mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-27 06:39:25 +01:00
df270ef745
- Add `struct llama_sampler` and `struct llama_sampler_i` - Add `llama_sampler_` API - Add `llama_sampler_chain_` API for chaining multiple samplers - Remove `LLAMA_API_INTERNAL` - Add `llama_perf_` API and remove old `llama_print_timings` and `llama_reset_timings`
341 lines
12 KiB
C++
341 lines
12 KiB
C++
#include "ggml.h"
|
|
#include "log.h"
|
|
#include "common.h"
|
|
#include "clip.h"
|
|
#include "llava.h"
|
|
#include "llama.h"
|
|
|
|
#include "base64.hpp"
|
|
|
|
#include <cstdio>
|
|
#include <cstdlib>
|
|
#include <vector>
|
|
|
|
static 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))) {
|
|
LOG_TEE("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past);
|
|
return false;
|
|
}
|
|
*n_past += n_eval;
|
|
}
|
|
return true;
|
|
}
|
|
|
|
static 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);
|
|
}
|
|
|
|
static 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, true);
|
|
eval_tokens(ctx_llama, embd_inp, n_batch, n_past);
|
|
return true;
|
|
}
|
|
|
|
static const char * sample(struct gpt_sampler * smpl,
|
|
struct llama_context * ctx_llama,
|
|
int * n_past) {
|
|
const llama_token id = gpt_sampler_sample(smpl, ctx_llama, -1);
|
|
gpt_sampler_accept(smpl, id, true);
|
|
static std::string ret;
|
|
if (llama_token_is_eog(llama_get_model(ctx_llama), id)) {
|
|
ret = "</s>";
|
|
} else {
|
|
ret = llama_token_to_piece(ctx_llama, id);
|
|
}
|
|
eval_id(ctx_llama, id, n_past);
|
|
return ret.c_str();
|
|
}
|
|
|
|
static const char* IMG_BASE64_TAG_BEGIN = "<img src=\"data:image/jpeg;base64,";
|
|
static const char* IMG_BASE64_TAG_END = "\">";
|
|
|
|
static void find_image_tag_in_prompt(const std::string& prompt, size_t& begin_out, size_t& end_out) {
|
|
begin_out = prompt.find(IMG_BASE64_TAG_BEGIN);
|
|
end_out = prompt.find(IMG_BASE64_TAG_END, (begin_out == std::string::npos) ? 0UL : begin_out);
|
|
}
|
|
|
|
static bool prompt_contains_image(const std::string& prompt) {
|
|
size_t begin, end;
|
|
find_image_tag_in_prompt(prompt, begin, end);
|
|
return (begin != std::string::npos);
|
|
}
|
|
|
|
// replaces the base64 image tag in the prompt with `replacement`
|
|
static llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip_ctx * ctx_clip, int n_threads, const std::string& prompt) {
|
|
size_t img_base64_str_start, img_base64_str_end;
|
|
find_image_tag_in_prompt(prompt, img_base64_str_start, img_base64_str_end);
|
|
if (img_base64_str_start == std::string::npos || img_base64_str_end == std::string::npos) {
|
|
LOG_TEE("%s: invalid base64 image tag. must be %s<base64 byte string>%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END);
|
|
return NULL;
|
|
}
|
|
|
|
auto base64_bytes_start = img_base64_str_start + strlen(IMG_BASE64_TAG_BEGIN);
|
|
auto base64_bytes_count = img_base64_str_end - base64_bytes_start;
|
|
auto base64_str = prompt.substr(base64_bytes_start, base64_bytes_count );
|
|
|
|
auto required_bytes = base64::required_encode_size(base64_str.size());
|
|
auto img_bytes = std::vector<unsigned char>(required_bytes);
|
|
base64::decode(base64_str.begin(), base64_str.end(), img_bytes.begin());
|
|
|
|
auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, img_bytes.data(), img_bytes.size());
|
|
if (!embed) {
|
|
LOG_TEE("%s: could not load image from base64 string.\n", __func__);
|
|
return NULL;
|
|
}
|
|
|
|
return embed;
|
|
}
|
|
|
|
static std::string remove_image_from_prompt(const std::string& prompt, const char * replacement = "") {
|
|
size_t begin, end;
|
|
find_image_tag_in_prompt(prompt, begin, end);
|
|
if (begin == std::string::npos || end == std::string::npos) {
|
|
return prompt;
|
|
}
|
|
auto pre = prompt.substr(0, begin);
|
|
auto post = prompt.substr(end + strlen(IMG_BASE64_TAG_END));
|
|
return pre + replacement + post;
|
|
}
|
|
|
|
struct llava_context {
|
|
struct clip_ctx * ctx_clip = NULL;
