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#include "arg.h"
#include "common.h"
#include "log.h"
#include "llama.h"
#include <ctime>
#include <algorithm>
#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, const std::string & separator = "\n") {
std::vector<std::string> lines;
size_t start = 0;
size_t end = s.find(separator);
while (end != std::string::npos) {
lines.push_back(s.substr(start, end - start));
start = end + separator.length();
end = s.find(separator, start);
}
lines.push_back(s.substr(start)); // Add the last part
return lines;
}
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_decode(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd, int embd_norm) {
const enum llama_pooling_type pooling_type = llama_pooling_type(ctx);
// clear previous kv_cache values (irrelevant for embeddings)
llama_memory_clear(llama_get_memory(ctx), true);
// 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;
}
const float * embd = nullptr;
int embd_pos = 0;
if (pooling_type == LLAMA_POOLING_TYPE_NONE) {
// try to get token embeddings
embd = llama_get_embeddings_ith(ctx, i);
embd_pos = i;
GGML_ASSERT(embd != NULL && "failed to get token embeddings");
} else {
// try to get sequence embeddings - supported only when pooling_type is not NONE
embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
embd_pos = batch.seq_id[i][0];
GGML_ASSERT(embd != NULL && "failed to get sequence embeddings");
}
float * out = output + embd_pos * n_embd;
common_embd_normalize(embd, out, n_embd, embd_norm);
}
}
// plain, pipe-friendly output: one embedding per line
static void print_raw_embeddings(const float * emb,
int n_embd_count,
int n_embd,
const llama_model * model,
enum llama_pooling_type pooling_type,
int embd_normalize) {
const uint32_t n_cls_out = llama_model_n_cls_out(model);
const bool is_rank = (pooling_type == LLAMA_POOLING_TYPE_RANK);
const int cols = is_rank ? std::min<int>(n_embd, (int) n_cls_out) : n_embd;
for (int j = 0; j < n_embd_count; ++j) {
for (int i = 0; i < cols; ++i) {
if (embd_normalize == 0) {
LOG("%1.0f%s", emb[j * n_embd + i], (i + 1 < cols ? " " : ""));
} else {
LOG("%1.7f%s", emb[j * n_embd + i], (i + 1 < cols ? " " : ""));
}
}
LOG("\n");
}
}
int main(int argc, char ** argv) {
common_params params;
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_EMBEDDING)) {
return 1;
}
common_init();
params.embedding = true;
// get max number of sequences per batch
const int n_seq_max = llama_max_parallel_sequences();
// if the number of prompts that would be encoded is known in advance, it's more efficient to specify the
// --parallel argument accordingly. for convenience, if not specified, we fallback to unified KV cache
// in order to support any number of prompts
if (params.n_parallel == 1) {
LOG_INF("%s: n_parallel == 1 -> unified KV cache is enabled\n", __func__);
params.kv_unified = true;
params.n_parallel = n_seq_max;
}
// utilize the full context
if (params.n_batch < params.n_ctx) {
LOG_WRN("%s: setting batch size to %d\n", __func__, params.n_ctx);
params.n_batch = params.n_ctx;
}
// for non-causal models, batch size must be equal to ubatch size
if (params.attention_type != LLAMA_ATTENTION_TYPE_CAUSAL) {
params.n_ubatch = params.n_batch;
}
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 (llama_model_has_encoder(model) && llama_model_has_decoder(model)) {
LOG_ERR("%s: computing embeddings in encoder-decoder models is 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());
}
// split the prompt into lines
