llama.cpp/examples/embedding/embedding.cpp
2024-03-14 15:14:14 +02:00

206 lines
5.9 KiB
C++

#include "common.h"
#include "llama.h"
#include <ctime>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
static std::vector<std::string> split_lines(const std::string & s) {
std::string line;
std::vector<std::string> lines;
std::stringstream ss(s);
while (std::getline(ss, line)) {
lines.push_back(line);
}
return lines;
}
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)) {
return 1;
}
params.embedding = true;
print_build_info();
if (params.seed == LLAMA_DEFAULT_SEED) {
params.seed = time(NULL);
}
fprintf(stderr, "%s: seed = %u\n", __func__, params.seed);
std::mt19937 rng(params.seed);
if (params.random_prompt) {
params.prompt = gpt_random_prompt(rng);
}
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", get_system_info(params).c_str());
}
// split the prompt into lines
std::vector<std::string> prompts = split_lines(params.prompt);
// max batch size
const uint64_t n_batch = params.n_batch;
GGML_ASSERT(params.n_batch >= params.n_ctx);
// tokenize the prompts and trim
std::vector<std::vector<int32_t>> inputs;
for (const auto & prompt : prompts) {
auto inp = ::llama_tokenize(ctx, prompt, true, false);
if (inp.size() > n_batch) {
inp.resize(n_batch);
}
inputs.push_back(inp);
}
// add eos if not present
for (auto & inp : inputs) {
if (inp.empty() || inp.back() != llama_token_eos(model)) {
inp.push_back(llama_token_eos(model));
}
}
// tokenization stats
if (params.verbose_prompt) {
for (int i = 0; i < (int) inputs.size(); i++) {
fprintf(stderr, "%s: prompt %d: '%s'\n", __func__, i, prompts[i].c_str());
fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, inputs[i].size());
for (int j = 0; j < (int) inputs[i].size(); j++) {
fprintf(stderr, "%6d -> '%s'\n", inputs[i][j], llama_token_to_piece(ctx, inputs[i][j]).c_str());
}
fprintf(stderr, "\n\n");
}
}
// initialize batch
const int n_prompts = prompts.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_prompts * 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_prompts; k++) {
// clamp to n_batch tokens
auto & inp = inputs[k];
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);
// print the first part of the embeddings
fprintf(stdout, "\n");
for (int j = 0; j < n_prompts; j++) {
fprintf(stdout, "embedding %d: ", j);
for (int i = 0; i < std::min(16, n_embd); i++) {
fprintf(stdout, "%9.6f ", emb[j * n_embd + i]);
}
fprintf(stdout, "\n");
}
// print cosine similarity matrix
fprintf(stdout, "\n");
printf("cosine similarity matrix:\n\n");
for (int i = 0; i < n_prompts; i++) {
for (int j = 0; j < n_prompts; j++) {
float sim = llama_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd);
fprintf(stdout, "%6.2f ", sim);
}
fprintf(stdout, "\n");
}
// clean up
llama_print_timings(ctx);
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
return 0;
}