fix crash in bindings
This commit is contained in:
Jeffrey Morgan
parent
6559a5b48f
commit
79a999e95d
4 changed files with 235 additions and 116 deletions
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@ -4,7 +4,7 @@ include(FetchContent)
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FetchContent_Declare(
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llama_cpp
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GIT_REPOSITORY https://github.com/ggerganov/llama.cpp.git
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GIT_TAG master
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GIT_TAG 55dbb91
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)
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FetchContent_MakeAvailable(llama_cpp)
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@ -1,25 +1,3 @@
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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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@ -55,14 +33,78 @@ void sigint_handler(int signo) {
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}
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#endif
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int eval(void *p, void *c, char *text) {
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gpt_params *params = (gpt_params *)params;
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llama_context *ctx = (llama_context *)ctx;
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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 = std::vector<llama_token>(params->repeat_last_n, 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->n_ctx);
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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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@ -71,22 +113,26 @@ int eval(void *p, void *c, char *text) {
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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->n_threads);
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params_p->n_threads);
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}
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int llama_predict(void *p, void *c, char *result, bool debug) {
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gpt_params *params = (gpt_params *)params;
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llama_context *ctx = (llama_context *)ctx;
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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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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::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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@ -109,7 +155,7 @@ int llama_predict(void *p, void *c, char *result, bool debug) {
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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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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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@ -123,12 +169,12 @@ int llama_predict(void *p, void *c, char *result, bool debug) {
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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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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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params.prompt.insert(0, 1, ' ');
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embd_inp = ::llama_tokenize(ctx, params->prompt, true);
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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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@ -144,7 +190,7 @@ int llama_predict(void *p, void *c, char *result, bool debug) {
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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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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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@ -169,8 +215,8 @@ int llama_predict(void *p, void *c, char *result, bool debug) {
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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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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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@ -183,7 +229,7 @@ int llama_predict(void *p, void *c, char *result, bool debug) {
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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_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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@ -195,7 +241,7 @@ int llama_predict(void *p, void *c, char *result, bool debug) {
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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_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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@ -208,10 +254,10 @@ int llama_predict(void *p, void *c, char *result, bool debug) {
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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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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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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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@ -220,6 +266,14 @@ int llama_predict(void *p, void *c, char *result, bool debug) {
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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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@ -248,17 +302,15 @@ int llama_predict(void *p, void *c, char *result, bool debug) {
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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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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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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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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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@ -272,26 +324,26 @@ int llama_predict(void *p, void *c, char *result, bool debug) {
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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 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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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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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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!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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@ -304,8 +356,8 @@ int llama_predict(void *p, void *c, char *result, bool debug) {
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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();
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it != params->logit_bias.end(); it++) {
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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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@ -361,6 +413,7 @@ int llama_predict(void *p, void *c, char *result, bool debug) {
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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());
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last_n_tokens.push_back(id);
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@ -375,7 +428,7 @@ int llama_predict(void *p, void *c, char *result, bool debug) {
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// call the token callback, no need to check if one is actually
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// registered, that will be handled on the Go side.
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auto token_str = llama_token_to_str(ctx, id);
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if (!tokenCallback(ctx, (char *)token_str)) {
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if (!tokenCallback(state_pr, (char *)token_str)) {
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break;
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}
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} else {
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@ -386,7 +439,7 @@ int llama_predict(void *p, void *c, char *result, bool debug) {
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last_n_tokens.erase(last_n_tokens.begin());
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last_n_tokens.push_back(embd_inp[n_consumed]);
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++n_consumed;
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if ((int)embd.size() >= params->n_batch) {
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if ((int)embd.size() >= params.n_batch) {
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break;
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}
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}
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@ -397,13 +450,13 @@ int llama_predict(void *p, void *c, char *result, bool debug) {
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}
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// check for stop prompt
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if (params->antiprompt.size()) {
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if (params.antiprompt.size()) {
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std::string last_output;
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for (auto id : last_n_tokens) {
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last_output += llama_token_to_str(ctx, id);
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}
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// Check if each of the reverse prompts appears at the end of the output.
