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https://github.com/dogkeeper886/ollama37.git
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add llama.cpp go bindings
This commit is contained in:
Jeffrey Morgan
730
llama/binding/binding.cpp
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730
llama/binding/binding.cpp
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// MIT License
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// Copyright (c) 2023 go-skynet authors
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// Permission is hereby granted, free of charge, to any person obtaining a copy
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// of this software and associated documentation files (the "Software"), to deal
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// in the Software without restriction, including without limitation the rights
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// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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// copies of the Software, and to permit persons to whom the Software is
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// furnished to do so, subject to the following conditions:
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// The above copyright notice and this permission notice shall be included in
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// all copies or substantial portions of the Software.
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// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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// SOFTWARE.
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#include "common.h"
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#include "llama.h"
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#include "binding.h"
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#include <cassert>
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#include <cinttypes>
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#include <cmath>
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#include <cstdio>
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#include <cstring>
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#include <fstream>
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#include <iostream>
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#include <regex>
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#include <sstream>
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#include <string>
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#include <vector>
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#if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__))
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#include <signal.h>
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#include <unistd.h>
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#elif defined(_WIN32)
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#define WIN32_LEAN_AND_MEAN
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#define NOMINMAX
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#include <signal.h>
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#include <windows.h>
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#endif
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#if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__)) || \
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defined(_WIN32)
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void sigint_handler(int signo) {
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if (signo == SIGINT) {
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_exit(130);
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}
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}
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#endif
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int get_embeddings(void *params_ptr, void *state_pr, float *res_embeddings) {
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gpt_params *params_p = (gpt_params *)params_ptr;
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llama_context *ctx = (llama_context *)state_pr;
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gpt_params params = *params_p;
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if (params.seed <= 0) {
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params.seed = time(NULL);
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}
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std::mt19937 rng(params.seed);
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llama_init_backend(params.numa);
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int n_past = 0;
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// Add a space in front of the first character to match OG llama tokenizer
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// behavior
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params.prompt.insert(0, 1, ' ');
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// tokenize the prompt
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auto embd_inp = ::llama_tokenize(ctx, params.prompt, true);
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// determine newline token
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auto llama_token_newline = ::llama_tokenize(ctx, "\n", false);
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if (embd_inp.size() > 0) {
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if (llama_eval(ctx, embd_inp.data(), embd_inp.size(), n_past,
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params.n_threads)) {
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fprintf(stderr, "%s : failed to eval\n", __func__);
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return 1;
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}
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}
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const int n_embd = llama_n_embd(ctx);
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const auto embeddings = llama_get_embeddings(ctx);
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for (int i = 0; i < n_embd; i++) {
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res_embeddings[i] = embeddings[i];
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}
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return 0;
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}
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int get_token_embeddings(void *params_ptr, void *state_pr, int *tokens,
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int tokenSize, float *res_embeddings) {
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gpt_params *params_p = (gpt_params *)params_ptr;
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llama_context *ctx = (llama_context *)state_pr;
