mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-30 16:07:17 +01:00
1571 lines
53 KiB
C++
1571 lines
53 KiB
C++
#include "llama.h"
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#include "ggml.h"
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#include <cinttypes>
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#include <fstream>
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#include <random>
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#include <unordered_map>
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#include <queue>
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#include <regex>
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#include <cassert>
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#include <cstring>
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// determine number of model parts based on the dimension
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static const std::unordered_map<int, int> LLAMA_N_PARTS = {
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{ 4096, 1 },
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{ 5120, 2 },
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{ 6656, 4 },
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{ 8192, 8 },
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};
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// default hparams (LLaMA 7B)
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struct llama_hparams {
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int32_t n_vocab = 32000;
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int32_t n_ctx = 512; // this is provided as user input?
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int32_t n_embd = 4096;
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int32_t n_mult = 256;
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int32_t n_head = 32;
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int32_t n_layer = 32;
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int32_t n_rot = 64;
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int32_t f16 = 1;
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};
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struct llama_layer {
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// normalization
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struct ggml_tensor * attention_norm;
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// attention
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struct ggml_tensor * wq;
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struct ggml_tensor * wk;
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struct ggml_tensor * wv;
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struct ggml_tensor * wo;
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// normalization
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struct ggml_tensor * ffn_norm;
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// ff
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struct ggml_tensor * w1;
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struct ggml_tensor * w2;
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struct ggml_tensor * w3;
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};
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struct llama_model {
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llama_hparams hparams;
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struct ggml_tensor * tok_embeddings;
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struct ggml_tensor * norm;
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struct ggml_tensor * output;
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std::vector<llama_layer> layers;
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// key + value memory
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struct ggml_tensor * memory_k;
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struct ggml_tensor * memory_v;
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//
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struct ggml_context * ctx;
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std::unordered_map<std::string, struct ggml_tensor *> tensors;
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};
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struct llama_vocab {
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using id = int32_t;
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using token = std::string;
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struct token_score {
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token tok;
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float score;
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};
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std::unordered_map<token, id> token_to_id;
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std::vector<token_score> id_to_token;
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};
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struct llama_context {
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std::mt19937 rng;
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int64_t t_load_us = 0;
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int64_t t_start_us = 0;
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int64_t t_sample_us = 0;
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int64_t t_eval_us = 0;
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int32_t n_sample = 0; // number of tokens sampled
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int32_t n_eval = 0; // number of eval calls
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llama_model model;
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llama_vocab vocab;
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size_t mem_per_token = 0;
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// decode output (2-dimensional array: [n_tokens][n_vocab])
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std::vector<float> logits;
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bool logits_all = false;
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};
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struct llama_context_params llama_context_default_params() {
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struct llama_context_params result = {
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/*.n_ctx =*/ 512,
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/*.n_parts =*/ -1,
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/*.seed =*/ 0,
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/*.f16_kv =*/ false,
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/*.logits_all =*/ false,
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/*.vocab_only =*/ false,
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};
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return result;
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}
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//
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// model loading
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//
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static bool llama_model_load(
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const std::string & fname,
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llama_context & lctx,
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int n_ctx,
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int n_parts,
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ggml_type memory_type,
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bool vocab_only) {
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fprintf(stderr, "%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
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const int64_t t_start_us = ggml_time_us();
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lctx.t_start_us = t_start_us;
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std::vector<char> f_buf(1024*1024);
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auto & model = lctx.model;
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auto & vocab = lctx.vocab;
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auto fin = std::ifstream(fname, std::ios::binary);
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fin.rdbuf()->pubsetbuf(f_buf.data(), f_buf.size());
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if (!fin) {
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fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
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return false;
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}
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// verify magic
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{
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uint32_t magic;
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fin.read((char *) &magic, sizeof(magic));
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if (magic == LLAMA_FILE_MAGIC_UNVERSIONED) {
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fprintf(stderr, "%s: invalid model file '%s' (too old, regenerate your model files!)\n",
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__func__, fname.c_str());
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return false;
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}
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if (magic != LLAMA_FILE_MAGIC) {
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fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
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return false;
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}
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uint32_t format_version;
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fin.read((char *) &format_version, sizeof(format_version));
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if (format_version != LLAMA_FILE_VERSION) {
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fprintf(stderr, "%s: invalid model file '%s' (unsupported format version %" PRIu32 ", expected %d)\n",
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__func__, fname.c_str(), format_version, LLAMA_FILE_VERSION);
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return false;
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}
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}
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int n_ff = 0;
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// load hparams
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{
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auto & hparams = model.hparams;