std::vector<std::string> prompts = split_lines(params.prompt, params.embd_sep);
// max batch size
const uint64_t n_batch = params.n_batch;
// get added sep and eos token, if any
const std::string added_sep_token = llama_vocab_get_add_sep(vocab) ? llama_vocab_get_text(vocab, llama_vocab_sep(vocab)) : "";
const std::string added_eos_token = llama_vocab_get_add_eos(vocab) ? llama_vocab_get_text(vocab, llama_vocab_eos(vocab)) : "";
const char * rerank_prompt = llama_model_chat_template(model, "rerank");
// tokenize the prompts and trim
std::vector<std::vector<int32_t>> inputs;
for (const auto & prompt : prompts) {
std::vector<llama_token> inp;
// split classification pairs and insert expected separator tokens
if (pooling_type == LLAMA_POOLING_TYPE_RANK && prompt.find(params.cls_sep) != std::string::npos) {
std::vector<std::string> pairs = split_lines(prompt, params.cls_sep);
if (rerank_prompt != nullptr) {
const std::string query = pairs[0];
const std::string doc = pairs[1];
std::string final_prompt = rerank_prompt;
string_replace_all(final_prompt, "{query}" , query);
string_replace_all(final_prompt, "{document}", doc );
inp = common_tokenize(vocab, final_prompt, true, true);
} else {
std::string final_prompt;
for (size_t i = 0; i < pairs.size(); i++) {
final_prompt += pairs[i];
if (i != pairs.size() - 1) {
if (!added_eos_token.empty()) {
final_prompt += added_eos_token;
}
if (!added_sep_token.empty()) {
final_prompt += added_sep_token;
}
}
}
inp = common_tokenize(ctx, final_prompt, true, true);
}
} else {
inp = common_tokenize(ctx, prompt, true, true);
}
if (inp.size() > n_batch) {
LOG_ERR("%s: number of tokens in input line (%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;
}
inputs.push_back(inp);
}
// check if the last token is SEP/EOS
// it should be automatically added by the tokenizer when 'tokenizer.ggml.add_eos_token' is set to 'true'
for (auto & inp : inputs) {
if (inp.empty() || (inp.back() != llama_vocab_sep(vocab) && inp.back() != llama_vocab_eos(vocab))) {
LOG_WRN("%s: last token in the prompt is not SEP or EOS\n", __func__);
LOG_WRN("%s: 'tokenizer.ggml.add_eos_token' should be set to 'true' in the GGUF header\n", __func__);
}
}
// tokenization stats
if (params.verbose_prompt) {
for (int i = 0; i < (int) inputs.size(); i++) {
LOG_INF("%s: prompt %d: '%s'\n", __func__, i, prompts[i].c_str());
LOG_INF("%s: number of tokens in prompt = %zu\n", __func__, inputs[i].size());
for (int j = 0; j < (int) inputs[i].size(); j++) {
LOG("%6d -> '%s'\n", inputs[i][j], common_token_to_piece(ctx, inputs[i][j]).c_str());
}
LOG("\n\n");
}
}
// initialize batch
const int n_prompts = prompts.size();
struct llama_batch batch = llama_batch_init(n_batch, 0, 1);
// count number of embeddings
int n_embd_count = 0;
if (pooling_type == LLAMA_POOLING_TYPE_NONE) {
for (int k = 0; k < n_prompts; k++) {
n_embd_count += inputs[k].size();
}
} else {
n_embd_count = n_prompts;
}
// allocate output
const int n_embd = llama_model_n_embd(model);
std::vector<float> embeddings(n_embd_count * n_embd, 0);
float * emb = embeddings.data();
// break into batches
int e = 0; // number of embeddings already stored
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 || s >= n_seq_max) {
float * out = emb + e * n_embd;
batch_decode(ctx, batch, out, s, n_embd, params.embd_normalize);
e += pooling_type == LLAMA_POOLING_TYPE_NONE ? batch.n_tokens : s;
s = 0;
common_batch_clear(batch);
}
// add to batch
batch_add_seq(batch, inp, s);
s += 1;
}
// final batch
float * out = emb + e * n_embd;