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gpt_params params = *params_p;
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for (int i = 0; i < tokenSize; i++) {
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auto token_str = llama_token_to_str(ctx, tokens[i]);
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if (token_str == nullptr) {
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continue;
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}
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std::vector<std::string> my_vector;
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std::string str_token(token_str); // create a new std::string from the char*
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params_p->prompt += str_token;
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}
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return get_embeddings(params_ptr, state_pr, res_embeddings);
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}
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int eval(void *params_ptr, void *state_pr, char *text) {
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gpt_params *params_p = (gpt_params *)params_ptr;
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llama_context *ctx = (llama_context *)state_pr;
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auto n_past = 0;
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auto last_n_tokens_data =
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std::vector<llama_token>(params_p->repeat_last_n, 0);
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auto tokens = std::vector<llama_token>(params_p->n_ctx);
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auto n_prompt_tokens =
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llama_tokenize(ctx, text, tokens.data(), tokens.size(), true);
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if (n_prompt_tokens < 1) {
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fprintf(stderr, "%s : failed to tokenize prompt\n", __func__);
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return 1;
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}
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// evaluate prompt
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return llama_eval(ctx, tokens.data(), n_prompt_tokens, n_past,
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params_p->n_threads);
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}
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int llama_predict(void *params_ptr, void *state_pr, char *result, bool debug) {
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gpt_params *params_p = (gpt_params *)params_ptr;
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llama_context *ctx = (llama_context *)state_pr;
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gpt_params params = *params_p;
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const int n_ctx = llama_n_ctx(ctx);
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if (params.seed <= 0) {
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params.seed = time(NULL);
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}
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std::mt19937 rng(params.seed);
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std::string path_session = params.path_prompt_cache;
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std::vector<llama_token> session_tokens;
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if (!path_session.empty()) {
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if (debug) {
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fprintf(stderr, "%s: attempting to load saved session from '%s'\n",
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__func__, path_session.c_str());
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}
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// fopen to check for existing session
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FILE *fp = std::fopen(path_session.c_str(), "rb");
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if (fp != NULL) {
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std::fclose(fp);
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session_tokens.resize(n_ctx);
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size_t n_token_count_out = 0;
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if (!llama_load_session_file(
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ctx, path_session.c_str(), session_tokens.data(),
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session_tokens.capacity(), &n_token_count_out)) {
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fprintf(stderr, "%s: error: failed to load session file '%s'\n",
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__func__, path_session.c_str());
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return 1;
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}
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session_tokens.resize(n_token_count_out);
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llama_set_rng_seed(ctx, params.seed);
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if (debug) {
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fprintf(stderr, "%s: loaded a session with prompt size of %d tokens\n",
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__func__, (int)session_tokens.size());
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}
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} else {
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if (debug) {
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fprintf(stderr, "%s: session file does not exist, will create\n",
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__func__);
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}
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}
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}
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std::vector<llama_token> embd_inp;
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if (!params.prompt.empty() || session_tokens.empty()) {
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// Add a space in front of the first character to match OG llama tokenizer
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// behavior
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params.prompt.insert(0, 1, ' ');
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embd_inp = ::llama_tokenize(ctx, params.prompt, true);
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} else {
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embd_inp = session_tokens;
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}
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// debug message about similarity of saved session, if applicable
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size_t n_matching_session_tokens = 0;
