat_penalty = inputs.rep_pen;
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params.n_ctx = inputs.max_context_length;
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params.n_batch = n_batch;
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params.n_threads = n_threads;
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if (params.repeat_last_n < 1)
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{
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params.repeat_last_n = 1;
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}
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if (params.top_k < 1)
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{
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params.top_k = 300; //to disable top_k we actually need to increase this value to a very high number
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}
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if (params.seed <= 0)
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{
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params.seed = time(NULL);
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}
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params.prompt.insert(0, 1, ' ');
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// tokenize the prompt
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std::vector<llama_token> embd_inp;
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if (file_format == 1)
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{
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embd_inp = ::legacy_llama_tokenize(ctx, params.prompt, true);
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}
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else
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{
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embd_inp = ::llama_tokenize(ctx, params.prompt, true);
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}
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//params.n_predict = std::min(params.n_predict, params.n_ctx - (int) embd_inp.size());
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//truncate to front of the prompt if its too long
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if (embd_inp.size() + params.n_predict > params.n_ctx)
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{
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int offset = embd_inp.size() - params.n_ctx + params.n_predict;
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embd_inp = std::vector<llama_token>(embd_inp.begin() + offset, embd_inp.end());
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}
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//determine how much npast we have to rewind from the current state
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std::vector<llama_token> embd;
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int last_n_size = params.repeat_last_n;
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last_n_tokens.resize(last_n_size);
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//display usage
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// std::string tst = " ";
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// char * tst2 = (char*)tst.c_str();
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// gpt_print_usage(1,&tst2,params);
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std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
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n_past = 0;
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//fast forward the past based on identical tokens, stop once a divergence is noted
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int embd_inp_len = embd_inp.size();
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for (int i = 0; i < current_context_tokens.size(); ++i)
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{
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if (current_context_tokens[i] == embd_inp[i])
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{
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n_past += 1;
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last_n_tokens.push_back(current_context_tokens[i]);
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}
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else
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{
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break;
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}
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if ((i + 2) >= embd_inp_len)
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{
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break;
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}
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}
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last_n_tokens.erase(last_n_tokens.begin(), last_n_tokens.begin() + n_past);
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embd_inp.erase(embd_inp.begin(), embd_inp.begin() + n_past);
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//if using BLAS and prompt is big enough, switch to single thread and use a huge batch
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bool blasmode = (embd_inp.size() >= 32 && ggml_cpu_has_blas());
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int original_batch = params.n_batch;
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int original_threads = params.n_threads;
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if (blasmode)
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{
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params.n_batch = 512;
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params.n_threads = 1;
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}
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current_context_tokens.resize(n_past);
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int remaining_tokens = params.n_predict;
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int input_consumed = 0;
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std::mt19937 rng(params.seed);
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std::string concat_output = "";
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bool startedsampling = false;
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timer_start();
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double time1 = 0, time2 = 0;
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unsigned int embd_inp_size = embd_inp.size();
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printf("\n");
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while (remaining_tokens > 0)
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{
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llama_token id = 0;
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// predict
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unsigned int embdsize = embd.size();
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if (embdsize > 0)
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{
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//print progress
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if (!startedsampling)
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{
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printf("\rProcessing Prompt%s (%d / %d tokens)", (blasmode ? " [BLAS]" : ""), input_consumed, embd_inp_size);
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}
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else
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{
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printf("\rGenerating (%d / %d tokens)", (1 + params.n_predict - remaining_tokens), params.n_predict);
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}
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//printf("\nnp:%d embd:%d txt:%s",n_past,embd.size(),llama_token_to_str(ctx, embd[0]));
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if (llama_eval(ctx, embd.data(), embdsize, n_past, params.n_threads))
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{
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fprintf(stderr, "Failed to predict\n");
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snprintf(output.text, sizeof(output.text), "%s", "");
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output.status = 0;
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return output;
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}
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}
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n_past += embd.size();
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embd.clear();
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if ((int)embd_inp.size() <= input_consumed)
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{
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// out of user input, sample next token
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const float top_k = params.top_k;
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const float top_p = params.top_p;
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const float temp = params.temp;
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const float repeat_penalty = params.repeat_penalty;
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if (!startedsampling)
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{
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startedsampling = true;
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params.n_batch = original_batch;
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params.n_threads = original_threads;
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time1 = timer_check();
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timer_start();
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printf("\n");
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}
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{
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auto logits = llama_get_logits(ctx);
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// set the logit of the eos token (2) to zero to avoid sampling it
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logits[llama_token_eos()] = 0;
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//set logits of opening square bracket to zero.
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logits[518] = 0;
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logits[29961] = 0;
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id = llama_sample_top_p_top_k(ctx, last_n_tokens.data(), last_n_tokens.size(), top_k, top_p, temp, repeat_penalty);
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last_n_tokens.erase(last_n_tokens.begin());
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last_n_tokens.push_back(id);
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current_context_tokens.push_back(id);
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}
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// add it to the context
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embd.push_back(id);
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// decrement remaining sampling budget
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--remaining_tokens;
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//printf("\nid:%d word:%s\n",id,llama_token_to_str(ctx, id));
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concat_output += llama_token_to_str(ctx, id);
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}
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else
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{
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// some user input remains from prompt or interaction, forward it to processing
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while ((int)embd_inp.size() > input_consumed)
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{
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embd.push_back(embd_inp[input_consumed]);
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last_n_tokens.erase(last_n_tokens.begin());
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last_n_tokens.push_back(embd_inp[input_consumed]);
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current_context_tokens.push_back(embd_inp[input_consumed]);
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++input_consumed;
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if ((int)embd.size() >= params.n_batch)
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{
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break;
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}
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}
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}
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}
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time2 = timer_check();
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printf("\nTime Taken - Processing:%.1fs, Generation:%.1fs, Total:%.1fs", time1, time2, (time1 + time2));
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output.status = 1;
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snprintf(output.text, sizeof(output.text), "%s", concat_output.c_str());
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return output;
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}
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