mirror of
https://github.com/LostRuins/koboldcpp.git
synced 2025-09-10 09:04:36 +00:00
277 lines
No EOL
8.8 KiB
C++
277 lines
No EOL
8.8 KiB
C++
//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 "./examples/main/main.cpp"
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#include "extra.h"
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void print_tok_vec(std::vector<llama_token> & embd)
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{
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std::cout << "[";
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bool first = true;
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for (auto i: embd) {
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if(!first)
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{
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std::cout << ',';
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}
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first = false;
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std::cout << i;
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}
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std::cout << "]";
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}
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extern "C" {
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struct load_model_inputs
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{
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const int threads;
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const int max_context_length;
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const int batch_size;
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const bool f16_kv;
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const char * model_filename;
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const int n_parts_overwrite = -1;
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};
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struct generation_inputs
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{
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const int seed;
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const char * prompt;
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const int max_context_length;
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const int max_length;
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const float temperature;
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const int top_k;
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const float top_p;
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const float rep_pen;
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const int rep_pen_range;
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};
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struct generation_outputs
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{
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int status = -1;
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char text[16384]; //16kb should be enough for any response
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};
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bool legacy_format = false;
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llama_context_params ctx_params;
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gpt_params params;
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int n_past = 0;
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int n_threads = 4;
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int n_batch = 8;
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std::string model;
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llama_context * ctx;
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std::vector<llama_token> last_n_tokens;
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std::vector<llama_token> current_context_tokens;
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bool load_model(const load_model_inputs inputs)
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{
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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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ctx = llama_init_from_file(model.c_str(), ctx_params);
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if (ctx == NULL) {
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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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//return val: 0=fail, 1=newformat, 2=legacy
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int fileformat = check_file_format(model.c_str());
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legacy_format = (fileformat==1?true:false);
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if(legacy_format)
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{
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printf("\n---\nWarning: Your model is using an OUTDATED format. Please reconvert it for better results!\n");
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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 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(legacy_format)
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{
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embd_inp = ::legacy_llama_tokenize(ctx, params.prompt, true);
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}else{
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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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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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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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printf("\nProcessing Prompt (%d tokens): ",embd_inp.size());
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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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if (embd.size() > 0)
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{
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printf("|");
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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(), embd.size(), 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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printf("\nGenerating (%d tokens): ",params.n_predict);
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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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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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} |