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if (session_tokens.size()) {
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for (llama_token id : session_tokens) {
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if (n_matching_session_tokens >= embd_inp.size() ||
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id != embd_inp[n_matching_session_tokens]) {
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break;
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}
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n_matching_session_tokens++;
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}
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if (debug) {
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if (params.prompt.empty() &&
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n_matching_session_tokens == embd_inp.size()) {
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fprintf(stderr, "%s: using full prompt from session file\n", __func__);
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} else if (n_matching_session_tokens >= embd_inp.size()) {
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fprintf(stderr, "%s: session file has exact match for prompt!\n",
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__func__);
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} else if (n_matching_session_tokens < (embd_inp.size() / 2)) {
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fprintf(stderr,
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"%s: warning: session file has low similarity to prompt (%zu / "
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"%zu tokens); will mostly be reevaluated\n",
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__func__, n_matching_session_tokens, embd_inp.size());
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} else {
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fprintf(stderr, "%s: session file matches %zu / %zu tokens of prompt\n",
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__func__, n_matching_session_tokens, embd_inp.size());
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}
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}
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}
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// if we will use the cache for the full prompt without reaching the end of
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// the cache, force reevaluation of the last token token to recalculate the
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// cached logits
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if (!embd_inp.empty() && n_matching_session_tokens == embd_inp.size() &&
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session_tokens.size() > embd_inp.size()) {
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session_tokens.resize(embd_inp.size() - 1);
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}
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// number of tokens to keep when resetting context
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if (params.n_keep < 0 || params.n_keep > (int)embd_inp.size()) {
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params.n_keep = (int)embd_inp.size();
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}
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// determine newline token
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auto llama_token_newline = ::llama_tokenize(ctx, "\n", false);
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// TODO: replace with ring-buffer
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std::vector<llama_token> last_n_tokens(n_ctx);
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std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
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bool need_to_save_session =
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!path_session.empty() && n_matching_session_tokens < embd_inp.size();
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int n_past = 0;
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int n_remain = params.n_predict;
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int n_consumed = 0;
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int n_session_consumed = 0;
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std::vector<llama_token> embd;
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std::string res = "";
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// do one empty run to warm up the model
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{
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const std::vector<llama_token> tmp = {
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llama_token_bos(),
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};
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llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads);
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llama_reset_timings(ctx);
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}
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while (n_remain != 0) {
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// predict
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if (embd.size() > 0) {
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// infinite text generation via context swapping
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// if we run out of context:
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// - take the n_keep first tokens from the original prompt (via n_past)
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// - take half of the last (n_ctx - n_keep) tokens and recompute the
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// logits in batches
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if (n_past + (int)embd.size() > n_ctx) {
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const int n_left = n_past - params.n_keep;
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// always keep the first token - BOS
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n_past = std::max(1, params.n_keep);
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// insert n_left/2 tokens at the start of embd from last_n_tokens
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embd.insert(embd.begin(),
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last_n_tokens.begin() + n_ctx - n_left / 2 - embd.size(),
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last_n_tokens.end() - embd.size());
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// stop saving session if we run out of context
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path_session.clear();
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// printf("\n---\n");
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// printf("resetting: '");