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fin.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
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//fin.read((char *) &hparams.n_ctx, sizeof(hparams.n_ctx));
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fin.read((char *) &hparams.n_embd, sizeof(hparams.n_embd));
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fin.read((char *) &hparams.n_mult, sizeof(hparams.n_mult));
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fin.read((char *) &hparams.n_head, sizeof(hparams.n_head));
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fin.read((char *) &hparams.n_layer, sizeof(hparams.n_layer));
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fin.read((char *) &hparams.n_rot, sizeof(hparams.n_rot));
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fin.read((char *) &hparams.f16, sizeof(hparams.f16));
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hparams.n_ctx = n_ctx;
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n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult;
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if (n_parts < 1) {
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n_parts = LLAMA_N_PARTS.at(hparams.n_embd);
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}
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// temp warning to tell the user to use "--n_parts"
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if (hparams.f16 == 4 && n_parts != 1) {
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fprintf(stderr, "%s: GPTQ model detected - are you sure n_parts should be %d? we normally expect it to be 1\n", __func__, n_parts);
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fprintf(stderr, "%s: use '--n_parts 1' if necessary\n", __func__);
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}
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fprintf(stderr, "%s: n_vocab = %d\n", __func__, hparams.n_vocab);
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fprintf(stderr, "%s: n_ctx = %d\n", __func__, hparams.n_ctx);
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fprintf(stderr, "%s: n_embd = %d\n", __func__, hparams.n_embd);
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fprintf(stderr, "%s: n_mult = %d\n", __func__, hparams.n_mult);
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fprintf(stderr, "%s: n_head = %d\n", __func__, hparams.n_head);
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fprintf(stderr, "%s: n_layer = %d\n", __func__, hparams.n_layer);
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fprintf(stderr, "%s: n_rot = %d\n", __func__, hparams.n_rot);
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fprintf(stderr, "%s: f16 = %d\n", __func__, hparams.f16);
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fprintf(stderr, "%s: n_ff = %d\n", __func__, n_ff);
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fprintf(stderr, "%s: n_parts = %d\n", __func__, n_parts);
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}
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// load vocab
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{
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std::string word;
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vocab.id_to_token.resize(model.hparams.n_vocab);
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std::vector<char> tmp(64);
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for (int i = 0; i < model.hparams.n_vocab; i++) {
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uint32_t len;
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fin.read((char *) &len, sizeof(len));
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word.resize(len);
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if (len > 0) {
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tmp.resize(len);
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fin.read(tmp.data(), len);
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word.assign(tmp.data(), len);
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} else {
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word.clear();
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}
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float score;
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fin.read((char *) &score, sizeof(score));
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vocab.token_to_id[word] = i;
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auto &tok_score = vocab.id_to_token[i];
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tok_score.tok = word;
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tok_score.score = score;
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}
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}
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if (vocab_only) {
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return true;
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}
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// for the big tensors, we have the option to store the data in 16-bit floats or quantized
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// in order to save memory and also to speed up the computation
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// wtype is for per-layer weights, while vtype is for other weights
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ggml_type wtype, vtype;
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switch (model.hparams.f16) {
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case 0: wtype = vtype = GGML_TYPE_F32; break;
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case 1: wtype = vtype = GGML_TYPE_F16; break;
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case 2: wtype = vtype = GGML_TYPE_Q4_0; break;
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case 3: wtype = vtype = GGML_TYPE_Q4_1; break;
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case 4: wtype = GGML_TYPE_Q4_1; vtype = GGML_TYPE_F16; break;
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default:
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{
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fprintf(stderr, "%s: invalid model file '%s' (bad f16 value %d)\n",
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__func__, fname.c_str(), model.hparams.f16);
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return false;
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}
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}
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auto & ctx = model.ctx;
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size_t ctx_size = 0;
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{
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const auto & hparams = model.hparams;
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const int n_embd = hparams.n_embd;
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const int n_layer = hparams.n_layer;
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const int n_ctx = hparams.n_ctx;
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const int n_vocab = hparams.n_vocab;
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ctx_size += n_embd*n_vocab*ggml_type_sizef(vtype); // tok_embeddings
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ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // norm
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ctx_size += n_embd*n_vocab*ggml_type_sizef(vtype); // output
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ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // attention_norm
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ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // wq
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ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // wk
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ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // wv
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ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // wo
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ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ffn_norm
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ctx_size += n_layer*(n_ff*n_embd*ggml_type_sizef(wtype)); // w1
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ctx_size += n_layer*(n_ff*n_embd*ggml_type_sizef(wtype)); // w2
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ctx_size += n_layer*(n_ff*n_embd*ggml_type_sizef(wtype)); // w3
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ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(memory_type); // memory_k
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ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(memory_type); // memory_v
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ctx_size += (5 + 10*n_layer)*256; // object overhead
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fprintf(stderr, "%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
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}
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// create the ggml context
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{
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struct ggml_init_params params = {
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/*.mem_size =*/ ctx_size,
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/*.mem_buffer =*/ NULL,
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};
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model.ctx = ggml_init(params);
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if (!model.ctx) {
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fprintf(stderr, "%s: ggml_init() failed\n", __func__);
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return false;
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}
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}
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// prepare memory for the weights
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{
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const auto & hparams = model.hparams;
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const int n_embd = hparams.n_embd;
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const int n_layer = hparams.n_layer;
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const int n_vocab = hparams.n_vocab;
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model.layers.resize(n_layer);
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model.tok_embeddings = ggml_new_tensor_2d(ctx, vtype, n_embd, n_vocab);
