llama.cpp/examples/llava/llava-utils.h
Georgi Gerganov 0e89203b51
speculative : add tree-based sampling example (#3624)
* sampling : one sequence per sampling context

ggml-ci

* speculative : add tree-based sampling support

ggml-ci

* speculative : reuse the n_parallel CLI param

* speculative : refactor sampling

* examples : fix build after sampling refactoring

ggml-ci

* batched : fix n_seq_id

* sampling : fix malloc

ggml-ci

* swift : fix build

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* swift : try to fix build

ggml-ci

* prompts : add assistant.txt

* common : add llama_batch_add() and llama_batch_clear() helpers

* speculative : minor refactor

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* minor : comments + rename

ggml-ci

* speculative : fix off-by-one for n_drafted

* speculative : fix the n_drafted fix + p constants
2023-10-18 16:21:57 +03:00

146 lines
6.1 KiB
C++

#pragma once
// this one and clip lib will be eventually merged to a single lib, let's keep it this way for now
#include "common.h"
#include "llama.h"
#include <cstdio>
#include <cstdlib>
#include <vector>
inline bool eval_image_embd(llama_context * ctx_llama, float * embd, int N, int n_batch, int * n_past) {
int n_embd = llama_n_embd(llama_get_model(ctx_llama));
for (int i = 0; i < N; i += n_batch) {
int n_eval = N - i;
if (n_eval > n_batch) {
n_eval = n_batch;
}
llama_batch batch = {int32_t(n_eval), nullptr, (embd+i*n_embd), nullptr, nullptr, nullptr, nullptr, *n_past, 1, 0, };
if (llama_decode(ctx_llama, batch)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return false;
}
*n_past += n_eval;
}
return true;
}
inline bool eval_tokens(struct llama_context * ctx_llama, std::vector<llama_token> tokens, int n_batch, int * n_past) {
int N = (int) tokens.size();
for (int i = 0; i < N; i += n_batch) {
int n_eval = (int) tokens.size() - i;
if (n_eval > n_batch) {
n_eval = n_batch;
}
if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval, *n_past, 0))) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return false;
}
*n_past += n_eval;
}
return true;
}
inline bool eval_id(struct llama_context * ctx_llama, int id, int * n_past) {
std::vector<llama_token> tokens;
tokens.push_back(id);
return eval_tokens(ctx_llama, tokens, 1, n_past);
}
inline bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, bool add_bos){
std::string str2 = str;
std::vector<llama_token> embd_inp = ::llama_tokenize(ctx_llama, str2, add_bos);
eval_tokens(ctx_llama, embd_inp, n_batch, n_past);
return true;
}
// TODO: use common/sampling.h
inline llama_token sample_id(llama_context * ctx_llama, gpt_params & params) {
// out of user input, sample next token
const float temp = params.sampling_params.temp;
const int32_t top_k = params.sampling_params.top_k <= 0 ? llama_n_vocab(llama_get_model(ctx_llama)) : params.sampling_params.top_k;
const float top_p = params.sampling_params.top_p;
const float tfs_z = params.sampling_params.tfs_z;
const float typical_p = params.sampling_params.typical_p;
// const int32_t repeat_last_n = params.sampling_params.repeat_last_n < 0 ? n_ctx : params.sampling_params.repeat_last_n;
// const float repeat_penalty = params.sampling_params.repeat_penalty;
// const float alpha_presence = params.sampling_params.presence_penalty;
// const float alpha_frequency = params.sampling_params.frequency_penalty;
const int mirostat = params.sampling_params.mirostat;
const float mirostat_tau = params.sampling_params.mirostat_tau;
const float mirostat_eta = params.sampling_params.mirostat_eta;
// const bool penalize_nl = params.sampling_params.penalize_nl;
llama_token id = 0;
{
auto logits = llama_get_logits(ctx_llama);
auto n_vocab = llama_n_vocab(llama_get_model(ctx_llama));
// Apply params.logit_bias map
for (auto it = params.sampling_params.logit_bias.begin(); it != params.sampling_params.logit_bias.end(); it++) {
logits[it->first] += it->second;
}
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
}
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
// TODO: Apply penalties
// float nl_logit = logits[llama_token_nl(ctx)];
// auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx);
// llama_sample_repetition_penalty(ctx, &candidates_p,
// last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
// last_n_repeat, repeat_penalty);
// llama_sample_frequency_and_presence_penalties(ctx, &candidates_p,
// last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
// last_n_repeat, alpha_frequency, alpha_presence);
// if (!penalize_nl) {
// logits[llama_token_nl(ctx)] = nl_logit;
// }
if (temp <= 0) {
// Greedy sampling
id = llama_sample_token_greedy(ctx_llama, &candidates_p);
} else {
if (mirostat == 1) {
static float mirostat_mu = 2.0f * mirostat_tau;
const int mirostat_m = 100;
llama_sample_temp(ctx_llama, &candidates_p, temp);
id = llama_sample_token_mirostat(ctx_llama, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
} else if (mirostat == 2) {
static float mirostat_mu = 2.0f * mirostat_tau;
llama_sample_temp(ctx_llama, &candidates_p, temp);
id = llama_sample_token_mirostat_v2(ctx_llama, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);
} else {
// Temperature sampling
llama_sample_top_k(ctx_llama, &candidates_p, top_k, 1);
llama_sample_tail_free(ctx_llama, &candidates_p, tfs_z, 1);
llama_sample_typical(ctx_llama, &candidates_p, typical_p, 1);
llama_sample_top_p(ctx_llama, &candidates_p, top_p, 1);
llama_sample_temp(ctx_llama, &candidates_p, temp);
id = llama_sample_token(ctx_llama, &candidates_p);
}
}
}
return id;
}
inline const char * sample(struct llama_context * ctx_llama, gpt_params & params, int * n_past) {
int id = sample_id(ctx_llama, params);
static std::string ret;
if (id == llama_token_eos(ctx_llama)) {
ret = "</s>";
} else {
ret = llama_token_to_piece(ctx_llama, id);
}
eval_id(ctx_llama, id, n_past);
return ret.c_str();
}