* Added numa options to allow finer grained control as well as plumbing for a new mirror mode that will require numa.h * Reverted Makefile * Fixed include * Removed sched.h from ggml.h, moved ggml_get_numa_affinity into ggml.c, removed trailing whitespace and fixed up a few inconsistent variables * removed trailing whitespace * Added numa options to allow finer grained control as well as plumbing for a new mirror mode that will require numa.h * Reverting Makefile * Fixed a number of issues with the move from BOOL to ggml_numa_strategies. Added a note about mirror mode note being implemented yet * Removing MIRROR_MODE code for this PR * Removing last bit of MIRROR_MODE code for this PR * Removing unneeded branch in server.cpp example and moving get_numa_affinity and making it static * Fixed lingering init_llama_backend() bool calls in tests and examples * Remote enum llama_numa_strategies * Revert bad merge with dynatemp flags * add missing enum ggml_numa_strategies declaration and revert sync problem with master * add missing enum ggml_numa_strategies declaration * fixed ggml_init_numa variable * Update ggml.h Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> * Update READMEs with info about numa flags, change INTERLEAVE strategy name to DISTRIBUTE everywhere, implement the improved distribution strategy from @rankaiyx, fix a spelling mistake and un-merge some bad merges * split numa init out from llama_backend_init and created llama_numa_init. Updated all code paths and samples * Fix up some boolean vs enum comparisons * Added #ifdefs for non-Linux OS that don't have cpu_set_t datatype * Update ggml.h Align enum values Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update ggml.c Remove whitespace Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update ggml.c align paremeters Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update examples/server/server.cpp remove whitespace and align brace Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update common/common.cpp Remove whitespace and align brace Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * unified ggml_numa_strategy enum and fixed text alignment in server.cpp example * Update ggml.c simplified return for platforms without NUMA support Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> * removed redundant else from cli argument processing of --numa * whitespace --------- Co-authored-by: root <root@nenya.lothlorien.ca> Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: Jared Van Bortel <jared@nomic.ai>
186 lines
5.1 KiB
C++
186 lines
5.1 KiB
C++
#include "common.h"
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#include "llama.h"
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#include <ctime>
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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static std::vector<std::string> split_lines(const std::string & s) {
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std::string line;
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std::vector<std::string> lines;
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std::stringstream ss(s);
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while (std::getline(ss, line)) {
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lines.push_back(line);
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}
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return lines;
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}
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static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, int seq_id) {
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for (size_t i = 0; i < tokens.size(); i++) {
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llama_batch_add(batch, tokens[i], i, { seq_id }, false);
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}
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}
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static void normalize(float * vec, float * out, int n) {
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float norm = 0;
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for (int i = 0; i < n; i++) {
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norm += vec[i] * vec[i];
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}
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norm = sqrt(norm);
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for (int i = 0; i < n; i++) {
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out[i] = vec[i] / norm;
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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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// normalize on copy
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for (int k = 0; k < n_seq; k++) {
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float * emb = llama_get_embeddings_ith(ctx, k);
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float * out = output + k * n_embd;
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normalize(emb, 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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return 1;
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}
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params.embedding = true;
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print_build_info();
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if (params.seed == LLAMA_DEFAULT_SEED) {
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params.seed = time(NULL);
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}
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fprintf(stderr, "%s: seed = %u\n", __func__, params.seed);
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std::mt19937 rng(params.seed);
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if (params.random_prompt) {
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params.prompt = gpt_random_prompt(rng);
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}
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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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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", get_system_info(params).c_str());
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}
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// split the prompt into lines
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std::vector<std::string> prompts = split_lines(params.prompt);
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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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std::vector<std::vector<int32_t>> inputs;
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for (const auto & prompt : prompts) {
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auto inp = ::llama_tokenize(ctx, prompt, true);
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if (inp.size() > n_batch) {
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inp.resize(n_batch);
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}
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inputs.push_back(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) inputs.size(); i++) {
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fprintf(stderr, "%s: prompt %d: '%s'\n", __func__, i, prompts[i].c_str());
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fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, inputs[i].size());
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for (int j = 0; j < (int) inputs[i].size(); j++) {
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fprintf(stderr, "%6d -> '%s'\n", inputs[i][j], llama_token_to_piece(ctx, inputs[i][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_prompts = prompts.size();
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struct llama_batch batch = llama_batch_init(n_batch, 0, n_prompts);
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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_prompts * 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_prompts; k++) {
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// clamp to n_batch tokens
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auto & inp = inputs[k];
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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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// print first 3 embeddings
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for (int j = 0; j < std::min(3, n_prompts); j++) {
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fprintf(stderr, "embedding %d: ", j);
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for (int i = 0; i < n_embd; i++) {
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fprintf(stderr, "%f ", emb[j * n_embd + i]);
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}
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fprintf(stderr, "\n\n");
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}
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fprintf(stderr, "\n");
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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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return 0;
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}
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