|
|
struct llama_context * ctx_llama = NULL;
|
|
struct llama_model * model = NULL;
|
|
};
|
|
|
|
static void print_usage(int argc, char ** argv, const gpt_params & params) {
|
|
gpt_params_print_usage(argc, argv, params);
|
|
|
|
LOG_TEE("\n example usage:\n");
|
|
LOG_TEE("\n %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> --image <path/to/another/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
|
|
LOG_TEE("\n note: a lower temperature value like 0.1 is recommended for better quality.\n");
|
|
}
|
|
|
|
static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_params * params, const std::string & fname) {
|
|
|
|
// load and preprocess the image
|
|
llava_image_embed * embed = NULL;
|
|
auto prompt = params->prompt;
|
|
if (prompt_contains_image(prompt)) {
|
|
if (!params->image.empty()) {
|
|
LOG_TEE("using base64 encoded image instead of command line image path\n");
|
|
}
|
|
embed = llava_image_embed_make_with_prompt_base64(ctx_llava->ctx_clip, params->cpuparams.n_threads, prompt);
|
|
if (!embed) {
|
|
LOG_TEE("%s: can't load image from prompt\n", __func__);
|
|
return NULL;
|
|
}
|
|
params->prompt = remove_image_from_prompt(prompt);
|
|
} else {
|
|
embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->cpuparams.n_threads, fname.c_str());
|
|
if (!embed) {
|
|
fprintf(stderr, "%s: is %s really an image file?\n", __func__, fname.c_str());
|
|
return NULL;
|
|
}
|
|
}
|
|
|
|
return embed;
|
|
}
|
|
|
|
static void process_prompt(struct llava_context * ctx_llava, struct llava_image_embed * image_embed, gpt_params * params, const std::string & prompt) {
|
|
int n_past = 0;
|
|
|
|
const int max_tgt_len = params->n_predict < 0 ? 256 : params->n_predict;
|
|
|
|
std::string system_prompt, user_prompt;
|
|
size_t image_pos = prompt.find("<image>");
|
|
if (image_pos != std::string::npos) {
|
|
// new templating mode: Provide the full prompt including system message and use <image> as a placeholder for the image
|
|
system_prompt = prompt.substr(0, image_pos);
|
|
user_prompt = prompt.substr(image_pos + std::string("<image>").length());
|
|
LOG_TEE("system_prompt: %s\n", system_prompt.c_str());
|
|
if (params->verbose_prompt) {
|
|
auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, system_prompt, true, true);
|
|
for (int i = 0; i < (int) tmp.size(); i++) {
|
|
LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
|
|
}
|
|
}
|
|
LOG_TEE("user_prompt: %s\n", user_prompt.c_str());
|
|
if (params->verbose_prompt) {
|
|
auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
|
|
for (int i = 0; i < (int) tmp.size(); i++) {
|
|
LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
|
|
}
|
|
}
|
|
} else {
|
|
// llava-1.5 native mode
|
|
system_prompt = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:";
|
|
user_prompt = prompt + "\nASSISTANT:";
|
|
if (params->verbose_prompt) {
|
|
auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
|
|
for (int i = 0; i < (int) tmp.size(); i++) {
|
|
LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
|
|
}
|
|
}
|
|
}
|
|
|
|
eval_string(ctx_llava->ctx_llama, system_prompt.c_str(), params->n_batch, &n_past, true);
|
|
llava_eval_image_embed(ctx_llava->ctx_llama, image_embed, params->n_batch, &n_past);
|
|
eval_string(ctx_llava->ctx_llama, user_prompt.c_str(), params->n_batch, &n_past, false);
|
|
|
|
// generate the response
|
|
|
|
LOG_TEE("\n");
|
|
|
|
struct gpt_sampler * smpl = gpt_sampler_init(ctx_llava->model, params->sparams);
|
|
if (!smpl) {
|
|
fprintf(stderr, "%s: failed to initialize sampling subsystem\n", __func__);
|
|
exit(1);
|
|
}
|
|
|
|
std::string response = "";
|
|
for (int i = 0; i < max_tgt_len; i++) {
|
|
const char * tmp = sample(smpl, ctx_llava->ctx_llama, &n_past);
|
|
response += tmp;
|
|
if (strcmp(tmp, "</s>") == 0) break;
|
|
if (strstr(tmp, "###")) break; // Yi-VL behavior
|
|
printf("%s", tmp);
|
|