batch_decode(ctx, batch, out, s, n_embd, params.embd_normalize);
if (params.embd_out.empty()) {
LOG("\n");
if (pooling_type == LLAMA_POOLING_TYPE_NONE) {
for (int j = 0; j < n_embd_count; j++) {
LOG("embedding %d: ", j);
for (int i = 0; i < std::min(3, n_embd); i++) {
if (params.embd_normalize == 0) {
LOG("%6.0f ", emb[j * n_embd + i]);
} else {
LOG("%9.6f ", emb[j * n_embd + i]);
}
}
LOG(" ... ");
for (int i = n_embd - 3; i < n_embd; i++) {
if (params.embd_normalize == 0) {
LOG("%6.0f ", emb[j * n_embd + i]);
} else {
LOG("%9.6f ", emb[j * n_embd + i]);
}
}
LOG("\n");
}
} else if (pooling_type == LLAMA_POOLING_TYPE_RANK) {
const uint32_t n_cls_out = llama_model_n_cls_out(model);
std::vector<std::string> cls_out_labels;
for (uint32_t i = 0; i < n_cls_out; i++) {
const char * label = llama_model_cls_label(model, i);
const std::string label_i(label == nullptr ? "" : label);
cls_out_labels.emplace_back(label_i.empty() ? std::to_string(i) : label_i);
}
for (int j = 0; j < n_embd_count; j++) {
for (uint32_t i = 0; i < n_cls_out; i++) {
// NOTE: if you change this log - update the tests in ci/run.sh
if (n_cls_out == 1) {
LOG("rerank score %d: %8.3f\n", j, emb[j * n_embd]);
} else {
LOG("rerank score %d: %8.3f [%s]\n", j, emb[j * n_embd + i], cls_out_labels[i].c_str());
}
}
}
} else {
// print the first part of the embeddings or for a single prompt, the full embedding
for (int j = 0; j < n_prompts; j++) {
LOG("embedding %d: ", j);
for (int i = 0; i < (n_prompts > 1 ? std::min(16, n_embd) : n_embd); i++) {
if (params.embd_normalize == 0) {
LOG("%6.0f ", emb[j * n_embd + i]);
} else {
LOG("%9.6f ", emb[j * n_embd + i]);
}
}
LOG("\n");
}
// print cosine similarity matrix
if (n_prompts > 1) {
LOG("\n");
LOG("cosine similarity matrix:\n\n");
for (int i = 0; i < n_prompts; i++) {
LOG("%6.6s ", prompts[i].c_str());
}
LOG("\n");
for (int i = 0; i < n_prompts; i++) {
for (int j = 0; j < n_prompts; j++) {
float sim = common_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd);
LOG("%6.2f ", sim);
}
LOG("%1.10s", prompts[i].c_str());
LOG("\n");
}
}
}
}
if (params.embd_out == "json" || params.embd_out == "json+" || params.embd_out == "array") {
const bool notArray = params.embd_out != "array";
LOG(notArray ? "{\n \"object\": \"list\",\n \"data\": [\n" : "[");
for (int j = 0;;) { // at least one iteration (one prompt)
if (notArray) LOG(" {\n \"object\": \"embedding\",\n \"index\": %d,\n \"embedding\": ",j);
LOG("[");
for (int i = 0;;) { // at least one iteration (n_embd > 0)
LOG(params.embd_normalize == 0 ? "%1.0f" : "%1.7f", emb[j * n_embd + i]);
i++;
if (i < n_embd) LOG(","); else break;
}
LOG(notArray ? "]\n }" : "]");
j++;
if (j < n_embd_count) LOG(notArray ? ",\n" : ","); else break;
}
LOG(notArray ? "\n ]" : "]\n");
if (params.embd_out == "json+" && n_prompts > 1) {
LOG(",\n \"cosineSimilarity\": [\n");
for (int i = 0;;) { // at least two iteration (n_embd_count > 1)
LOG(" [");
for (int j = 0;;) { // at least two iteration (n_embd_count > 1)
float sim = common_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd);
LOG("%6.2f", sim);
j++;
if (j < n_embd_count) LOG(", "); else break;
}
LOG(" ]");
i++;
if (i < n_embd_count) LOG(",\n"); else break;
}
LOG("\n ]");
}
if (notArray) LOG("\n}\n");
} else if (params.embd_out == "raw") {
print_raw_embeddings(emb, n_embd_count, n_embd, model, pooling_type, params.embd_normalize);
}
LOG("\n");
llama_perf_context_print(ctx);
// clean up
llama_batch_free(batch);
llama_backend_free();
return 0;
}
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