mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-01 07:30:17 +01:00
80ea089d77
* create append_pooling operation; allow to specify attention_type; add last token pooling; update examples * find result_norm/result_embd tensors properly; update output allocation logic * only use embd output for pooling_type NONE * get rid of old causal_attn accessor * take out attention_type; add in llama_set_embeddings * bypass logits when doing non-NONE pooling
300 lines
10 KiB
C++
300 lines
10 KiB
C++
#include "common.h"
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#include "llama.h"
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#include <algorithm>
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#include <fstream>
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static void print_usage(int argc, char ** argv, const gpt_params & params) {
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gpt_params_print_usage(argc, argv, params);
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LOG_TEE("\nexample usage:\n");
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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]);
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LOG_TEE("\n");
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}
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struct chunk {
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// filename
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std::string filename;
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// original file position
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size_t filepos;
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// original text data
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std::string textdata = "";
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// tokenized text data
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std::vector<llama_token> tokens;
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// embedding
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std::vector<float> embedding;
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};
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// chunk file data to chunks of size >= chunk_size
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// chunk_separator is the separator between chunks
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static std::vector<chunk> chunk_file(const std::string & filename, int chunk_size, const std::string & chunk_separator) {
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std::vector<chunk> chunks;
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std::ifstream f(filename.c_str());
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if (!f.is_open()) {
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fprintf(stderr, "Error: could not open file %s\n", filename.c_str());
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return chunks;
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}
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chunk current_chunk;
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char buffer[1024];
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int64_t filepos = 0;
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std::string current = "";
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while (f.read(buffer, 1024)) {
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current += std::string(buffer, f.gcount());
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size_t pos;
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while ((pos = current.find(chunk_separator)) != std::string::npos) {
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current_chunk.textdata += current.substr(0, pos + chunk_separator.size());
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if ((int) current_chunk.textdata.size() > chunk_size) {
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// save chunk
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current_chunk.filepos = filepos;
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current_chunk.filename = filename;
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chunks.push_back(current_chunk);
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// update filepos
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filepos += (int) current_chunk.textdata.size();
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// reset current_chunk
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current_chunk = chunk();
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}
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current = current.substr(pos + chunk_separator.size());
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}
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}
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// add leftover data to last chunk
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if (current_chunk.textdata.size() > 0) {
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if (chunks.empty()) {
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current_chunk.filepos = filepos;
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current_chunk.filename = filename;
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chunks.push_back(current_chunk);
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} else {
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chunks.back().textdata += current_chunk.textdata;
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}
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}
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f.close();
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return chunks;
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}
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static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, llama_seq_id seq_id) {
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size_t n_tokens = tokens.size();
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for (size_t i = 0; i < n_tokens; i++) {
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llama_batch_add(batch, tokens[i], i, { seq_id }, true);
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}
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}
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static void batch_decode(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd) {
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// clear previous kv_cache values (irrelevant for embeddings)
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llama_kv_cache_clear(ctx);
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// run model
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fprintf(stderr, "%s: n_tokens = %d, n_seq = %d\n", __func__, batch.n_tokens, n_seq);
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if (llama_decode(ctx, batch) < 0) {
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fprintf(stderr, "%s : failed to decode\n", __func__);
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}
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for (int i = 0; i < batch.n_tokens; i++) {
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if (!batch.logits[i]) {
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continue;
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}
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// try to get sequence embeddings - supported only when pooling_type is not NONE
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const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
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if (embd == NULL) {
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embd = llama_get_embeddings_ith(ctx, i);
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if (embd == NULL) {
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fprintf(stderr, "%s: failed to get embeddings for token %d\n", __func__, i);
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continue;
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}
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}
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float * out = output + batch.seq_id[i][0] * n_embd;
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llama_embd_normalize(embd, out, n_embd);
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}
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}
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int main(int argc, char ** argv) {
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gpt_params params;
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if (!gpt_params_parse(argc, argv, params)) {
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print_usage(argc, argv, params);
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return 1;
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}
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// For BERT models, batch size must be equal to ubatch size
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params.n_ubatch = params.n_batch;
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params.embedding = true;
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if (params.chunk_size <= 0) {
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fprintf(stderr, "chunk_size must be positive\n");
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return 1;
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}
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if (params.context_files.empty()) {
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fprintf(stderr, "context_files must be specified\n");
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return 1;
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}
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print_build_info();
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printf("processing files:\n");
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for (auto & context_file : params.context_files) {
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printf("%s\n", context_file.c_str());
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}
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std::vector<chunk> chunks;
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for (auto & context_file : params.context_files) {
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std::vector<chunk> file_chunk = chunk_file(context_file, params.chunk_size, params.chunk_separator);
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chunks.insert(chunks.end(), file_chunk.begin(), file_chunk.end());
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}
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printf("Number of chunks: %ld\n", chunks.size());
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llama_backend_init();
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llama_numa_init(params.numa);