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model.norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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model.output = ggml_new_tensor_2d(ctx, vtype, n_embd, n_vocab);
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// map by name
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model.tensors["tok_embeddings.weight"] = model.tok_embeddings;
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model.tensors["norm.weight"] = model.norm;
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model.tensors["output.weight"] = model.output;
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for (int i = 0; i < n_layer; ++i) {
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auto & layer = model.layers[i];
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layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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layer.wq = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
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layer.wk = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
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layer.wv = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
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layer.wo = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
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layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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layer.w1 = ggml_new_tensor_2d(ctx, wtype, n_embd, n_ff);
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layer.w2 = ggml_new_tensor_2d(ctx, wtype, n_ff, n_embd);
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layer.w3 = ggml_new_tensor_2d(ctx, wtype, n_embd, n_ff);
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// map by name
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model.tensors["layers." + std::to_string(i) + ".attention_norm.weight"] = layer.attention_norm;
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model.tensors["layers." + std::to_string(i) + ".attention.wq.weight"] = layer.wq;
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model.tensors["layers." + std::to_string(i) + ".attention.wk.weight"] = layer.wk;
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model.tensors["layers." + std::to_string(i) + ".attention.wv.weight"] = layer.wv;
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model.tensors["layers." + std::to_string(i) + ".attention.wo.weight"] = layer.wo;
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model.tensors["layers." + std::to_string(i) + ".ffn_norm.weight"] = layer.ffn_norm;
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model.tensors["layers." + std::to_string(i) + ".feed_forward.w1.weight"] = layer.w1;
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model.tensors["layers." + std::to_string(i) + ".feed_forward.w2.weight"] = layer.w2;
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model.tensors["layers." + std::to_string(i) + ".feed_forward.w3.weight"] = layer.w3;
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}
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}
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// key + value memory
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{
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const auto & hparams = model.hparams;
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const int n_embd = hparams.n_embd;
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const int n_layer = hparams.n_layer;
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const int n_ctx = hparams.n_ctx;
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const int n_mem = n_layer*n_ctx;
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const int n_elements = n_embd*n_mem;
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model.memory_k = ggml_new_tensor_1d(ctx, memory_type, n_elements);
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model.memory_v = ggml_new_tensor_1d(ctx, memory_type, n_elements);
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const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);
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fprintf(stderr, "%s: memory_size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);
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}
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const size_t file_offset = fin.tellg();
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fin.close();
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std::vector<uint8_t> tmp;
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for (int i = 0; i < n_parts; ++i) {
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const int part_id = i;
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//const int part_id = n_parts - i - 1;
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std::string fname_part = fname;
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if (i > 0) {
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fname_part += "." + std::to_string(i);
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}
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fprintf(stderr, "%s: loading model part %d/%d from '%s'\n", __func__, i+1, n_parts, fname_part.c_str());
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fin = std::ifstream(fname_part, std::ios::binary);
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fin.rdbuf()->pubsetbuf(f_buf.data(), f_buf.size());
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fin.seekg(file_offset);
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// load weights
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{
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int n_tensors = 0;
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size_t total_size = 0;
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fprintf(stderr, "%s: ", __func__);
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while (true) {
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int32_t n_dims;
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int32_t length;
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int32_t ftype;
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fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
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fin.read(reinterpret_cast<char *>(&length), sizeof(length));
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fin.read(reinterpret_cast<char *>(&ftype), sizeof(ftype));
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if (fin.eof()) {
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break;
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}
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int32_t nelements = 1;
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int32_t ne[2] = { 1, 1 };
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for (int i = 0; i < n_dims; ++i) {
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fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
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nelements *= ne[i];
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}
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std::string name(length, 0);
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fin.read(&name[0], length);
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if (model.tensors.find(name.data()) == model.tensors.end()) {
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fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
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return false;
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}
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// split_type = 0: split by columns
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// split_type = 1: split by rows
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int split_type = 0;
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// split_type = 0:
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// regex:
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// - tok_embeddings.*
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// - layers.*.attention.wo.weight
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// - layers.*.feed_forward.w2.weight
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// split_type = 1:
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// regex:
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// - output.*
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// - layers.*.attention.wq.weight
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// - layers.*.attention.wk.weight
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// - layers.*.attention.wv.weight
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// - layers.*.feed_forward.w1.weight
|
|
// - layers.*.feed_forward.w3.weight
|
|
if (name.find("tok_embeddings") != std::string::npos) {
|
|
split_type = 0;
|
|
} else if (name.find("layers") != std::string::npos) {
|
|
if (name.find("attention.wo.weight") != std::string::npos) {
|
|
split_type = 0;
|
|
} else if (name.find("feed_forward.w2.weight") != std::string::npos) {
|
|
split_type = 0;
|
|
} else {
|
|
split_type = 1;
|
|
}
|
|
} else if (name.find("output") != std::string::npos) {
|
|
split_type = 1;
|
|
}
|
|
|
|
auto tensor = model.tensors[name.data()];
|
|
|
|
if (n_dims == 1) {
|
|
if (ggml_nelements(tensor) != nelements) {
|
|
fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
|
|
return false;
|
|
}
|
|
} else {
|
|
if (ggml_nelements(tensor)/n_parts != nelements) {
|
|
fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
|
|
return false;
|
|
}
|
|
}
|
|
|
|
if (n_dims == 1) {
|
|
if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
|
|
fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
|
|
__func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]);
|
|
return false;
|
|
}
|
|
} else {
|
|
if (split_type == 0) {
|
|
if (tensor->ne[0]/n_parts != ne[0] || tensor->ne[1] != ne[1]) {
|
|
fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
|
|
__func__, name.data(), tensor->ne[0]/n_parts, tensor->ne[1], ne[0], ne[1]);
|
|
return false;
|
|
}
|
|
} else {
|
|
if (tensor->ne[0] != ne[0] || tensor->ne[1]/n_parts != ne[1]) {
|
|
fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
|
|
__func__, name.data(), tensor->ne[0], tensor->ne[1]/n_parts, ne[0], ne[1]);
|
|
return false;
|
|
}
|
|
}
|
|
}
|
|
|
|
if (0) {
|
|
static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", };
|
|
fprintf(stderr, "%24s - [%5d, %5d], type = %6s, split = %d\n", name.data(), ne[0], ne[1], ftype_str[ftype], split_type);
|
|
}
|
|
|
|
size_t bpe = 0;
|
|
|
|
switch (ftype) {
|
|
case 0: bpe = ggml_type_size(GGML_TYPE_F32); break;
