mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-25 19:04:35 +00:00
1442677f92
* common : gpt_params_parse do not print usage * common : rework usage print (wip) * common : valign * common : rework print_usage * infill : remove cfg support * common : reorder args * server : deduplicate parameters ggml-ci * common : add missing header ggml-ci * common : remote --random-prompt usages ggml-ci * examples : migrate to gpt_params ggml-ci * batched-bench : migrate to gpt_params * retrieval : migrate to gpt_params * common : change defaults for escape and n_ctx * common : remove chatml and instruct params ggml-ci * common : passkey use gpt_params
293 lines
9.8 KiB
C++
293 lines
9.8 KiB
C++
#include "common.h"
|
|
#include "llama.h"
|
|
|
|
#include <algorithm>
|
|
#include <fstream>
|
|
|
|
static void print_usage(int argc, char ** argv, const gpt_params & params) {
|
|
gpt_params_print_usage(argc, argv, params);
|
|
|
|
LOG_TEE("\nexample usage:\n");
|
|
LOG_TEE("\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_TEE("\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()) {
|
|
fprintf(stderr, "Error: 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, int seq_id) {
|
|
for (size_t i = 0; i < tokens.size(); i++) {
|
|
llama_batch_add(batch, tokens[i], i, { seq_id }, i == tokens.size() - 1);
|
|
}
|
|
}
|
|
|
|
static void batch_decode(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd) {
|
|
// clear previous kv_cache values (irrelevant for embeddings)
|
|
llama_kv_cache_clear(ctx);
|
|
|
|
// run model
|
|
fprintf(stderr, "%s: n_tokens = %d, n_seq = %d\n", __func__, batch.n_tokens, n_seq);
|
|
if (llama_decode(ctx, batch) < 0) {
|
|
fprintf(stderr, "%s : failed to decode\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) {
|
|
fprintf(stderr, "%s: failed to get embeddings for token %d\n", __func__, i);
|
|
continue;
|
|
}
|
|
}
|
|
|
|
float * out = output + batch.seq_id[i][0] * n_embd;
|
|
llama_embd_normalize(embd, out, n_embd);
|
|
}
|
|
}
|
|
|
|
int main(int argc, char ** argv) {
|
|
gpt_params params;
|
|
|
|
if (!gpt_params_parse(argc, argv, params)) {
|
|
print_usage(argc, argv, params);
|
|
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) {
|
|
fprintf(stderr, "chunk_size must be positive\n");
|
|
return 1;
|
|
}
|
|
if (params.context_files.empty()) {
|
|
fprintf(stderr, "context_files must be specified\n");
|
|
return 1;
|
|
}
|
|
|
|
print_build_info();
|
|
|
|
printf("processing files:\n");
|
|
for (auto & context_file : params.context_files) {
|
|
printf("%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());
|
|
}
|
|
printf("Number of chunks: %ld\n", chunks.size());
|
|
|
|
llama_backend_init();
|
|
llama_numa_init(params.numa);
|
|
|
|
llama_model * model;
|
|
llama_context * ctx;
|
|
|
|
// load the model
|
|
std::tie(model, ctx) = llama_init_from_gpt_params(params);
|
|
if (model == NULL) {
|
|
fprintf(stderr, "%s: error: unable to load model\n", __func__);
|
|
return 1;
|
|
}
|
|
|
|
const int n_ctx_train = llama_n_ctx_train(model);
|
|
const int n_ctx = llama_n_ctx(ctx);
|
|
|
|
if (n_ctx > n_ctx_train) {
|
|
fprintf(stderr, "%s: warning: model was trained on only %d context tokens (%d specified)\n",
|
|
__func__, n_ctx_train, n_ctx);
|
|
}
|
|
|
|
// print system information
|
|
{
|
|
fprintf(stderr, "\n");
|
|
fprintf(stderr, "%s\n", gpt_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 = ::llama_tokenize(ctx, chunk.textdata, true, false);
|
|
if (inp.size() > n_batch) {
|
|
fprintf(stderr, "%s: error: 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_token_eos(model) >= 0 && (inp.empty() || inp.back() != llama_token_eos(model))) {
|
|
inp.push_back(llama_token_eos(model));
|
|
}
|
|
chunk.tokens = inp;
|
|
}
|
|
|
|
// tokenization stats
|
|
if (params.verbose_prompt) {
|
|
for (int i = 0; i < (int) chunks.size(); i++) {
|
|
fprintf(stderr, "%s: prompt %d: '%s'\n", __func__, i, chunks[i].textdata.c_str());
|
|
fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, chunks[i].tokens.size());
|
|
for (int j = 0; j < (int) chunks[i].tokens.size(); j++) {
|
|
fprintf(stderr, "%6d -> '%s'\n", chunks[i].tokens[j], llama_token_to_piece(ctx, chunks[i].tokens[j]).c_str());
|
|
}
|
|
fprintf(stderr, "\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 = llama_n_embd(model);
|
|
std::vector<float> embeddings(n_chunks * n_embd, 0);
|
|
float * emb = embeddings.data();
|
|
|
|
// break into batches
|
|
int p = 0; // number of prompts processed already
|
|
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) {
|
|
float * out = emb + p * n_embd;
|
|
batch_decode(ctx, batch, out, s, n_embd);
|
|
llama_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;
|
|
batch_decode(ctx, batch, out, s, n_embd);
|
|
|
|
// save embeddings to chunks
|
|
for (int i = 0; i < n_chunks; i++) {
|
|
chunks[i].embedding = std::vector<float>(emb + i * n_embd, emb + (i + 1) * n_embd);
|
|
// clear tokens as they are no longer needed
|
|
chunks[i].tokens.clear();
|
|
}
|
|
|
|
// start loop, receive query and return top k similar chunks based on cosine similarity
|
|
std::string query;
|
|
while (true) {
|
|
printf("Enter query: ");
|
|
std::getline(std::cin, query);
|
|
std::vector<int32_t> query_tokens = llama_tokenize(ctx, query, true);
|
|
|
|
struct llama_batch query_batch = llama_batch_init(n_batch, 0, 1);
|
|
batch_add_seq(query_batch, query_tokens, 0);
|
|
|
|
std::vector<float> query_emb(n_embd, 0);
|
|
batch_decode(ctx, query_batch, query_emb.data(), 1, n_embd);
|
|
|
|
llama_batch_clear(query_batch);
|
|
|
|
// compute cosine similarities
|
|
{
|
|
std::vector<std::pair<int, float>> similarities;
|
|
for (int i = 0; i < n_chunks; i++) {
|
|
float sim = llama_embd_similarity_cos(chunks[i].embedding.data(), query_emb.data(), n_embd);
|
|
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;
|
|
});
|
|
|
|
printf("Top %d similar chunks:\n", params.sparams.top_k);
|
|
for (int i = 0; i < std::min(params.sparams.top_k, (int) chunks.size()); i++) {
|
|
printf("filename: %s\n", chunks[similarities[i].first].filename.c_str());
|
|
printf("filepos: %lld\n", (long long int) chunks[similarities[i].first].filepos);
|
|
printf("similarity: %f\n", similarities[i].second);
|
|
printf("textdata:\n%s\n", chunks[similarities[i].first].textdata.c_str());
|
|
printf("--------------------\n");
|
|
}
|
|
}
|
|
}
|
|
|
|
// clean up
|
|
llama_print_timings(ctx);
|
|
llama_free(ctx);
|
|
llama_free_model(model);
|
|
llama_backend_free();
|
|
}
|