if (strstr(response.c_str(), "<|im_end|>")) break; // Yi-34B llava-1.6 - for some reason those decode not as the correct token (tokenizer works)
|
|
if (strstr(response.c_str(), "<|im_start|>")) break; // Yi-34B llava-1.6
|
|
if (strstr(response.c_str(), "USER:")) break; // mistral llava-1.6
|
|
|
|
fflush(stdout);
|
|
}
|
|
|
|
gpt_sampler_free(smpl);
|
|
printf("\n");
|
|
}
|
|
|
|
static struct llama_model * llava_init(gpt_params * params) {
|
|
llama_backend_init();
|
|
llama_numa_init(params->numa);
|
|
|
|
llama_model_params model_params = llama_model_params_from_gpt_params(*params);
|
|
|
|
llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params);
|
|
if (model == NULL) {
|
|
LOG_TEE("%s: error: unable to load model\n" , __func__);
|
|
return NULL;
|
|
}
|
|
return model;
|
|
}
|
|
|
|
static struct llava_context * llava_init_context(gpt_params * params, llama_model * model) {
|
|
const char * clip_path = params->mmproj.c_str();
|
|
|
|
auto prompt = params->prompt;
|
|
if (prompt.empty()) {
|
|
prompt = "describe the image in detail.";
|
|
}
|
|
|
|
auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1);
|
|
|
|
|
|
llama_context_params ctx_params = llama_context_params_from_gpt_params(*params);
|
|
ctx_params.n_ctx = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings
|
|
|
|
llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params);
|
|
|
|
if (ctx_llama == NULL) {
|
|
LOG_TEE("%s: error: failed to create the llama_context\n" , __func__);
|
|
return NULL;
|
|
}
|
|
|
|
auto ctx_llava = (struct llava_context *)malloc(sizeof(llava_context));
|
|
|
|
ctx_llava->ctx_llama = ctx_llama;
|
|
ctx_llava->ctx_clip = ctx_clip;
|
|
ctx_llava->model = model;
|
|
return ctx_llava;
|
|
}
|
|
|
|
static void llava_free(struct llava_context * ctx_llava) {
|
|
if (ctx_llava->ctx_clip) {
|
|
clip_free(ctx_llava->ctx_clip);
|
|
ctx_llava->ctx_clip = NULL;
|
|
}
|
|
|
|
llama_free(ctx_llava->ctx_llama);
|
|
llama_free_model(ctx_llava->model);
|
|
llama_backend_free();
|
|
}
|
|
|
|
static void llama_log_callback_logTee(ggml_log_level level, const char * text, void * user_data) {
|
|
(void) level;
|
|
(void) user_data;
|
|
LOG_TEE("%s", text);
|
|
}
|
|
|
|
int main(int argc, char ** argv) {
|
|
ggml_time_init();
|
|
|
|
gpt_params params;
|
|
|
|
if (!gpt_params_parse(argc, argv, params)) {
|
|
print_usage(argc, argv, params);
|
|
return 1;
|
|
}
|
|
|
|
#ifndef LOG_DISABLE_LOGS
|
|
log_set_target(log_filename_generator("llava", "log"));
|
|
LOG_TEE("Log start\n");
|
|
log_dump_cmdline(argc, argv);
|
|
llama_log_set(llama_log_callback_logTee, nullptr);
|
|
#endif // LOG_DISABLE_LOGS
|
|
|
|
if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) {
|
|
print_usage(argc, argv, {});
|
|
return 1;
|
|
}
|
|
auto model = llava_init(¶ms);
|
|
if (model == NULL) {
|
|
fprintf(stderr, "%s: error: failed to init llava model\n", __func__);
|
|
return 1;
|
|
}
|
|
|
|
if (prompt_contains_image(params.prompt)) {
|
|
auto ctx_llava = llava_init_context(¶ms, model);
|
|
|
|
auto image_embed = load_image(ctx_llava, ¶ms, "");
|
|
|
|
// process the prompt
|
|
process_prompt(ctx_llava, image_embed, ¶ms, params.prompt);
|
|
|
|
llama_perf_print(ctx_llava->ctx_llama, LLAMA_PERF_TYPE_CONTEXT);
|
|
llava_image_embed_free(image_embed);
|
|
ctx_llava->model = NULL;
|
|
llava_free(ctx_llava);
|
|
} else {
|
|
for (auto & image : params.image) {
|
|
auto ctx_llava = llava_init_context(¶ms, model);
|
|
|
|
auto image_embed = load_image(ctx_llava, ¶ms, image);
|
|
if (!image_embed) {
|
|
std::cerr << "error: failed to load image " << image << ". Terminating\n\n";
|
|
return 1;
|
|
}
|
|
|
|
// process the prompt
|
|
process_prompt(ctx_llava, image_embed, ¶ms, params.prompt);
|
|
|
|
llama_perf_print(ctx_llava->ctx_llama, LLAMA_PERF_TYPE_CONTEXT);
|
|
llava_image_embed_free(image_embed);
|
|
ctx_llava->model = NULL;
|
|
llava_free(ctx_llava);
|
|
}
|
|
}
|
|
|
|
llama_free_model(model);
|
|
|
|
return 0;
|
|
}
|