|
|
case 1: bpe = ggml_type_size(GGML_TYPE_F16); break;
|
|
case 2: bpe = ggml_type_size(GGML_TYPE_Q4_0); assert(ne[0] % 64 == 0); break;
|
|
case 3: bpe = ggml_type_size(GGML_TYPE_Q4_1); assert(ne[0] % 64 == 0); break;
|
|
default:
|
|
{
|
|
fprintf(stderr, "%s: unknown ftype %d in model file\n", __func__, ftype);
|
|
return false;
|
|
}
|
|
};
|
|
|
|
if (n_dims == 1 || n_parts == 1) {
|
|
if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)) {
|
|
fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
|
|
__func__, name.data(), ggml_nbytes(tensor), nelements*bpe);
|
|
return false;
|
|
}
|
|
|
|
if (part_id == 0) {
|
|
fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
|
|
} else {
|
|
fin.seekg(ggml_nbytes(tensor), std::ios::cur);
|
|
}
|
|
|
|
total_size += ggml_nbytes(tensor);
|
|
} else {
|
|
if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)/n_parts) {
|
|
fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
|
|
__func__, name.data(), ggml_nbytes(tensor)/n_parts, nelements*bpe);
|
|
return false;
|
|
}
|
|
|
|
if (split_type == 0) {
|
|
const int np0 = ne[0];
|
|
|
|
const size_t row_size = (tensor->ne[0]/ggml_blck_size(tensor->type))*ggml_type_size(tensor->type);
|
|
assert(row_size == tensor->nb[1]);
|
|
|
|
for (int i1 = 0; i1 < ne[1]; ++i1) {
|
|
const size_t offset_row = i1*row_size;
|
|
const size_t offset = offset_row + ((part_id*np0)/ggml_blck_size(tensor->type))*ggml_type_size(tensor->type);
|
|
fin.read(reinterpret_cast<char *>(tensor->data) + offset, row_size/n_parts);
|
|
}
|
|
} else {
|
|
const int np1 = ne[1];
|
|
|
|
const size_t row_size = (tensor->ne[0]/ggml_blck_size(tensor->type))*ggml_type_size(tensor->type);
|
|
|
|
for (int i1 = 0; i1 < ne[1]; ++i1) {
|
|
const size_t offset_row = (i1 + part_id*np1)*row_size;
|
|
fin.read(reinterpret_cast<char *>(tensor->data) + offset_row, row_size);
|
|
}
|
|
}
|
|
|
|
total_size += ggml_nbytes(tensor)/n_parts;
|
|
}
|
|
|
|
//fprintf(stderr, "%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
|
|
if (++n_tensors % 8 == 0) {
|
|
fprintf(stderr, ".");
|
|
fflush(stderr);
|
|
}
|
|
}
|
|
|
|
fprintf(stderr, " done\n");
|
|
|
|
fprintf(stderr, "%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, n_tensors);
|
|
}
|
|
|
|
fin.close();
|
|
}
|
|
|
|
lctx.logits.reserve(lctx.model.hparams.n_ctx);
|
|
|
|
lctx.t_load_us = ggml_time_us() - t_start_us;
|
|
|
|
return true;
|
|
}
|
|
|
|
// evaluate the transformer
|
|
//
|
|
// - lctx: llama context
|
|
// - tokens: new batch of tokens to process
|
|
// - n_past: the context size so far
|
|
// - n_threads: number of threads to use
|
|
//
|
|
static bool llama_eval_internal(
|
|
llama_context & lctx,
|
|
const llama_token * tokens,
|
|
const int n_tokens,
|
|
const int n_past,
|
|
const int n_threads) {
|
|
const int64_t t_start_us = ggml_time_us();
|
|
|
|
const int N = n_tokens;
|
|
|
|
const auto & model = lctx.model;
|
|
const auto & hparams = model.hparams;
|
|
|
|
const int n_embd = hparams.n_embd;
|
|
const int n_layer = hparams.n_layer;
|
|
const int n_ctx = hparams.n_ctx;
|
|
const int n_head = hparams.n_head;
|
|
const int n_vocab = hparams.n_vocab;
|
|
const int n_rot = hparams.n_embd/hparams.n_head;
|
|
|
|
auto & mem_per_token = lctx.mem_per_token;
|
|
|
|
// TODO: fix this hardcoded size
|
|
static size_t buf_size = 512u*1024*1024;
|
|
static void * buf = malloc(buf_size);
|
|
|
|
if (mem_per_token > 0 && mem_per_token*N > buf_size) {
|
|
const size_t buf_size_new = 1.3*(mem_per_token*N); // add 30% to account for ggml object overhead
|
|
//fprintf(stderr, "\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
|
|
|
|
// reallocate
|
|
buf_size = buf_size_new;
|
|
buf = realloc(buf, buf_size);
|
|
if (buf == nullptr) {
|
|
fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size);
|
|
return false;
|
|
}
|
|
}
|
|
|
|
struct ggml_init_params params = {
|
|
/*.mem_size =*/ buf_size,
|
|
/*.mem_buffer =*/ buf,
|
|
};
|
|
|
|
struct ggml_context * ctx0 = ggml_init(params);
|
|
ggml_cgraph gf = {};
|
|
gf.n_threads = n_threads;
|
|
|
|
struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
|
|
memcpy(embd->data, tokens, N*ggml_element_size(embd));
|
|
|
|
struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.tok_embeddings, embd);
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
struct ggml_tensor * inpSA = inpL;
|
|
|
|
struct ggml_tensor * cur;
|
|
|
|
// norm
|
|
{
|
|
cur = ggml_rms_norm(ctx0, inpL);
|
|
|
|
// cur = attention_norm*cur
|
|
cur = ggml_mul(ctx0,
|
|
ggml_repeat(ctx0, model.layers[il].attention_norm, cur),
|
|
cur);
|
|
}
|
|
|
|
// self-attention
|
|
{
|
|
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
|
|
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
|
|
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
|
|
|
|
// store key and value to memory
|
|
if (N >= 1) {
|
|
struct ggml_tensor * k = ggml_view_1d(ctx0, model.memory_k, N*n_embd, (ggml_element_size(model.memory_k)*n_embd)*(il*n_ctx + n_past));
|
|
struct ggml_tensor * v = ggml_view_1d(ctx0, model.memory_v, N*n_embd, (ggml_element_size(model.memory_v)*n_embd)*(il*n_ctx + n_past));
|
|
|
|
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
|
|
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
|
|
}
|
|
|
|
// Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3)
|
|
struct ggml_tensor * Q =
|
|
ggml_permute(ctx0,
|
|
ggml_rope(ctx0,
|
|
ggml_cpy(ctx0,
|
|
Qcur,
|
|
ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd/n_head, n_head, N)),
|
|
n_past, n_rot, 0),
|
|
0, 2, 1, 3);
|
|
|
|
// K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3)
|
|
struct ggml_tensor * K =
|
|
ggml_permute(ctx0,
|
|
ggml_rope(ctx0,
|
|
ggml_reshape_3d(ctx0,
|
|
ggml_view_1d(ctx0, model.memory_k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_k)*n_embd),
|
|
n_embd/n_head, n_head, n_past + N),
|
|
n_past, n_rot, 1),
|
|
0, 2, 1, 3);
|
|
|
|
// K * Q
|
|
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
|
|
|
|
// KQ_scaled = KQ / sqrt(n_embd/n_head)
|
|
struct ggml_tensor * KQ_scaled =
|
|
ggml_scale(ctx0,
|
|
KQ,
|
|
ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head))
|
|
);
|
|
|
|
// KQ_masked = mask_past(KQ_scaled)
|
|
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
|
|
|
|
// KQ = soft_max(KQ_masked)
|
|
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
|
|
|
|
// V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous()
|
|
struct ggml_tensor * V_trans =
|
|
ggml_permute(ctx0,
|
|
ggml_reshape_3d(ctx0,
|
|
ggml_view_1d(ctx0, model.memory_v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_v)*n_embd),
|
|
n_embd/n_head, n_head, n_past + N),
|
|
1, 2, 0, 3);
|
|
|
|
// KQV = transpose(V) * KQ_soft_max
|
|
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max);
|
|
|
|
// KQV_merged = KQV.permute(0, 2, 1, 3)
|
|
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
|
|
|
|
// cur = KQV_merged.contiguous().view(n_embd, N)
|
|
cur = ggml_cpy(ctx0,
|
|
KQV_merged,
|
|
ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
|
|
|
|
// projection (no bias)
|
|
cur = ggml_mul_mat(ctx0,
|
|
model.layers[il].wo,
|
|
cur);
|
|
}
|
|
|
|
struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
|
|
|
|
// feed-forward network
|
|
{
|
|
// norm
|
|
{
|
|
cur = ggml_rms_norm(ctx0, inpFF);
|
|
|
|
// cur = ffn_norm*cur
|
|
cur = ggml_mul(ctx0,
|
|
ggml_repeat(ctx0, model.layers[il].ffn_norm, cur),
|
|
cur);
|
|
}
|
|
|
|
struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
|
|
model.layers[il].w3,
|
|
cur);
|
|
|
|
|
|
cur = ggml_mul_mat(ctx0,
|
|
model.layers[il].w1,
|
|
cur);
|
|
|
|
// SILU activation
|
|
cur = ggml_silu(ctx0, cur);
|
|
|
|
cur = ggml_mul(ctx0, cur, tmp);
|
|
|
|
cur = ggml_mul_mat(ctx0,
|
|
model.layers[il].w2,
|
|
cur);
|
|
}
|
|
|
|
cur = ggml_add(ctx0, cur, inpFF);
|
|
|
|
// input for next layer
|
|
inpL = cur;
|
|
}
|
|
|
|
// norm
|
|
{
|
|
inpL = ggml_rms_norm(ctx0, inpL);
|
|
|
|
// inpL = norm*inpL
|
|
inpL = ggml_mul(ctx0,
|
|
ggml_repeat(ctx0, model.norm, inpL),
|
|
inpL);
|
|
}
|
|
|
|
// lm_head
|
|
{
|
|
inpL = ggml_mul_mat(ctx0, model.output, inpL);
|
|
}
|
|
|
|
// logits -> probs
|
|
//inpL = ggml_soft_max(ctx0, inpL);
|
|
|
|
// run the computation
|
|
ggml_build_forward_expand(&gf, inpL);
|
|
ggml_graph_compute (ctx0, &gf);
|
|
|
|
//if (n_past%100 == 0) {
|
|
// ggml_graph_print (&gf);
|
|
// ggml_graph_dump_dot(&gf, NULL, "gpt-2.dot");
|
|
//}
|
|
|
|
//embd_w.resize(n_vocab*N);
|
|
//memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N);
|
|
|
|
auto & logits_out = lctx.logits;
|
|
|
|
if (lctx.logits_all) {
|
|
logits_out.resize(n_vocab * N);
|
|
memcpy(logits_out.data(), (float *) ggml_get_data(inpL), sizeof(float)*n_vocab*N);
|
|
} else {
|
|
// return result for just the last token
|
|
logits_out.resize(n_vocab);
|
|
memcpy(logits_out.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
|
|
}
|
|
|
|
if (mem_per_token == 0) {
|
|
mem_per_token = ggml_used_mem(ctx0)/N;
|
|
}
|
|
//fprintf(stderr, "used_mem = %zu\n", ggml_used_mem(ctx0));
|
|
|
|
ggml_free(ctx0);
|
|
|
|
// measure the performance only for the single-token evals
|
|
if (N == 1) {
|
|
lctx.t_eval_us += ggml_time_us() - t_start_us;
|
|
lctx.n_eval++;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
//
|
|
// tokenizer
|
|
//
|
|
|
|
static size_t utf8_len(char src) {
|
|
const size_t lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 };
|
|
uint8_t highbits = static_cast<uint8_t>(src) >> 4;
|
|
return lookup[highbits];
|
|
}
|
|
|
|
struct llama_sp_symbol {
|
|
using index = int;
|
|
index prev;
|
|
index next;
|
|
const char * text;
|
|
size_t n;
|
|
};
|
|
|
|
struct llama_sp_bigram {
|
|
struct comparator {
|
|
bool operator()(llama_sp_bigram & l, llama_sp_bigram & r) {
|
|
return (l.score < r.score) || (l.score == r.score && l.left > r.left);
|
|
}
|
|
};
|
|
using queue_storage = std::vector<llama_sp_bigram>;
|
|
using queue = std::priority_queue<llama_sp_bigram, queue_storage, comparator>;
|
|
llama_sp_symbol::index left;
|
|
llama_sp_symbol::index right;
|
|
float score;
|
|
size_t size;
|
|
};
|
|
|
|
// original implementation:
|
|
// https://github.com/ggerganov/llama.cpp/commit/074bea2eb1f1349a0118239c4152914aecaa1be4
|
|
struct llama_tokenizer {
|
|
llama_tokenizer(const llama_vocab & vocab): vocab_(vocab) {}
|
|
|
|
void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
|
|
// split string into utf8 chars
|
|
int index = 0;
|
|
size_t offs = 0;
|
|
while (offs < text.size()) {
|
|
llama_sp_symbol sym;
|
|
size_t char_len = std::min(text.size() - offs, utf8_len(text[offs]));
|
|
sym.text = text.c_str() + offs;
|
|
sym.n = char_len;
|
|
offs += char_len;
|
|
sym.prev = index - 1;
|
|
sym.next = offs == text.size() ? -1 : index + 1;
|
|
index++;
|
|
symbols_.emplace_back(std::move(sym));
|
|
}
|
|
|
|
// seed the work queue with all possible 2-character tokens.