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for (std::string &antiprompt : params->antiprompt) {
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for (std::string &antiprompt : params.antiprompt) {
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// size_t extra_padding = params.interactive ? 0 : 2;
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size_t extra_padding = 2;
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size_t search_start_pos =
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@ -426,8 +479,8 @@ int llama_predict(void *p, void *c, char *result, bool debug) {
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}
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}
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if (!path_session.empty() && params->prompt_cache_all &&
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!params->prompt_cache_ro) {
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if (!path_session.empty() && params.prompt_cache_all &&
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!params.prompt_cache_ro) {
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if (debug) {
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fprintf(stderr, "\n%s: saving final output to session file '%s'\n",
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__func__, path_session.c_str());
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@ -450,8 +503,68 @@ end:
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return 0;
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}
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void llama_binding_free_model(void *ctx) { llama_free((llama_context *)ctx); }
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void llama_free_params(void *params) { delete (gpt_params *)params; }
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void llama_binding_free_model(void *state_ptr) {
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llama_context *ctx = (llama_context *)state_ptr;
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llama_free(ctx);
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}
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void llama_free_params(void *params_ptr) {
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gpt_params *params = (gpt_params *)params_ptr;
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delete params;
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}
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std::vector<std::string> create_vector(const char **strings, int count) {
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std::vector<std::string> *vec = new std::vector<std::string>;
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for (int i = 0; i < count; i++) {
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vec->push_back(std::string(strings[i]));
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}
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return *vec;
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}
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void delete_vector(std::vector<std::string> *vec) { delete vec; }
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int load_state(void *ctx, char *statefile, char *modes) {
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llama_context *state = (llama_context *)ctx;
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const llama_context *constState = static_cast<const llama_context *>(state);
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const size_t state_size = llama_get_state_size(state);
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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,
|
||||
|
|
@ -505,13 +618,9 @@ void *llama_allocate_params(
|
|||
if (ignore_eos) {
|
||||
params->logit_bias[llama_token_eos()] = -INFINITY;
|
||||
}
|
||||
|
||||
if (antiprompt_count > 0) {
|
||||
for (int i = 0; i < antiprompt_count; i++) {
|
||||
params->antiprompt.push_back(std::string(antiprompt[i]));
|
||||
params->antiprompt = create_vector(antiprompt, antiprompt_count);
|
||||
}
|
||||
}
|
||||
|
||||
params->tfs_z = tfs_z;
|
||||
params->typical_p = typical_p;
|
||||
params->presence_penalty = presence_penalty;
|
||||
|
|
@ -519,7 +628,6 @@ void *llama_allocate_params(
|
|||
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;
|
||||
|
|
@ -539,6 +647,7 @@ 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;
|
||||
|
|
@ -575,11 +684,13 @@ void *load_model(const char *fname, int n_ctx, int n_seed, bool memory_f16,
|
|||
lparams.n_batch = n_batch;
|
||||
|
||||
llama_init_backend(numa);
|
||||
|
||||
struct llama_model *model = llama_load_model_from_file(fname, lparams);
|
||||
if (!model) {
|
||||
return nullptr;
|
||||
void *res = nullptr;
|
||||
try {
|
||||
res = llama_init_from_file(fname, lparams);
|
||||
} catch (std::runtime_error &e) {
|
||||
fprintf(stderr, "failed %s", e.what());
|
||||
return res;
|
||||
}
|
||||
|
||||
return llama_new_context_with_model(model, lparams);
|
||||
return res;
|
||||
}
|
||||
|
|
@ -1,25 +1,3 @@
|
|||
// MIT License
|
||||
|
||||
// Copyright (c) 2023 go-skynet authors
|
||||
|
||||
// Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
// of this software and associated documentation files (the "Software"), to deal
|
||||
// in the Software without restriction, including without limitation the rights
|
||||
// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
// copies of the Software, and to permit persons to whom the Software is
|
||||
// furnished to do so, subject to the following conditions:
|
||||
|
||||
// The above copyright notice and this permission notice shall be included in
|
||||
// all copies or substantial portions of the Software.
|
||||
|
||||
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
// SOFTWARE.
|
||||
|
||||
#ifdef __cplusplus
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
|
@ -30,13 +8,22 @@ extern "C" {
|
|||
|
||||
extern unsigned char tokenCallback(void *, char *);
|
||||
|
||||
int eval(void *p, void *c, char *text);
|
||||
int load_state(void *ctx, char *statefile, char *modes);
|
||||
|
||||
int eval(void *params_ptr, void *ctx, char *text);
|
||||
|
||||
void save_state(void *ctx, char *dst, char *modes);
|
||||
|
||||
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, int n_batch, const char *maingpu,
|
||||
const char *tensorsplit, bool numa);
|
||||
|
||||
int get_embeddings(void *params_ptr, void *state_pr, float *res_embeddings);
|
||||
|
||||
int get_token_embeddings(void *params_ptr, void *state_pr, int *tokens,
|
||||
int tokenSize, float *res_embeddings);
|
||||
|
||||
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,
|
||||
|
|
@ -50,11 +37,13 @@ void *llama_allocate_params(
|
|||
|
||||
void llama_free_params(void *params_ptr);
|
||||
|
||||
void llama_binding_free_model(void *ctx);
|
||||
void llama_binding_free_model(void *state);
|
||||
|
||||
int llama_predict(void *params_ptr, void *state_pr, char *result, bool debug);
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
|
||||
std::vector<std::string> create_vector(const char **strings, int count);
|
||||
void delete_vector(std::vector<std::string> *vec);
|
||||
#endif
|
||||
|
|
@ -28,6 +28,7 @@ package llama
|
|||
// #cgo CXXFLAGS: -std=c++11
|
||||
// #cgo darwin LDFLAGS: -framework Accelerate -framework Foundation -framework Metal -framework MetalKit -framework MetalPerformanceShaders
|
||||
// #include "binding/binding.h"
|
||||
// #include <stdlib.h>
|
||||
import "C"
|
||||
import (
|
||||
"fmt"
|
||||
|
|
@ -45,8 +46,8 @@ type LLama struct {
|
|||
func New(model string, opts ...ModelOption) (*LLama, error) {
|
||||
mo := NewModelOptions(opts...)