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// for (int i = 0; i < (int) embd.size(); i++) {
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// printf("%s", llama_token_to_str(ctx, embd[i]));
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// }
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// printf("'\n");
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// printf("\n---\n");
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}
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// try to reuse a matching prefix from the loaded session instead of
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// re-eval (via n_past)
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if (n_session_consumed < (int)session_tokens.size()) {
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size_t i = 0;
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for (; i < embd.size(); i++) {
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if (embd[i] != session_tokens[n_session_consumed]) {
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session_tokens.resize(n_session_consumed);
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break;
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}
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n_past++;
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n_session_consumed++;
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if (n_session_consumed >= (int)session_tokens.size()) {
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++i;
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break;
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}
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}
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if (i > 0) {
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embd.erase(embd.begin(), embd.begin() + i);
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}
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}
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// evaluate tokens in batches
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// embd is typically prepared beforehand to fit within a batch, but not
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// always
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for (int i = 0; i < (int)embd.size(); i += params.n_batch) {
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int n_eval = (int)embd.size() - i;
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if (n_eval > params.n_batch) {
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n_eval = params.n_batch;
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}
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if (llama_eval(ctx, &embd[i], n_eval, n_past, params.n_threads)) {
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fprintf(stderr, "%s : failed to eval\n", __func__);
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return 1;
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}
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n_past += n_eval;
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}
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if (embd.size() > 0 && !path_session.empty()) {
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session_tokens.insert(session_tokens.end(), embd.begin(), embd.end());
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n_session_consumed = session_tokens.size();
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}
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}
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embd.clear();
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if ((int)embd_inp.size() <= n_consumed) {
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// out of user input, sample next token
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const float temp = params.temp;
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const int32_t top_k =
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params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k;
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const float top_p = params.top_p;
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const float tfs_z = params.tfs_z;
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const float typical_p = params.typical_p;
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const int32_t repeat_last_n =
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params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n;
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const float repeat_penalty = params.repeat_penalty;
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const float alpha_presence = params.presence_penalty;
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const float alpha_frequency = params.frequency_penalty;
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const int mirostat = params.mirostat;
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const float mirostat_tau = params.mirostat_tau;
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const float mirostat_eta = params.mirostat_eta;
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const bool penalize_nl = params.penalize_nl;
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// optionally save the session on first sample (for faster prompt loading
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// next time)
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if (!path_session.empty() && need_to_save_session &&
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!params.prompt_cache_ro) {
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need_to_save_session = false;
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llama_save_session_file(ctx, path_session.c_str(),
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session_tokens.data(), session_tokens.size());
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}
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llama_token id = 0;
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{
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auto logits = llama_get_logits(ctx);
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auto n_vocab = llama_n_vocab(ctx);
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// Apply params.logit_bias map
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for (auto it = params.logit_bias.begin(); it != params.logit_bias.end();
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it++) {
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logits[it->first] += it->second;
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}
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std::vector<llama_token_data> candidates;
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candidates.reserve(n_vocab);
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for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