|
|
for (size_t i = 1; i < symbols_.size(); ++i) {
|
|
try_add_bigram(i - 1, i);
|
|
}
|
|
|
|
// keep substituting the highest frequency pairs for as long as we can.
|
|
while (!work_queue_.empty()) {
|
|
auto bigram = work_queue_.top();
|
|
work_queue_.pop();
|
|
|
|
auto & left_sym = symbols_[bigram.left];
|
|
auto & right_sym = symbols_[bigram.right];
|
|
|
|
// if one of the symbols already got merged, skip it.
|
|
if (left_sym.n == 0 || right_sym.n == 0 ||
|
|
left_sym.n + right_sym.n != bigram.size) {
|
|
continue;
|
|
}
|
|
|
|
// merge the right sym into the left one
|
|
left_sym.n += right_sym.n;
|
|
right_sym.n = 0;
|
|
|
|
//printf("left = '%*s' size = %zu\n", (int) left_sym.n, left_sym.text, bigram.size);
|
|
|
|
// remove the right sym from the chain
|
|
left_sym.next = right_sym.next;
|
|
if (right_sym.next >= 0) {
|
|
symbols_[right_sym.next].prev = bigram.left;
|
|
}
|
|
|
|
// find more substitutions
|
|
try_add_bigram(left_sym.prev, bigram.left);
|
|
try_add_bigram(bigram.left, left_sym.next);
|
|
}
|
|
|
|
for (int i = 0; i != -1; i = symbols_[i].next) {
|
|
auto & symbol = symbols_[i];
|
|
auto token = vocab_.token_to_id.find(std::string(symbol.text, symbol.n));
|
|
|
|
if (token == vocab_.token_to_id.end()) {
|
|
// output any symbols that did not form tokens as bytes.
|
|
for (int j = 0; j < (int) symbol.n; ++j) {
|
|
llama_vocab::id token_id = static_cast<uint8_t>(symbol.text[j]) + 3;
|
|
output.push_back(token_id);
|
|
}
|
|
} else {
|
|
output.push_back((*token).second);
|
|
}
|
|
}
|
|
}
|
|
|
|
private:
|
|
void try_add_bigram(int left, int right) {
|
|
if (left == -1 || right == -1) {
|
|
return;
|
|
}
|
|
|
|
const std::string text = std::string(symbols_[left].text, symbols_[left].n + symbols_[right].n);
|
|
auto token = vocab_.token_to_id.find(text);
|
|
|
|
if (token == vocab_.token_to_id.end()) {
|
|
return;
|
|
}
|
|
|
|
if (static_cast<size_t>((*token).second) >= vocab_.id_to_token.size()) {
|
|
return;
|
|
}
|
|
|
|
const auto &tok_score = vocab_.id_to_token[(*token).second];
|
|
|
|
llama_sp_bigram bigram;
|
|
bigram.left = left;
|
|
bigram.right = right;
|
|
bigram.score = tok_score.score;
|
|
bigram.size = text.size();
|
|
work_queue_.push(bigram);
|
|
}
|
|
|
|
const llama_vocab & vocab_;
|
|
std::vector<llama_sp_symbol> symbols_;
|
|
llama_sp_bigram::queue work_queue_;
|
|
};
|
|
|
|
static std::vector<llama_vocab::id> llama_tokenize(const llama_vocab & vocab, const std::string & text, bool bos) {
|
|
llama_tokenizer tokenizer(vocab);
|
|
std::vector<llama_vocab::id> output;
|
|
|
|
if (text.size() == 0) {
|
|
return output;
|
|
}
|
|
|
|
if (bos) {
|
|
output.push_back(1);
|
|
}
|
|
|
|
tokenizer.tokenize(text, output);
|
|
return output;
|
|
}
|
|
|
|
//
|
|
// sampling
|
|
//
|
|
|
|
static void sample_top_k(std::vector<std::pair<double, llama_vocab::id>> & logits_id, int top_k) {
|
|
// find the top k tokens
|
|
std::partial_sort(
|
|
logits_id.begin(),
|
|
logits_id.begin() + top_k, logits_id.end(),
|
|
[](const std::pair<double, llama_vocab::id> & a, const std::pair<double, llama_vocab::id> & b) {
|
|
return a.first > b.first;
|
|
});
|
|
|
|
logits_id.resize(top_k);
|
|
}
|
|
|
|
static llama_vocab::id llama_sample_top_p_top_k(
|
|
llama_context & lctx,
|
|
const std::vector<llama_vocab::id> & last_n_tokens,
|
|
int top_k,
|
|
double top_p,
|
|
double temp,
|
|
double repeat_penalty) {
|
|
auto & rng = lctx.rng;
|
|
|
|
const auto & vocab = lctx.vocab;
|
|
const auto & logits = lctx.logits;
|
|
|
|
int n_logits = vocab.id_to_token.size();
|
|
|
|
std::vector<std::pair<double, llama_vocab::id>> logits_id;
|
|
logits_id.reserve(n_logits);
|
|
|
|
{
|
|
const double scale = 1.0/temp;
|
|
for (int i = 0; i < n_logits; ++i) {
|
|
// repetition penalty from ctrl paper (https://arxiv.org/abs/1909.05858)
|
|
// credit https://github.com/facebookresearch/llama/compare/main...shawwn:llama:main
|
|
if (std::find(last_n_tokens.begin(), last_n_tokens.end(), i) != last_n_tokens.end()) {
|
|
// if score < 0 then repetition penalty has to multiplied to reduce the previous token probability
|
|
if (logits[i] < 0.0) {
|
|
logits_id.push_back(std::make_pair(logits[i]*scale*repeat_penalty, i));
|
|
} else {
|
|
logits_id.push_back(std::make_pair(logits[i]*scale/repeat_penalty, i));
|
|
}
|
|
} else {
|
|
logits_id.push_back(std::make_pair(logits[i]*scale, i));
|
|
}
|
|
}
|
|
}
|
|
|
|
sample_top_k(logits_id, top_k);
|
|
|
|
double maxl = -std::numeric_limits<double>::infinity();
|
|
for (const auto & kv : logits_id) {
|
|
maxl = std::max(maxl, kv.first);
|
|
}
|
|
|
|
// compute probs for the top k tokens
|
|
std::vector<double> probs;
|
|
probs.reserve(logits_id.size());
|
|
|
|
double sum = 0.0;
|
|
for (const auto & kv : logits_id) {
|
|
double p = exp(kv.first - maxl);
|
|
probs.push_back(p);
|
|
sum += p;
|
|
}
|
|
|
|
// normalize the probs
|
|
for (auto & p : probs) {