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llama_model * model;
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llama_context * ctx;
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// load the model
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std::tie(model, ctx) = llama_init_from_gpt_params(params);
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if (model == NULL) {
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fprintf(stderr, "%s: error: unable to load model\n", __func__);
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return 1;
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}
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const int n_ctx_train = llama_n_ctx_train(model);
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const int n_ctx = llama_n_ctx(ctx);
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const enum llama_pooling_type pooling_type = llama_pooling_type(ctx);
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if (pooling_type == LLAMA_POOLING_TYPE_NONE) {
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fprintf(stderr, "%s: error: pooling type NONE not supported\n", __func__);
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return 1;
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}
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if (n_ctx > n_ctx_train) {
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fprintf(stderr, "%s: warning: model was trained on only %d context tokens (%d specified)\n",
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__func__, n_ctx_train, n_ctx);
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}
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// print system information
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{
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fprintf(stderr, "\n");
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fprintf(stderr, "%s\n", gpt_params_get_system_info(params).c_str());
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}
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// max batch size
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const uint64_t n_batch = params.n_batch;
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GGML_ASSERT(params.n_batch >= params.n_ctx);
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// tokenize the prompts and trim
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for (auto & chunk : chunks) {
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auto inp = ::llama_tokenize(ctx, chunk.textdata, true, false);
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if (inp.size() > n_batch) {
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fprintf(stderr, "%s: error: chunk size (%lld) exceeds batch size (%lld), increase batch size and re-run\n",
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__func__, (long long int) inp.size(), (long long int) n_batch);
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return 1;
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}
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// add eos if not present
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if (llama_token_eos(model) >= 0 && (inp.empty() || inp.back() != llama_token_eos(model))) {
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inp.push_back(llama_token_eos(model));
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}
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chunk.tokens = inp;
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}
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// tokenization stats
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if (params.verbose_prompt) {
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for (int i = 0; i < (int) chunks.size(); i++) {
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fprintf(stderr, "%s: prompt %d: '%s'\n", __func__, i, chunks[i].textdata.c_str());
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fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, chunks[i].tokens.size());
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for (int j = 0; j < (int) chunks[i].tokens.size(); j++) {
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fprintf(stderr, "%6d -> '%s'\n", chunks[i].tokens[j], llama_token_to_piece(ctx, chunks[i].tokens[j]).c_str());
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}
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fprintf(stderr, "\n\n");
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}
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}
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// initialize batch
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const int n_chunks = chunks.size();
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struct llama_batch batch = llama_batch_init(n_batch, 0, 1);
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// allocate output
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const int n_embd = llama_n_embd(model);
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std::vector<float> embeddings(n_chunks * n_embd, 0);
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float * emb = embeddings.data();
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// break into batches
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int p = 0; // number of prompts processed already
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int s = 0; // number of prompts in current batch
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for (int k = 0; k < n_chunks; k++) {
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// clamp to n_batch tokens
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auto & inp = chunks[k].tokens;
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const uint64_t n_toks = inp.size();
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// encode if at capacity
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if (batch.n_tokens + n_toks > n_batch) {
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float * out = emb + p * n_embd;
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batch_decode(ctx, batch, out, s, n_embd);
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llama_batch_clear(batch);
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p += s;
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s = 0;
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}
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// add to batch
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batch_add_seq(batch, inp, s);
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s += 1;
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}
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// final batch
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float * out = emb + p * n_embd;
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batch_decode(ctx, batch, out, s, n_embd);
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// save embeddings to chunks
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for (int i = 0; i < n_chunks; i++) {
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chunks[i].embedding = std::vector<float>(emb + i * n_embd, emb + (i + 1) * n_embd);
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// clear tokens as they are no longer needed
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chunks[i].tokens.clear();
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}
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// start loop, receive query and return top k similar chunks based on cosine similarity
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std::string query;
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while (true) {
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printf("Enter query: ");
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std::getline(std::cin, query);
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std::vector<int32_t> query_tokens = llama_tokenize(ctx, query, true);
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struct llama_batch query_batch = llama_batch_init(n_batch, 0, 1);
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batch_add_seq(query_batch, query_tokens, 0);
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std::vector<float> query_emb(n_embd, 0);
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batch_decode(ctx, query_batch, query_emb.data(), 1, n_embd);
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llama_batch_clear(query_batch);
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// compute cosine similarities
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{
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std::vector<std::pair<int, float>> similarities;
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for (int i = 0; i < n_chunks; i++) {
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float sim = llama_embd_similarity_cos(chunks[i].embedding.data(), query_emb.data(), n_embd);
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similarities.push_back(std::make_pair(i, sim));
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}
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// sort similarities
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std::sort(similarities.begin(), similarities.end(), [](const std::pair<int, float> & a, const std::pair<int, float> & b) {
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return a.second > b.second;
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});
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printf("Top %d similar chunks:\n", params.sparams.top_k);
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for (int i = 0; i < std::min(params.sparams.top_k, (int) chunks.size()); i++) {
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printf("filename: %s\n", chunks[similarities[i].first].filename.c_str());
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printf("filepos: %lld\n", (long long int) chunks[similarities[i].first].filepos);
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printf("similarity: %f\n", similarities[i].second);
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printf("textdata:\n%s\n", chunks[similarities[i].first].textdata.c_str());
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printf("--------------------\n");
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}
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}
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}
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// clean up
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llama_print_timings(ctx);
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llama_free(ctx);
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llama_free_model(model);
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llama_backend_free();
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}
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