#include "arg.h"
#include "common.h"
#include "log.h"
#include "llama.h"
#include <algorithm>
#include <clocale>
#include <fstream>
#include <iostream> // TODO: remove me
static void print_usage(int, char ** argv) {
LOG("\nexample usage:\n");
LOG("\n %s --model ./models/bge-base-en-v1.5-f16.gguf --top-k 3 --context-file README.md --context-file License --chunk-size 100 --chunk-separator .\n", argv[0]);
LOG("\n");
}
struct chunk {
// filename
std::string filename;
// original file position
size_t filepos;
// original text data
std::string textdata;
// tokenized text data
std::vector<llama_token> tokens;
// embedding
std::vector<float> embedding;
};
// chunk file data to chunks of size >= chunk_size
// chunk_separator is the separator between chunks
static std::vector<chunk> chunk_file(const std::string & filename, int chunk_size, const std::string & chunk_separator) {
std::vector<chunk> chunks;
std::ifstream f(filename.c_str());
if (!f.is_open()) {
LOG_ERR("could not open file %s\n", filename.c_str());
return chunks;
}
chunk current_chunk;
char buffer[1024];
int64_t filepos = 0;
std::string current;
while (f.read(buffer, 1024)) {
current += std::string(buffer, f.gcount());
size_t pos;
while ((pos = current.find(chunk_separator)) != std::string::npos) {
current_chunk.textdata += current.substr(0, pos + chunk_separator.size());
if ((int) current_chunk.textdata.size() > chunk_size) {
// save chunk
current_chunk.filepos = filepos;
current_chunk.filename = filename;
chunks.push_back(current_chunk);
// update filepos
filepos += (int) current_chunk.textdata.size();
// reset current_chunk
current_chunk = chunk();
}
current = current.substr(pos + chunk_separator.size());
}
}
// add leftover data to last chunk
if (current_chunk.textdata.size() > 0) {
if (chunks.empty()) {
current_chunk.filepos = filepos;
current_chunk.filename = filename;
chunks.push_back(current_chunk);
} else {
chunks.back().textdata += current_chunk.textdata;
}
}
f.close();
return chunks;
}
static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, llama_seq_id seq_id) {
size_t n_tokens = tokens.size();
for (size_t i = 0; i < n_tokens; i++) {
common_batch_add(batch, tokens[i], i, { seq_id }, true);
}
}
static void batch_process(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd) {
// clear previous kv_cache values (irrelevant for embeddings)
llama_memory_clear(llama_get_memory(ctx), false);
// run model
LOG_INF("%s: n_tokens = %d, n_seq = %d\n", __func__, batch.n_tokens, n_seq);
if (llama_decode(ctx, batch) < 0) {
LOG_ERR("%s : failed to process\n", __func__);
}
for (int i = 0; i < batch.n_tokens; i++) {
if (!batch.logits[i]) {
continue;
}
// try to get sequence embeddings - supported only when pooling_type is not NONE
const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
if (embd == NULL) {
embd = llama_get_embeddings_ith(ctx, i);
if (embd == NULL) {
LOG_ERR("%s: failed to get embeddings for token %d\n", __func__, i);
continue;
}
}
float * out = output + batch.seq_id[i][0] * n_embd;
common_embd_normalize(embd, out, n_embd, 2);
}
}
int main(int argc, char ** argv) {
std::setlocale(LC_NUMERIC, "C");
common_params params;
common_init();
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_RETRIEVAL, print_usage)) {
return 1;
}
// For BERT models, batch size must be equal to ubatch size
params.n_ubatch = params.n_batch;
params.embedding = true;
if (params.chunk_size <= 0) {
LOG_ERR("chunk_size must be positive\n");
return 1;
}
if (params.context_files.empty()) {
LOG_ERR("context_files must be specified\n");
return 1;
}
LOG_INF("processing files:\n");
for (auto & context_file : params.context_files) {
LOG_INF("%s\n", context_file.c_str());
}
std::vector<chunk> chunks;
for (auto & context_file : params.context_files) {
std::vector<chunk> file_chunk = chunk_file(context_file, params.chunk_size, params.chunk_separator);
chunks.insert(chunks.end(), file_chunk.begin(), file_chunk.end());
}
LOG_INF("Number of chunks: %zu\n", chunks.size());
llama_backend_init();
llama_numa_init(params.numa);
// load the model
auto llama_init = common_init_from_params(params);
auto * model = llama_init->model();
auto * ctx = llama_init->context();
if (model == NULL) {
LOG_ERR("%s: unable to load model\n", __func__);
return 1;
}
