diff options
author | ningshanwutuobang <ningshanwutuobang@gmail.com> | 2023-06-28 23:53:37 +0800 |
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committer | GitHub <noreply@github.com> | 2023-06-28 18:53:37 +0300 |
commit | cfa0750bc9dbc2d957a91b8ed09ab0035d8f3d4e (patch) | |
tree | c8d6d6e6548d4f03899704f64bce6939e471e4e6 /examples/embd-input/embd-input-lib.cpp | |
parent | 9d23589d638dc74577d5ff880e6d4248b795f12e (diff) |
llama : support input embeddings directly (#1910)
* add interface for float input
* fixed inpL shape and type
* add examples of input floats
* add test example for embd input
* fixed sampling
* add free for context
* fixed add end condition for generating
* add examples for llava.py
* add READMD for llava.py
* add READMD for llava.py
* add example of PandaGPT
* refactor the interface and fixed the styles
* add cmake build for embd-input
* add cmake build for embd-input
* Add MiniGPT-4 example
* change the order of the args of llama_eval_internal
* fix ci error
Diffstat (limited to 'examples/embd-input/embd-input-lib.cpp')
-rw-r--r-- | examples/embd-input/embd-input-lib.cpp | 220 |
1 files changed, 220 insertions, 0 deletions
diff --git a/examples/embd-input/embd-input-lib.cpp b/examples/embd-input/embd-input-lib.cpp new file mode 100644 index 0000000..37de52a --- /dev/null +++ b/examples/embd-input/embd-input-lib.cpp @@ -0,0 +1,220 @@ +// Defines sigaction on msys: +#ifndef _GNU_SOURCE +#define _GNU_SOURCE +#endif + +#include "embd-input.h" + +#include <cassert> +#include <cinttypes> +#include <cmath> +#include <cstdio> +#include <cstring> +#include <ctime> +#include <fstream> +#include <iostream> +#include <string> +#include <vector> + +static llama_context ** g_ctx; + +extern "C" { + +struct MyModel* create_mymodel(int argc, char ** argv) { + gpt_params params; + + if (gpt_params_parse(argc, argv, params) == false) { + return nullptr; + } + + fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT); + + if (params.seed < 0) { + params.seed = time(NULL); + } + fprintf(stderr, "%s: seed = %d\n", __func__, params.seed); + + llama_init_backend(params.numa); + + llama_model * model; + llama_context * ctx; + + g_ctx = &ctx; + + // load the model and apply lora adapter, if any + std::tie(model, ctx) = llama_init_from_gpt_params(params); + if (model == NULL) { + fprintf(stderr, "%s: error: unable to load model\n", __func__); + return nullptr; + } + + // print system information + { + fprintf(stderr, "\n"); + fprintf(stderr, "system_info: n_threads = %d / %d | %s\n", + params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info()); + } + struct MyModel * ret = new MyModel(); + ret->ctx = ctx; + ret->params = params; + ret->n_past = 0; + // printf("ctx: %d\n", ret->ctx); + return ret; +} + +void free_mymodel(struct MyModel * mymodel) { + llama_context * ctx = mymodel->ctx; + llama_print_timings(ctx); + llama_free(ctx); + delete mymodel; +} + + +bool eval_float(void * model, float * input, int N){ + MyModel * mymodel = (MyModel*)model; + llama_context * ctx = mymodel->ctx; + gpt_params params = mymodel->params; + int n_emb = llama_n_embd(ctx); + int n_past = mymodel->n_past; + int n_batch = N; // params.n_batch; + + for (int i = 0; i < (int) N; i += n_batch) { + int n_eval = (int) N - i; + if (n_eval > n_batch) { + n_eval = n_batch; + } + if (llama_eval_embd(ctx, (input+i*n_emb), n_eval, n_past, params.n_threads)) { + fprintf(stderr, "%s : failed to eval\n", __func__); + return false; + } + n_past += n_eval; + } + mymodel->n_past = n_past; + return true; +} + +bool eval_tokens(void * model, std::vector<llama_token> tokens) { + MyModel * mymodel = (MyModel* )model; + llama_context * ctx; + ctx = mymodel->ctx; + gpt_params params = mymodel->params; + int n_past = mymodel->n_past; + for (int i = 0; i < (int) tokens.size(); i += params.n_batch) { + int n_eval = (int) tokens.size() - i; + if (n_eval > params.n_batch) { + n_eval = params.n_batch; + } + if (llama_eval(ctx, &tokens[i], n_eval, n_past, params.n_threads)) { + fprintf(stderr, "%s : failed to eval\n", __func__); + return false; + } + n_past += n_eval; + } + mymodel->n_past = n_past; + return true; +} + +bool eval_id(struct MyModel* mymodel, int id) { + std::vector<llama_token> tokens; + tokens.push_back(id); + return eval_tokens(mymodel, tokens); +} + +bool eval_string(struct MyModel * mymodel,const char* str){ + llama_context * ctx = mymodel->ctx; + std::string str2 = str; + std::vector<llama_token> embd_inp = ::llama_tokenize(ctx, str2, true); + eval_tokens(mymodel, embd_inp); + return true; +} + +llama_token sampling_id(struct MyModel* mymodel) { + llama_context* ctx = mymodel->ctx; + gpt_params params = mymodel->params; + // int n_ctx = llama_n_ctx(ctx); + + // out of user input, sample next token + const float temp = params.temp; + const int32_t top_k = params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k; + const float top_p = params.top_p; + const float tfs_z = params.tfs_z; + const float typical_p = params.typical_p; + // const int32_t repeat_last_n = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n; + // const float repeat_penalty = params.repeat_penalty; + // const float alpha_presence = params.presence_penalty; + // const float alpha_frequency = params.frequency_penalty; + const int mirostat = params.mirostat; + const float mirostat_tau = params.mirostat_tau; + const float mirostat_eta = params.mirostat_eta; + // const bool penalize_nl = params.penalize_nl; + + llama_token id = 0; + { + auto logits = llama_get_logits(ctx); + auto n_vocab = llama_n_vocab(ctx); + + // Apply params.logit_bias map + for (auto it = params.logit_bias.begin(); it != 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()]; + // 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()] = nl_logit; + // } + + if (temp <= 0) { + // Greedy sampling + id = llama_sample_token_greedy(ctx, &candidates_p); + } else { + if (mirostat == 1) { + static float mirostat_mu = 2.0f * mirostat_tau; + const int mirostat_m = 100; + llama_sample_temperature(ctx, &candidates_p, temp); + id = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu); + } else if (mirostat == 2) { + static float mirostat_mu = 2.0f * mirostat_tau; + llama_sample_temperature(ctx, &candidates_p, temp); + id = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu); + } else { + // Temperature sampling + llama_sample_top_k(ctx, &candidates_p, top_k, 1); + llama_sample_tail_free(ctx, &candidates_p, tfs_z, 1); + llama_sample_typical(ctx, &candidates_p, typical_p, 1); + llama_sample_top_p(ctx, &candidates_p, top_p, 1); + llama_sample_temperature(ctx, &candidates_p, temp); + id = llama_sample_token(ctx, &candidates_p); + } + } + } + + return id; +} + +const char * sampling(struct MyModel * mymodel) { + llama_context * ctx = mymodel->ctx; + int id = sampling_id(mymodel); + std::string ret; + if (id == llama_token_eos()) ret = "</s>"; + else ret = llama_token_to_str(ctx, id); + eval_id(mymodel, id); + return ret.c_str(); +} + +} |