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candidates.emplace_back(
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llama_token_data{token_id, logits[token_id], 0.0f});
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}
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llama_token_data_array candidates_p = {candidates.data(),
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candidates.size(), false};
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// Apply penalties
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float nl_logit = logits[llama_token_nl()];
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auto last_n_repeat =
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std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx);
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llama_sample_repetition_penalty(
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ctx, &candidates_p,
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last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
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last_n_repeat, repeat_penalty);
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llama_sample_frequency_and_presence_penalties(
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ctx, &candidates_p,
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last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
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last_n_repeat, alpha_frequency, alpha_presence);
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if (!penalize_nl) {
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logits[llama_token_nl()] = nl_logit;
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}
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if (temp <= 0) {
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// Greedy sampling
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id = llama_sample_token_greedy(ctx, &candidates_p);
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} else {
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if (mirostat == 1) {
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static float mirostat_mu = 2.0f * mirostat_tau;
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const int mirostat_m = 100;
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llama_sample_temperature(ctx, &candidates_p, temp);
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id = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau,
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mirostat_eta, mirostat_m,
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&mirostat_mu);
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} else if (mirostat == 2) {
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static float mirostat_mu = 2.0f * mirostat_tau;
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llama_sample_temperature(ctx, &candidates_p, temp);
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id = llama_sample_token_mirostat_v2(
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ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);
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} else {
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// Temperature sampling
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llama_sample_top_k(ctx, &candidates_p, top_k, 1);
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llama_sample_tail_free(ctx, &candidates_p, tfs_z, 1);
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llama_sample_typical(ctx, &candidates_p, typical_p, 1);
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llama_sample_top_p(ctx, &candidates_p, top_p, 1);
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llama_sample_temperature(ctx, &candidates_p, temp);
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id = llama_sample_token(ctx, &candidates_p);
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}
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}
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// printf("`%d`", candidates_p.size);
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last_n_tokens.erase(last_n_tokens.begin());
|
||||
last_n_tokens.push_back(id);
|
||||
}
|
||||
|
||||
// add it to the context
|
||||
embd.push_back(id);
|
||||
|
||||
// decrement remaining sampling budget
|
||||
--n_remain;
|
||||
|
||||
// call the token callback, no need to check if one is actually
|
||||
// registered, that will be handled on the Go side.
|
||||
auto token_str = llama_token_to_str(ctx, id);
|
||||
if (!tokenCallback(state_pr, (char *)token_str)) {
|
||||
break;
|
||||
}
|
||||
} else {
|
||||
// some user input remains from prompt or interaction, forward it to
|
||||
// processing
|
||||
while ((int)embd_inp.size() > n_consumed) {
|
||||
embd.push_back(embd_inp[n_consumed]);
|
||||
last_n_tokens.erase(last_n_tokens.begin());
|
||||
last_n_tokens.push_back(embd_inp[n_consumed]);
|
||||
++n_consumed;
|
||||
if ((int)embd.size() >= params.n_batch) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
for (auto id : embd) {
|
||||
res += llama_token_to_str(ctx, id);
|
||||
}
|
||||
|
||||
// check for stop prompt
|
||||
if (params.antiprompt.size()) {
|
||||
std::string last_output;
|
||||
for (auto id : last_n_tokens) {
|
||||
last_output += llama_token_to_str(ctx, id);
|
||||
}
|
||||
// Check if each of the reverse prompts appears at the end of the output.
|
||||
for (std::string &antiprompt : params.antiprompt) {
|
||||
// size_t extra_padding = params.interactive ? 0 : 2;
|
||||
size_t extra_padding = 2;
|
||||
size_t search_start_pos =
|
||||
last_output.length() >
|
||||
static_cast<size_t>(antiprompt.length() + extra_padding)
|
||||
? last_output.length() -
|
||||
static_cast<size_t>(antiprompt.length() + extra_padding)
|
||||
: 0;
|
||||
|
||||
if (last_output.find(antiprompt.c_str(), search_start_pos) !=
|
||||
std::string::npos) {
|
||||
goto end;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// end of text token
|
||||
if (!embd.empty() && embd.back() == llama_token_eos()) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
if (!path_session.empty() && params.prompt_cache_all &&
|
||||
!params.prompt_cache_ro) {
|
||||
if (debug) {
|
||||
fprintf(stderr, "\n%s: saving final output to session file '%s'\n",
|
||||
__func__, path_session.c_str());
|
||||
}
|
||||
llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(),
|
||||
session_tokens.size());
|
||||
}
|
||||
|
||||
end:
|
||||
#if defined(_WIN32)
|
||||
signal(SIGINT, SIG_DFL);
|
||||
#endif
|
||||
|
||||
if (debug) {
|
||||
llama_print_timings(ctx);
|
||||
llama_reset_timings(ctx);
|
||||
}
|
||||
|
||||
strcpy(result, res.c_str());
|
||||
return 0;
|
||||
}
|
||||
|
||||
void llama_binding_free_model(void *state_ptr) {
|
||||
llama_context *ctx = (llama_context *)state_ptr;
|
||||
llama_free(ctx);
|
||||
}
|
||||
|
||||
void llama_free_params(void *params_ptr) {
|
||||
gpt_params *params = (gpt_params *)params_ptr;
