mirror of
https://github.com/LostRuins/koboldcpp.git
synced 2025-09-10 00:54:41 +00:00
363 lines
12 KiB
C++
363 lines
12 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 <time.h>
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#include "model_adapter.h"
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#include "otherarch/otherarch.h"
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//concat source files into one file for compilation purposes
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#include "otherarch/utils.cpp"
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#include "otherarch/gptj_v1.cpp"
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#include "otherarch/gptj_v2.cpp"
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#include "otherarch/gpt2_v1.cpp"
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#include "otherarch/gpt2_v2.cpp"
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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::BADFORMAT;
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static gpt_vocab vocab;
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static gptj_model_v1 model_v1;
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static gptj_model model_v2;
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static gpt2_v1_model model_gpt2_v1;
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static gpt2_model model_gpt2_v2;
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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 modelname;
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static std::vector<gpt_vocab::id> last_n_tokens;
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static std::vector<gpt_vocab::id> current_context_tokens;
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static size_t mem_per_token = 0;
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static std::vector<float> logits;
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ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in_file_format)
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{
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ggml_time_init();
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file_format = in_file_format;
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n_threads = params.n_threads = inputs.threads;
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n_batch = params.n_batch = inputs.batch_size;
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modelname = params.model = inputs.model_filename;
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if (file_format == FileFormat::GPT2_1)
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{
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ModelLoadResult res = legacy_gpt2_model_load(params.model, model_gpt2_v1, vocab, file_format);
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if(res==ModelLoadResult::FAIL)
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{
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fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
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return res;
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}
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else if(res==ModelLoadResult::RETRY_LOAD)
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{
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printf("\nTensor Transposition Detected! Retrying GPT-2 model loading...");
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return res;
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}
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// determine the required inference memory per token:
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legacy_gpt2_eval(model_gpt2_v1, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token, file_format);
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return ModelLoadResult::SUCCESS;
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}
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else if (file_format == FileFormat::GPT2_2)
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{
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ModelLoadResult res = gpt2_model_load(params.model, model_gpt2_v2, vocab, file_format);
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if(res==ModelLoadResult::FAIL)
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{
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fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
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return res;
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}
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else if(res==ModelLoadResult::RETRY_LOAD)
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{
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printf("\nTensor Transposition Detected! Retrying GPT-2 model loading...");
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return res;
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}
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// determine the required inference memory per token:
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gpt2_eval(model_gpt2_v2, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token, file_format);
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return ModelLoadResult::SUCCESS;
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}
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else if (file_format == FileFormat::GPTJ_1 || file_format == FileFormat::GPTJ_2)
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{
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ModelLoadResult res = legacy_gptj_model_load(params.model, model_v1, vocab, file_format);
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if(res==ModelLoadResult::FAIL)
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{
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fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
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return res;
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}
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else if(res==ModelLoadResult::RETRY_LOAD)
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{
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printf("\nTensor Transposition Detected! Retrying GPT-J model loading...");
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return res;
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}
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// determine the required inference memory per token:
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legacy_gptj_eval(model_v1, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token, file_format);
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return ModelLoadResult::SUCCESS;
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}
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else
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{
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ModelLoadResult loadresult = gptj_model_load(params.model, model_v2, vocab);
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if (loadresult == ModelLoadResult::FAIL)
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{
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fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
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return loadresult;
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}
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else if (loadresult == ModelLoadResult::RETRY_LOAD)
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{
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printf("\nTensor Transposition Detected! Retrying GPT-J model loading...");
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return loadresult;
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}
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// determine the required inference memory per token:
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gptj_eval(model_v2, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token);
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return ModelLoadResult::SUCCESS;
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}
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}
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generation_outputs gpttype_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_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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// tokenize the prompt
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std::vector<gpt_vocab::id> embd_inp = ::gpt_tokenize(vocab, params.prompt);
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//truncate to front of the prompt if its too long
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int32_t nctx = 512;
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if(file_format == FileFormat::GPTJ_1||file_format == FileFormat::GPTJ_2)
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{
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nctx = model_v1.hparams.n_ctx;
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}
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else if(file_format==FileFormat::GPTJ_3)
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{
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nctx = model_v2.hparams.n_ctx;
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}
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else if(file_format==FileFormat::GPT2_1)
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{
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nctx = model_gpt2_v1.hparams.n_ctx;
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}
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else if(file_format==FileFormat::GPT2_2)
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{
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nctx = model_gpt2_v2.hparams.n_ctx;
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}
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if (embd_inp.size() + params.n_predict > nctx)
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{
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int offset = embd_inp.size() - nctx + 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<gpt_vocab::id> 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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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 = false; //(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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int32_t n_vocab = 0;
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if(file_format == FileFormat::GPTJ_1||file_format == FileFormat::GPTJ_2)
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{
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n_vocab = model_v1.hparams.n_vocab;
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}
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else if(file_format == FileFormat::GPTJ_3)
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{
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n_vocab = model_v2.hparams.n_vocab;
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}
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else if(file_format == FileFormat::GPT2_1)
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{
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n_vocab = model_gpt2_v1.hparams.n_vocab;
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}
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else if(file_format == FileFormat::GPT2_2)
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{
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n_vocab = model_gpt2_v2.hparams.n_vocab;
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}
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else
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{
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printf("Bad format!");
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}
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printf("\n");
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while (remaining_tokens > 0)
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{
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gpt_vocab::id 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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bool evalres = false;
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//print_tok_vec(logits);
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if(file_format==FileFormat::GPT2_1)
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{
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evalres = legacy_gpt2_eval(model_gpt2_v1, params.n_threads, n_past, embd, logits, mem_per_token, file_format);
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}
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else if(file_format==FileFormat::GPT2_2)
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{
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evalres = gpt2_eval(model_gpt2_v2, params.n_threads, n_past, embd, logits, mem_per_token, file_format);
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}
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else if(file_format==FileFormat::GPTJ_1 || file_format==FileFormat::GPTJ_2)
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{
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evalres = legacy_gptj_eval(model_v1, params.n_threads, n_past, embd, logits, mem_per_token, file_format);
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}
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else
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{
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evalres = gptj_eval(model_v2, params.n_threads, n_past, embd, logits, mem_per_token);
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}
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if (!evalres)
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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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// set the logit of the eos token (2) to zero to avoid sampling it
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logits[50256] = (logits[50256]<0?logits[50256]:0);
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//gpt2 uses negative logits, so we cant zero it
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id = gptj_sample_top_p_top_k(vocab, logits.data() + (logits.size() - n_vocab), last_n_tokens, repeat_penalty, top_k, top_p, temp, rng);
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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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for (auto id : embd) {
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concat_output += vocab.id_to_token[id].c_str();
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}
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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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fflush(stdout);
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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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