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This commit is contained in:
M. Yusuf Sarıgöz
parent
54495c9474
commit
9b0ec4d2cc
2 changed files with 142 additions and 142 deletions
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@ -1,141 +1,141 @@
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// this one and clip lib will be eventually merged to a single lib, let's keep it this way for now
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#include <stdio.h>
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#include <stdlib.h>
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#include <vector>
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#include "common.h"
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#include "llama.h"
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bool eval_image_embd(llama_context * ctx_llama, float * embd, int N, int n_batch, int * n_past) {
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int n_embd = llama_n_embd(llama_get_model(ctx_llama));
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for (int i = 0; i < N; i += n_batch) {
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int n_eval = N - i;
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if (n_eval > n_batch) {
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n_eval = n_batch;
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}
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llama_batch batch = {int32_t(n_eval), nullptr, (embd+i*n_embd), nullptr, nullptr, nullptr, *n_past, 1, 0, };
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if (llama_decode(ctx_llama, batch)) {
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fprintf(stderr, "%s : failed to eval\n", __func__);
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return false;
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}
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*n_past += n_eval;
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}
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return true;
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}
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bool eval_tokens(struct llama_context * ctx_llama, std::vector<llama_token> tokens, int n_batch, int * n_past) {
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int N = (int) tokens.size();
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for (int i = 0; i < N; i += n_batch) {
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int n_eval = (int) tokens.size() - i;
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if (n_eval > n_batch) {
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n_eval = n_batch;
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}
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if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval, *n_past, 0))) {
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fprintf(stderr, "%s : failed to eval\n", __func__);
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return false;
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}
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*n_past += n_eval;
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}
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return true;
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}
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bool eval_id(struct llama_context * ctx_llama, int id, int * n_past) {
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std::vector<llama_token> tokens;
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tokens.push_back(id);
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return eval_tokens(ctx_llama, tokens, 1, n_past);
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}
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bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past){
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std::string str2 = str;
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std::vector<llama_token> embd_inp = ::llama_tokenize(ctx_llama, str2, true);
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eval_tokens(ctx_llama, embd_inp, n_batch, n_past);
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return true;
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}
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llama_token sample_id(llama_context * ctx_llama, gpt_params & params) {
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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 = params.top_k <= 0 ? llama_n_vocab(llama_get_model(ctx_llama)) : 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 = 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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llama_token id = 0;
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{
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auto logits = llama_get_logits(ctx_llama);
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auto n_vocab = llama_n_vocab(llama_get_model(ctx_llama));
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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(); 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(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(), candidates.size(), false };
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// TODO: Apply penalties
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// float nl_logit = logits[llama_token_nl(ctx)];
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// auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx);
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// llama_sample_repetition_penalty(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(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(ctx)] = 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_llama, &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_temp(ctx_llama, &candidates_p, temp);
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id = llama_sample_token_mirostat(ctx_llama, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &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_temp(ctx_llama, &candidates_p, temp);
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id = llama_sample_token_mirostat_v2(ctx_llama, &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_llama, &candidates_p, top_k, 1);
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llama_sample_tail_free(ctx_llama, &candidates_p, tfs_z, 1);
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llama_sample_typical(ctx_llama, &candidates_p, typical_p, 1);
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llama_sample_top_p(ctx_llama, &candidates_p, top_p, 1);
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llama_sample_temp(ctx_llama, &candidates_p, temp);
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id = llama_sample_token(ctx_llama, &candidates_p);
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}
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}
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}
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return id;
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}
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const char * sample(struct llama_context * ctx_llama, gpt_params & params, int * n_past) {
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int id = sample_id(ctx_llama, params);
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static std::string ret;
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if (id == llama_token_eos(ctx_llama)) {
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ret = "</s>";
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} else {
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ret = llama_token_to_piece(ctx_llama, id);
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}
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eval_id(ctx_llama, id, n_past);
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return ret.c_str();
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}
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// this one and clip lib will be eventually merged to a single lib, let's keep it this way for now
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#include <stdio.h>