|
||||
delete params;
|
||||
}
|
||||
|
||||
std::vector<std::string> create_vector(const char **strings, int count) {
|
||||
std::vector<std::string> *vec = new std::vector<std::string>;
|
||||
for (int i = 0; i < count; i++) {
|
||||
vec->push_back(std::string(strings[i]));
|
||||
}
|
||||
return *vec;
|
||||
}
|
||||
|
||||
void delete_vector(std::vector<std::string> *vec) { delete vec; }
|
||||
|
||||
int load_state(void *ctx, char *statefile, char *modes) {
|
||||
llama_context *state = (llama_context *)ctx;
|
||||
const llama_context *constState = static_cast<const llama_context *>(state);
|
||||
const size_t state_size = llama_get_state_size(state);
|
||||
uint8_t *state_mem = new uint8_t[state_size];
|
||||
|
||||
{
|
||||
FILE *fp_read = fopen(statefile, modes);
|
||||
if (state_size != llama_get_state_size(constState)) {
|
||||
fprintf(stderr, "\n%s : failed to validate state size\n", __func__);
|
||||
return 1;
|
||||
}
|
||||
|
||||
const size_t ret = fread(state_mem, 1, state_size, fp_read);
|
||||
if (ret != state_size) {
|
||||
fprintf(stderr, "\n%s : failed to read state\n", __func__);
|
||||
return 1;
|
||||
}
|
||||
|
||||
llama_set_state_data(
|
||||
state, state_mem); // could also read directly from memory mapped file
|
||||
fclose(fp_read);
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
void save_state(void *ctx, char *dst, char *modes) {
|
||||
llama_context *state = (llama_context *)ctx;
|
||||
|
||||
const size_t state_size = llama_get_state_size(state);
|
||||
uint8_t *state_mem = new uint8_t[state_size];
|
||||
|
||||
// Save state (rng, logits, embedding and kv_cache) to file
|
||||
{
|
||||
FILE *fp_write = fopen(dst, modes);
|
||||
llama_copy_state_data(
|
||||
state, state_mem); // could also copy directly to memory mapped file
|
||||
fwrite(state_mem, 1, state_size, fp_write);
|
||||
fclose(fp_write);
|
||||
}
|
||||
}
|
||||
|
||||
void *llama_allocate_params(
|
||||
const char *prompt, int seed, int threads, int tokens, int top_k,
|
||||
float top_p, float temp, float repeat_penalty, int repeat_last_n,
|
||||
bool ignore_eos, bool memory_f16, int n_batch, int n_keep,
|
||||
const char **antiprompt, int antiprompt_count, float tfs_z, float typical_p,
|
||||
float frequency_penalty, float presence_penalty, int mirostat,
|
||||
float mirostat_eta, float mirostat_tau, bool penalize_nl,
|
||||
const char *logit_bias, const char *session_file, bool prompt_cache_all,
|
||||
bool mlock, bool mmap, const char *maingpu, const char *tensorsplit,
|
||||
bool prompt_cache_ro) {
|
||||
gpt_params *params = new gpt_params;
|
||||
params->seed = seed;
|
||||
params->n_threads = threads;
|
||||
params->n_predict = tokens;
|
||||
params->repeat_last_n = repeat_last_n;
|
||||
params->prompt_cache_ro = prompt_cache_ro;
|
||||
params->top_k = top_k;
|
||||
params->top_p = top_p;
|
||||
params->memory_f16 = memory_f16;
|
||||
params->temp = temp;
|
||||
params->use_mmap = mmap;
|
||||
params->use_mlock = mlock;
|
||||
params->repeat_penalty = repeat_penalty;
|
||||
params->n_batch = n_batch;
|
||||
params->n_keep = n_keep;
|
||||
if (maingpu[0] != '\0') {
|
||||
params->main_gpu = std::stoi(maingpu);
|
||||
}
|
||||
|
||||
if (tensorsplit[0] != '\0') {
|
||||
std::string arg_next = tensorsplit;
|
||||
// split string by , and /
|
||||
const std::regex regex{R"([,/]+)"};
|
||||
std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1};
|
||||
std::vector<std::string> split_arg{it, {}};
|
||||
GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);
|
||||
|
||||
for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i) {
|
||||
if (i < split_arg.size()) {
|
||||
params->tensor_split[i] = std::stof(split_arg[i]);
|
||||
} else {
|
||||
params->tensor_split[i] = 0.0f;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
params->prompt_cache_all = prompt_cache_all;
|
||||
params->path_prompt_cache = session_file;
|
||||
|
||||
if (ignore_eos) {
|
||||
params->logit_bias[llama_token_eos()] = -INFINITY;
|
||||
}
|
||||
if (antiprompt_count > 0) {
|
||||
params->antiprompt = create_vector(antiprompt, antiprompt_count);
|
||||
}
|
||||
params->tfs_z = tfs_z;
|
||||
params->typical_p = typical_p;
|
||||
params->presence_penalty = presence_penalty;
|
||||
params->mirostat = mirostat;
|
||||
params->mirostat_eta = mirostat_eta;
|
||||
params->mirostat_tau = mirostat_tau;
|
||||
params->penalize_nl = penalize_nl;
|
||||
std::stringstream ss(logit_bias);
|
||||
llama_token key;
|
||||
char sign;
|
||||
std::string value_str;
|
||||
if (ss >> key && ss >> sign && std::getline(ss, value_str) &&
|
||||
(sign == '+' || sign == '-')) {
|
||||
params->logit_bias[key] =
|
||||
std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f);
|
||||
}
|
||||
params->frequency_penalty = frequency_penalty;
|
||||
params->prompt = prompt;
|
||||
|
||||
return params;
|
||||
}
|
||||
|
||||
void *load_model(const char *fname, int n_ctx, int n_seed, bool memory_f16,
|
||||
bool mlock, bool embeddings, bool mmap, bool low_vram,
|
||||
bool vocab_only, int n_gpu_layers, int n_batch,
|
||||
const char *maingpu, const char *tensorsplit, bool numa) {
|
||||
// load the model
|
||||
auto lparams = llama_context_default_params();
|
||||
|
||||
lparams.n_ctx = n_ctx;
|
||||
lparams.seed = n_seed;
|
||||
lparams.f16_kv = memory_f16;
|
||||
lparams.embedding = embeddings;
|
||||
lparams.use_mlock = mlock;
|
||||
lparams.n_gpu_layers = n_gpu_layers;
|
||||
lparams.use_mmap = mmap;
|
||||
lparams.low_vram = low_vram;
|
||||
lparams.vocab_only = vocab_only;
|
||||
|
||||
if (maingpu[0] != '\0') {
|
||||
lparams.main_gpu = std::stoi(maingpu);
|
||||
}
|
||||
|
||||
if (tensorsplit[0] != '\0') {
|
||||
std::string arg_next = tensorsplit;
|
||||
// split string by , and /
|
||||
const std::regex regex{R"([,/]+)"};
|
||||
std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1};
|
||||
std::vector<std::string> split_arg{it, {}};
|
||||
GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);
|
||||
|
||||
for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i) {
|
||||
if (i < split_arg.size()) {
|
||||
lparams.tensor_split[i] = std::stof(split_arg[i]);
|
||||
} else {
|
||||
lparams.tensor_split[i] = 0.0f;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
lparams.n_batch = n_batch;
|
||||
|
||||
llama_init_backend(numa);
|
||||
void *res = nullptr;
|
||||
try {
|
||||
llama_model *model = llama_load_model_from_file(fname, lparams);
|
||||
if (model == NULL) {
|
||||
fprintf(stderr, "error: failed to load model \n");
|
||||
return res;
|
||||
}
|
||||
|
||||
llama_context *lctx = llama_new_context_with_model(model, lparams);
|
||||
if (lctx == NULL) {
|
||||
fprintf(stderr, "error: failed to create context with model \n");
|
||||
llama_free_model(model);
|
||||
return res;
|
||||
}
|
||||
|
||||
} catch (std::runtime_error &e) {
|
||||
fprintf(stderr, "failed %s", e.what());
|
||||
return res;
|
||||
}
|
||||
|
||||
return res;
|
||||
}
|
||||
Reference in New Issue
Block a user