|
|
p /= sum;
|
|
}
|
|
|
|
if (top_p < 1.0f) {
|
|
double cumsum = 0.0f;
|
|
for (int i = 0; i < (int) probs.size(); i++) {
|
|
cumsum += probs[i];
|
|
if (cumsum >= top_p) {
|
|
probs.resize(i + 1);
|
|
logits_id.resize(i + 1);
|
|
break;
|
|
}
|
|
}
|
|
|
|
cumsum = 1.0/cumsum;
|
|
for (int i = 0; i < (int) probs.size(); i++) {
|
|
probs[i] *= cumsum;
|
|
}
|
|
}
|
|
|
|
//printf("\n");
|
|
//for (int i = 0; i < (int) 10; i++) {
|
|
// printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), probs[i]);
|
|
//}
|
|
//printf("\n\n");
|
|
//exit(0);
|
|
|
|
std::discrete_distribution<> dist(probs.begin(), probs.end());
|
|
int idx = dist(rng);
|
|
|
|
return logits_id[idx].second;
|
|
}
|
|
|
|
//
|
|
// quantization
|
|
//
|
|
|
|
// TODO: reuse code from the llama_model_load() somehow
|
|
bool llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, int itype, int qk) {
|
|
ggml_type type = GGML_TYPE_Q4_1;
|
|
|
|
switch (itype) {
|
|
case 2: type = GGML_TYPE_Q4_0; break;
|
|
case 3: type = GGML_TYPE_Q4_1; break;
|
|
default: fprintf(stderr, "%s: invalid quantization type %d\n", __func__, itype); return 1;
|
|
};
|
|
|
|
if (type != GGML_TYPE_Q4_0 && type != GGML_TYPE_Q4_1) {
|
|
fprintf(stderr, "%s: invalid quantization type %d\n", __func__, type);
|
|
return false;
|
|
}
|
|
|
|
llama_vocab vocab;
|
|
|
|
printf("%s: loading model from '%s'\n", __func__, fname_inp.c_str());
|
|
|
|
auto finp = std::ifstream(fname_inp, std::ios::binary);
|
|
if (!finp) {
|
|
fprintf(stderr, "%s: failed to open '%s' for reading\n", __func__, fname_inp.c_str());
|
|
return false;
|
|
}
|
|
|
|
auto fout = std::ofstream(fname_out, std::ios::binary);
|
|
if (!fout) {
|
|
fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname_out.c_str());
|
|
return false;
|
|
}
|
|
|
|
// verify magic
|
|
{
|
|
uint32_t magic;
|
|
finp.read((char *) &magic, sizeof(magic));
|
|
if (magic == LLAMA_FILE_MAGIC_UNVERSIONED) {
|
|
fprintf(stderr, "%s: invalid model file '%s' (too old, regenerate your model files!)\n",
|
|
__func__, fname_inp.c_str());
|
|
return false;
|
|
}
|
|
if (magic != LLAMA_FILE_MAGIC) {
|
|
fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname_inp.c_str());
|
|
return false;
|
|
}
|
|
|
|
fout.write((char *) &magic, sizeof(magic));
|
|
|
|
uint32_t format_version;
|
|
finp.read((char *) &format_version, sizeof(format_version));
|
|
|
|
if (format_version != LLAMA_FILE_VERSION) {
|
|
fprintf(stderr, "%s: invalid model file '%s' (unsupported format version %" PRIu32 ", expected %d)\n",
|
|
__func__, fname_inp.c_str(), format_version, LLAMA_FILE_VERSION);
|
|
return false;
|
|
}
|
|
|
|
fout.write((char *) &format_version, sizeof(format_version));
|
|
}
|
|
|
|
llama_hparams hparams;
|
|
|
|
// load hparams
|
|
{
|
|
finp.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
|
|
//finp.read((char *) &hparams.n_ctx, sizeof(hparams.n_ctx));
|
|
finp.read((char *) &hparams.n_embd, sizeof(hparams.n_embd));
|
|
finp.read((char *) &hparams.n_mult, sizeof(hparams.n_mult));
|
|
finp.read((char *) &hparams.n_head, sizeof(hparams.n_head));
|
|
finp.read((char *) &hparams.n_layer, sizeof(hparams.n_layer));
|
|
finp.read((char *) &hparams.n_rot, sizeof(hparams.n_rot));
|
|
finp.read((char *) &hparams.f16, sizeof(hparams.f16));
|
|
|
|
printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
|
|
printf("%s: n_ctx = %d\n", __func__, hparams.n_ctx);
|
|
printf("%s: n_embd = %d\n", __func__, hparams.n_embd);
|
|
printf("%s: n_mult = %d\n", __func__, hparams.n_mult);
|
|
printf("%s: n_head = %d\n", __func__, hparams.n_head);
|
|
printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
|
|
printf("%s: f16 = %d\n", __func__, hparams.f16);
|
|
|
|
fout.write((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
|
|
//fout.write((char *) &hparams.n_ctx, sizeof(hparams.n_ctx));
|
|
fout.write((char *) &hparams.n_embd, sizeof(hparams.n_embd));
|
|
fout.write((char *) &hparams.n_mult, sizeof(hparams.n_mult));
|
|
fout.write((char *) &hparams.n_head, sizeof(hparams.n_head));
|
|
fout.write((char *) &hparams.n_layer, sizeof(hparams.n_layer));
|
|
fout.write((char *) &hparams.n_rot, sizeof(hparams.n_rot));
|
|
fout.write((char *) &itype, sizeof(hparams.f16));
|
|
}
|
|
|
|
// load vocab
|
|
{
|
|
const int32_t n_vocab = hparams.n_vocab;
|
|
|
|
if (n_vocab != hparams.n_vocab) {
|
|
fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",
|
|
__func__, fname_inp.c_str(), n_vocab, hparams.n_vocab);
|
|
return false;
|
|
}
|
|
|
|
std::string word;
|
|
vocab.id_to_token.resize(n_vocab);
|
|
for (int i = 0; i < n_vocab; i++) {
|
|
uint32_t len;
|
|
finp.read ((char *) &len, sizeof(len));
|
|
fout.write((char *) &len, sizeof(len));
|
|
|
|
word.resize(len);
|
|
finp.read ((char *) word.data(), len);
|
|
fout.write((char *) word.data(), len);
|
|
|
|
float score;
|
|
finp.read ((char *) &score, sizeof(score));
|
|