|
||||
|
||||
// TODO: free this pointer
|
||||
modelPath := C.CString(model)
|
||||
defer C.free(unsafe.Pointer(modelPath))
|
||||
|
||||
ctx := C.load_model(modelPath, C.int(mo.ContextSize), C.int(mo.Seed), C.bool(mo.F16Memory), C.bool(mo.MLock), C.bool(mo.Embeddings), C.bool(mo.MMap), C.bool(mo.LowVRAM), C.bool(mo.VocabOnly), C.int(mo.NGPULayers), C.int(mo.NBatch), C.CString(mo.MainGPU), C.CString(mo.TensorSplit), C.bool(mo.NUMA))
|
||||
if ctx == nil {
|
||||
|
|
@ -94,24 +95,34 @@ func (l *LLama) Eval(text string, opts ...PredictOption) error {
|
|||
return fmt.Errorf("inference failed")
|
||||
}
|
||||
|
||||
fmt.Println("hi 4")
|
||||
|
||||
C.llama_free_params(params)
|
||||
|
||||
fmt.Println("hi 5")
|
||||
|
||||
return nil
|
||||
}
|
||||
|
||||
func (l *LLama) Predict(text string, opts ...PredictOption) (string, error) {
|
||||
po := NewPredictOptions(opts...)
|
||||
|
||||
fmt.Println("predict 1")
|
||||
|
||||
if po.TokenCallback != nil {
|
||||
setCallback(l.ctx, po.TokenCallback)
|
||||
}
|
||||
|
||||
fmt.Println("predict 2")
|
||||
|
||||
input := C.CString(text)
|
||||
if po.Tokens == 0 {
|
||||
po.Tokens = 99999999
|
||||
}
|
||||
out := make([]byte, po.Tokens)
|
||||
|
||||
fmt.Println("predict 3")
|
||||
|
||||
reverseCount := len(po.StopPrompts)
|
||||
reversePrompt := make([]*C.char, reverseCount)
|
||||
var pass **C.char
|
||||
|
|
@ -121,6 +132,8 @@ func (l *LLama) Predict(text string, opts ...PredictOption) (string, error) {
|
|||
pass = &reversePrompt[0]
|
||||
}
|
||||
|
||||
fmt.Println("predict 4")
|
||||
|
||||
params := C.llama_allocate_params(input, C.int(po.Seed), C.int(po.Threads), C.int(po.Tokens), C.int(po.TopK),
|
||||
C.float(po.TopP), C.float(po.Temperature), C.float(po.Penalty), C.int(po.Repeat),
|
||||
C.bool(po.IgnoreEOS), C.bool(po.F16KV),
|
||||
|
|
@ -131,12 +144,16 @@ func (l *LLama) Predict(text string, opts ...PredictOption) (string, error) {
|
|||
C.CString(po.MainGPU), C.CString(po.TensorSplit),
|
||||
C.bool(po.PromptCacheRO),
|
||||
)
|
||||
|
||||
fmt.Println("predict 4.5")
|
||||
ret := C.llama_predict(params, l.ctx, (*C.char)(unsafe.Pointer(&out[0])), C.bool(po.DebugMode))
|
||||
if ret != 0 {
|
||||
return "", fmt.Errorf("inference failed")
|
||||
}
|
||||
res := C.GoString((*C.char)(unsafe.Pointer(&out[0])))
|
||||
|
||||
fmt.Println("predict 5")
|
||||
|
||||
res = strings.TrimPrefix(res, " ")
|
||||
res = strings.TrimPrefix(res, text)
|
||||
res = strings.TrimPrefix(res, "\n")
|
||||
|
|
@ -145,6 +162,8 @@ func (l *LLama) Predict(text string, opts ...PredictOption) (string, error) {
|
|||
res = strings.TrimRight(res, s)
|
||||
}
|
||||
|
||||
fmt.Println("predict 6")
|
||||
|
||||
C.llama_free_params(params)
|
||||
|
||||
if po.TokenCallback != nil {
|
||||
|
|
|
|||
Loading…
Reference in a new issue