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#include <stdlib.h>
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#include <vector>
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#include "common.h"
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#include "llama.h"
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bool eval_image_embd(llama_context * ctx_llama, float * embd, int N, int n_batch, int * n_past) {
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int n_embd = llama_n_embd(llama_get_model(ctx_llama));
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for (int i = 0; i < N; i += n_batch) {
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int n_eval = N - i;
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if (n_eval > n_batch) {
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n_eval = n_batch;
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}
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llama_batch batch = {int32_t(n_eval), nullptr, (embd+i*n_embd), nullptr, nullptr, nullptr, *n_past, 1, 0, };
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if (llama_decode(ctx_llama, batch)) {
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fprintf(stderr, "%s : failed to eval\n", __func__);
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return false;
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}
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*n_past += n_eval;
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}
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return true;
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}
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bool eval_tokens(struct llama_context * ctx_llama, std::vector<llama_token> tokens, int n_batch, int * n_past) {
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int N = (int) tokens.size();
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for (int i = 0; i < N; i += n_batch) {
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int n_eval = (int) tokens.size() - i;
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if (n_eval > n_batch) {
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n_eval = n_batch;
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}
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if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval, *n_past, 0))) {
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fprintf(stderr, "%s : failed to eval\n", __func__);
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return false;
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}
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*n_past += n_eval;
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}
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return true;
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}
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bool eval_id(struct llama_context * ctx_llama, int id, int * n_past) {
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std::vector<llama_token> tokens;
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tokens.push_back(id);
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return eval_tokens(ctx_llama, tokens, 1, n_past);
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}
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bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past){
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std::string str2 = str;
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std::vector<llama_token> embd_inp = ::llama_tokenize(ctx_llama, str2, true);
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eval_tokens(ctx_llama, embd_inp, n_batch, n_past);
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return true;
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}
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llama_token sample_id(llama_context * ctx_llama, gpt_params & params) {
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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 = params.top_k <= 0 ? llama_n_vocab(llama_get_model(ctx_llama)) : 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 = 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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llama_token id = 0;
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{
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auto logits = llama_get_logits(ctx_llama);
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auto n_vocab = llama_n_vocab(llama_get_model(ctx_llama));
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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(); 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(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(), candidates.size(), false };
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// TODO: Apply penalties
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// float nl_logit = logits[llama_token_nl(ctx)];
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// auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx);
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// llama_sample_repetition_penalty(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(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(ctx)] = 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_llama, &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_temp(ctx_llama, &candidates_p, temp);
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id = llama_sample_token_mirostat(ctx_llama, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &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_temp(ctx_llama, &candidates_p, temp);
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id = llama_sample_token_mirostat_v2(ctx_llama, &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_llama, &candidates_p, top_k, 1);
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llama_sample_tail_free(ctx_llama, &candidates_p, tfs_z, 1);
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llama_sample_typical(ctx_llama, &candidates_p, typical_p, 1);
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llama_sample_top_p(ctx_llama, &candidates_p, top_p, 1);
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llama_sample_temp(ctx_llama, &candidates_p, temp);
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id = llama_sample_token(ctx_llama, &candidates_p);
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}
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}
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}
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return id;
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}
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const char * sample(struct llama_context * ctx_llama, gpt_params & params, int * n_past) {
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int id = sample_id(ctx_llama, params);
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static std::string ret;
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if (id == llama_token_eos(ctx_llama)) {
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ret = "</s>";
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} else {
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ret = llama_token_to_piece(ctx_llama, id);
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}
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eval_id(ctx_llama, id, n_past);
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return ret.c_str();
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}
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@ -35,7 +35,7 @@ int main(int argc, char ** argv) {
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clip_image_f32 img_res;
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clip_image_load_from_file(img_path, &img);
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clip_image_preprocess(ctx_clip, &img, &img_res);
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float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip));
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if (!image_embd) {
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fprintf(stderr, "Unable to allocate memory for CLIP embeddings\n");
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