fout.write((char *) &score, sizeof(score));
|
|
|
|
vocab.token_to_id[word] = i;
|
|
|
|
auto &tok_score = vocab.id_to_token[i];
|
|
tok_score.tok = word;
|
|
tok_score.score = score;
|
|
}
|
|
}
|
|
|
|
// load weights
|
|
{
|
|
size_t total_size_org = 0;
|
|
size_t total_size_new = 0;
|
|
|
|
std::vector<float> work;
|
|
|
|
std::vector<uint8_t> data_u8;
|
|
std::vector<ggml_fp16_t> data_f16;
|
|
std::vector<float> data_f32;
|
|
|
|
std::vector<int64_t> hist_all(1 << 4, 0);
|
|
|
|
while (true) {
|
|
int32_t n_dims;
|
|
int32_t length;
|
|
int32_t ftype;
|
|
|
|
finp.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
|
|
finp.read(reinterpret_cast<char *>(&length), sizeof(length));
|
|
finp.read(reinterpret_cast<char *>(&ftype), sizeof(ftype));
|
|
|
|
if (finp.eof()) {
|
|
break;
|
|
}
|
|
|
|
int32_t nelements = 1;
|
|
int32_t ne[2] = { 1, 1 };
|
|
for (int i = 0; i < n_dims; ++i) {
|
|
finp.read (reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
|
|
nelements *= ne[i];
|
|
}
|
|
|
|
std::string name(length, 0);
|
|
finp.read (&name[0], length);
|
|
|
|
{
|
|
static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", };
|
|
printf("%48s - [%5d, %5d], type = %6s ", name.data(), ne[0], ne[1], ftype_str[ftype]);
|
|
}
|
|
|
|
// regexes of tensor names to be quantized
|
|
const std::vector<std::string> k_names = {
|
|
".*weight",
|
|
};
|
|
|
|
bool quantize = false;
|
|
for (const auto & s : k_names) {
|
|
if (std::regex_match(name, std::regex(s))) {
|
|
quantize = true;
|
|
break;
|
|
}
|
|
}
|
|
|
|
// quantize only 2D tensors
|
|
quantize &= (n_dims == 2);
|
|
|
|
if (quantize) {
|
|
if (ftype != 0 && ftype != 1) {
|
|
fprintf(stderr, "%s: unsupported ftype %d for integer quantization\n", __func__, ftype);
|
|
return false;
|
|
}
|
|
|
|
if (ftype == 1) {
|
|
data_f16.resize(nelements);
|
|
finp.read(reinterpret_cast<char *>(data_f16.data()), nelements * sizeof(ggml_fp16_t));
|
|
data_f32.resize(nelements);
|
|
for (int i = 0; i < nelements; ++i) {
|
|
data_f32[i] = ggml_fp16_to_fp32(data_f16[i]);
|
|
}
|
|
} else {
|
|
data_f32.resize(nelements);
|
|
finp.read(reinterpret_cast<char *>(data_f32.data()), nelements * sizeof(float));
|
|
}
|
|
|
|
ftype = itype;
|
|
} else {
|
|
const int bpe = (ftype == 0) ? sizeof(float) : sizeof(uint16_t);
|
|
|
|
data_u8.resize(nelements*bpe);
|
|
finp.read(reinterpret_cast<char *>(data_u8.data()), nelements * bpe);
|
|
}
|
|
|
|
fout.write(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
|
|
fout.write(reinterpret_cast<char *>(&length), sizeof(length));
|
|
fout.write(reinterpret_cast<char *>(&ftype), sizeof(ftype));
|
|
for (int i = 0; i < n_dims; ++i) {
|
|
fout.write(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
|
|
}
|
|
fout.write(&name[0], length);
|
|
|
|
if (quantize) {
|
|
printf("quantizing .. ");
|
|
work.resize(nelements); // for quantization
|
|
|
|
size_t cur_size = 0;
|
|
std::vector<int64_t> hist_cur(1 << 4, 0);
|
|
|
|
switch (type) {
|
|
case GGML_TYPE_Q4_0:
|
|
{
|
|
cur_size = ggml_quantize_q4_0(data_f32.data(), work.data(), nelements, ne[0], qk, hist_cur.data());
|
|
} break;
|
|
case GGML_TYPE_Q4_1:
|
|
{
|
|
cur_size = ggml_quantize_q4_1(data_f32.data(), work.data(), nelements, ne[0], qk, hist_cur.data());
|
|
} break;
|
|
default:
|
|
{
|
|
fprintf(stderr, "%s: unsupported quantization type %d\n", __func__, type);
|
|
return false;
|
|
}
|
|
}
|
|
|
|
fout.write(reinterpret_cast<char *>(work.data()), cur_size);
|
|
total_size_new += cur_size;
|
|
|
|
printf("size = %8.2f MB -> %8.2f MB | hist: ", nelements * sizeof(float)/1024.0/1024.0, cur_size/1024.0/1024.0);
|
|
for (int i = 0; i < (int) hist_cur.size(); ++i) {
|
|
hist_all[i] += hist_cur[i];
|
|
}
|
|
|
|
for (int i = 0; i < (int) hist_cur.size(); ++i) {
|
|
printf("%5.3f ", hist_cur[i] / (float)nelements);
|
|
}
|
|
printf("\n");
|
|
} else {
|
|
printf("size = %8.3f MB\n", data_u8.size()/1024.0/1024.0);
|
|
fout.write(reinterpret_cast<char *>(data_u8.data()), data_u8.size());
|
|
total_size_new += data_u8.size();
|
|
}
|
|
|
|
total_size_org += nelements * sizeof(float);
|
|
}
|
|
|
|
printf("%s: model size = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0);
|
|
printf("%s: quant size = %8.2f MB\n", __func__, total_size_new/1024.0/1024.0);
|
|
|
|
{
|
|
int64_t sum_all = 0;
|
|
for (int i = 0; i < (int) hist_all.size(); ++i) {
|
|
sum_all += hist_all[i];
|
|
}
|
|
|
|
printf("%s: hist: ", __func__);
|
|
for (int i = 0; i < (int) hist_all.size(); ++i) {
|
|
printf("%5.3f ", hist_all[i] / (float)sum_all);
|
|
}
|
|
printf("\n");
|
|
}
|
|
}
|
|
|
|
finp.close();
|
|
fout.close();
|
|
|
|
return true;
|
|
}
|
|
|
|
//
|
|
// interface implementation
|
|
//
|
|
|
|
struct llama_context * llama_init_from_file(
|
|
const char * path_model,
|
|
struct llama_context_params params) {
|
|
ggml_time_init();
|
|
|
|
llama_context * ctx = new llama_context;
|
|
|
|
if (params.seed <= 0) {
