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Diffstat (limited to 'examples/llava/llava-utils.h')
| -rw-r--r-- | examples/llava/llava-utils.h | 145 |
1 files changed, 145 insertions, 0 deletions
diff --git a/examples/llava/llava-utils.h b/examples/llava/llava-utils.h new file mode 100644 index 00000000..79e237c8 --- /dev/null +++ b/examples/llava/llava-utils.h @@ -0,0 +1,145 @@ +#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, *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){ + std::string str2 = str; + std::vector<llama_token> embd_inp = ::llama_tokenize(ctx_llama, str2, true); + 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(); +} |