const llama_vocab * vocab = llama_model_get_vocab(model);
const int n_ctx_train = llama_model_n_ctx_train(model);
const int n_ctx = llama_n_ctx(ctx);
const enum llama_pooling_type pooling_type = llama_pooling_type(ctx);
if (pooling_type == LLAMA_POOLING_TYPE_NONE) {
LOG_ERR("%s: pooling type NONE not supported\n", __func__);
return 1;
}
if (n_ctx > n_ctx_train) {
LOG_WRN("%s: warning: model was trained on only %d context tokens (%d specified)\n",
__func__, n_ctx_train, n_ctx);
}
// print system information
{
LOG_INF("\n");
LOG_INF("%s\n", common_params_get_system_info(params).c_str());
}
// max batch size
const uint64_t n_batch = params.n_batch;
GGML_ASSERT(params.n_batch >= params.n_ctx);
// tokenize the prompts and trim
for (auto & chunk : chunks) {
auto inp = common_tokenize(ctx, chunk.textdata, true, false);
if (inp.size() > n_batch) {
LOG_ERR("%s: chunk size (%lld) exceeds batch size (%lld), increase batch size and re-run\n",
__func__, (long long int) inp.size(), (long long int) n_batch);
return 1;
}
// add eos if not present
if (llama_vocab_eos(vocab) >= 0 && (inp.empty() || inp.back() != llama_vocab_eos(vocab))) {
inp.push_back(llama_vocab_eos(vocab));
}
chunk.tokens = inp;
}
// tokenization stats
if (params.verbose_prompt) {
for (int i = 0; i < (int) chunks.size(); i++) {
LOG_INF("%s: prompt %d: '%s'\n", __func__, i, chunks[i].textdata.c_str());
LOG_INF("%s: number of tokens in prompt = %zu\n", __func__, chunks[i].tokens.size());
for (int j = 0; j < (int) chunks[i].tokens.size(); j++) {
LOG_INF("%6d -> '%s'\n", chunks[i].tokens[j], common_token_to_piece(ctx, chunks[i].tokens[j]).c_str());
}
LOG_INF("\n\n");
}
}
// initialize batch
const int n_chunks = chunks.size();
struct llama_batch batch = llama_batch_init(n_batch, 0, 1);
// allocate output
const int n_embd_out = llama_model_n_embd_out(model);
std::vector<float> embeddings(n_chunks * n_embd_out, 0);
float * emb = embeddings.data();
// break into batches
unsigned int p = 0; // number of prompts processed already
unsigned int s = 0; // number of prompts in current batch
for (int k = 0; k < n_chunks; k++) {
// clamp to n_batch tokens
auto & inp = chunks[k].tokens;
const uint64_t n_toks = inp.size();
// encode if at capacity
if (batch.n_tokens + n_toks > n_batch || s >= llama_n_seq_max(ctx)) {
float * out = emb + p * n_embd_out;
batch_process(ctx, batch, out, s, n_embd_out);
common_batch_clear(batch);
p += s;
s = 0;
}
// add to batch
batch_add_seq(batch, inp, s);
s += 1;
}
// final batch
float * out = emb + p * n_embd_out;
batch_process(ctx, batch, out, s, n_embd_out);
// save embeddings to chunks
for (int i = 0; i < n_chunks; i++) {
chunks[i].embedding = std::vector<float>(emb + i * n_embd_out, emb + (i + 1) * n_embd_out);
// clear tokens as they are no longer needed
chunks[i].tokens.clear();
}
struct llama_batch query_batch = llama_batch_init(n_batch, 0, 1);
// start loop, receive query and return top k similar chunks based on cosine similarity
std::string query;
while (true) {
LOG("Enter query: ");
std::getline(std::cin, query);
std::vector<int32_t> query_tokens = common_tokenize(ctx, query, true);
batch_add_seq(query_batch, query_tokens, 0);
std::vector<float> query_emb(n_embd_out, 0);
batch_process(ctx, query_batch, query_emb.data(), 1, n_embd_out);
common_batch_clear(query_batch);
// compute cosine similarities
{
std::vector<std::pair<int, float>> similarities;
for (int i = 0; i < n_chunks; i++) {
float sim = common_embd_similarity_cos(chunks[i].embedding.data(), query_emb.data(), n_embd_out);
similarities.push_back(std::make_pair(i, sim));
}
// sort similarities
std::sort(similarities.begin(), similarities.end(), [](const std::pair<int, float> & a, const std::pair<int, float> & b) {
return a.second > b.second;
});
LOG("Top %d similar chunks:\n", params.sampling.top_k);
for (int i = 0; i < std::min(params.sampling.top_k, (int) chunks.size()); i++) {
LOG("filename: %s\n", chunks[similarities[i].first].filename.c_str());
LOG("filepos: %lld\n", (long long int) chunks[similarities[i].first].filepos);
LOG("similarity: %f\n", similarities[i].second);
LOG("textdata:\n%s\n", chunks[similarities[i].first].textdata.c_str());
LOG("--------------------\n");
}
}
}
LOG("\n");
llama_perf_context_print(ctx);
// clean up
llama_batch_free(query_batch);
llama_backend_free();
}