|
|
params.seed = time(NULL);
|
|
}
|
|
|
|
ctx->rng = std::mt19937(params.seed);
|
|
ctx->logits_all = params.logits_all;
|
|
|
|
ggml_type type_memory = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32;
|
|
|
|
if (!llama_model_load(path_model, *ctx, params.n_ctx, params.n_parts, type_memory, params.vocab_only)) {
|
|
fprintf(stderr, "%s: failed to load model\n", __func__);
|
|
delete ctx;
|
|
return nullptr;
|
|
}
|
|
|
|
return ctx;
|
|
}
|
|
|
|
void llama_free(struct llama_context * ctx) {
|
|
ggml_free(ctx->model.ctx);
|
|
|
|
delete ctx;
|
|
}
|
|
|
|
int llama_model_quantize(
|
|
const char * fname_inp,
|
|
const char * fname_out,
|
|
int itype,
|
|
int qk) {
|
|
if (!llama_model_quantize_internal(fname_inp, fname_out, itype, qk)) {
|
|
fprintf(stderr, "%s: failed to quantize\n", __func__);
|
|
return 1;
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
int llama_eval(
|
|
struct llama_context * ctx,
|
|
const llama_token * tokens,
|
|
int n_tokens,
|
|
int n_past,
|
|
int n_threads) {
|
|
if (!llama_eval_internal(*ctx, tokens, n_tokens, n_past, n_threads)) {
|
|
fprintf(stderr, "%s: failed to eval\n", __func__);
|
|
return 1;
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
int llama_tokenize(
|
|
struct llama_context * ctx,
|
|
const char * text,
|
|
llama_token * tokens,
|
|
int n_max_tokens,
|
|
bool add_bos) {
|
|
auto res = llama_tokenize(ctx->vocab, text, add_bos);
|
|
|
|
if (n_max_tokens < (int) res.size()) {
|
|
fprintf(stderr, "%s: too many tokens\n", __func__);
|
|
return -((int) res.size());
|
|
}
|
|
|
|
for (size_t i = 0; i < res.size(); i++) {
|
|
tokens[i] = res[i];
|
|
}
|
|
|
|
return res.size();
|
|
}
|
|
|
|
int llama_n_vocab(struct llama_context * ctx) {
|
|
return ctx->vocab.id_to_token.size();
|
|
}
|
|
|
|
int llama_n_ctx(struct llama_context * ctx) {
|
|
return ctx->model.hparams.n_ctx;
|
|
}
|
|
|
|
float * llama_get_logits(struct llama_context * ctx) {
|
|
return ctx->logits.data();
|
|
}
|
|
|
|
const char * llama_token_to_str(struct llama_context * ctx, llama_token token) {
|
|
if (token >= llama_n_vocab(ctx)) {
|
|
return nullptr;
|
|
}
|
|
|
|
return ctx->vocab.id_to_token[token].tok.c_str();
|
|
}
|
|
|
|
llama_token llama_token_bos() {
|
|
return 1;
|
|
}
|
|
|
|
llama_token llama_token_eos() {
|
|
return 2;
|
|
}
|
|
|
|
llama_token llama_sample_top_p_top_k(
|
|
llama_context * ctx,
|
|
const llama_token * last_n_tokens_data,
|
|
int last_n_tokens_size,
|
|
int top_k,
|
|
double top_p,
|
|
double temp,
|
|
double repeat_penalty) {
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
llama_token result = 0;
|
|
|
|
// TODO: avoid this ...
|
|
const auto last_n_tokens = std::vector<llama_token>(last_n_tokens_data, last_n_tokens_data + last_n_tokens_size);
|
|
|
|
result = llama_sample_top_p_top_k(
|
|
*ctx,
|
|
last_n_tokens,
|
|
top_k,
|
|
top_p,
|
|
temp,
|
|
repeat_penalty);
|
|
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
ctx->n_sample++;
|
|
|
|
return result;
|
|
}
|
|
|
|
|
|
void llama_print_timings(struct llama_context * ctx) {
|
|
const int64_t t_end_us = ggml_time_us();
|
|
|
|
const int32_t n_sample = std::max(1, ctx->n_sample);
|
|
const int32_t n_eval = std::max(1, ctx->n_eval);
|
|
|
|
fprintf(stderr, "\n");
|
|
fprintf(stderr, "%s: load time = %8.2f ms\n", __func__, ctx->t_load_us / 1000.0f);
|
|
fprintf(stderr, "%s: sample time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3f * ctx->t_sample_us, n_sample, 1e-3f * ctx->t_sample_us / n_sample);
|
|
fprintf(stderr, "%s: eval time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3f * ctx->t_eval_us, n_eval, 1e-3f * ctx->t_eval_us / n_eval);
|
|
fprintf(stderr, "%s: total time = %8.2f ms\n", __func__, (t_end_us - ctx->t_start_us)/1000.0f);
|
|
}
|
|
|
|
void llama_reset_timings(struct llama_context * ctx) {
|
|
ctx->t_start_us = ggml_time_us();
|
|
|
|
ctx->t_sample_us = ctx->n_sample = 0;
|
|
ctx->t_eval_us = ctx->n_eval = 0;
|
|
}
|
|
|
|
const char * llama_print_system_info(void) {
|
|
static std::string s;
|
|
|
|
s = "";
|
|
s += "AVX = " + std::to_string(ggml_cpu_has_avx()) + " | ";
|
|
s += "AVX2 = " + std::to_string(ggml_cpu_has_avx2()) + " | ";
|
|
s += "AVX512 = " + std::to_string(ggml_cpu_has_avx512()) + " | ";
|
|
s += "FMA = " + std::to_string(ggml_cpu_has_fma()) + " | ";
|
|
s += "NEON = " + std::to_string(ggml_cpu_has_neon()) + " | ";
|
|
s += "ARM_FMA = " + std::to_string(ggml_cpu_has_arm_fma()) + " | ";
|
|
s += "F16C = " + std::to_string(ggml_cpu_has_f16c()) + " | ";
|
|
s += "FP16_VA = " + std::to_string(ggml_cpu_has_fp16_va()) + " | ";
|
|
s += "WASM_SIMD = " + std::to_string(ggml_cpu_has_wasm_simd()) + " | ";
|
|
s += "BLAS = " + std::to_string(ggml_cpu_has_blas()) + " | ";
|
|
s += "SSE3 = " + std::to_string(ggml_cpu_has_sse3()) + " | ";
|
|
s += "VSX = " + std::to_string(ggml_cpu_has_vsx()) + " | ";
|
|
|
|
return s.c_str();
|
|
}
|
|
|