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926 lines
30 KiB
926 lines
30 KiB
#include "ggml.h"
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#include "gpt-2.h"
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#include <cmath>
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#include <cstdio>
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#include <cstring>
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#include <fstream>
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#include <map>
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#include <string>
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#include <thread>
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#include <vector>
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#include <regex>
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#include <random>
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/////////////////////// GPT-2 BEGIN /////////////////////////
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//
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// Vocab utils
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//
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std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::string & text) {
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std::vector<std::string> words;
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// first split the text into words
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{
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std::string str = text;
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std::string pat = R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)";
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std::regex re(pat);
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std::smatch m;
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while (std::regex_search(str, m, re)) {
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for (auto x : m) {
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words.push_back(x);
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}
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str = m.suffix();
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}
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}
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// find the longest tokens that form the words:
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std::vector<gpt_vocab::id> tokens;
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for (const auto & word : words) {
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if (word.size() == 0) continue;
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int i = 0;
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int n = word.size();
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while (i < n) {
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int j = n;
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while (j > i) {
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auto it = vocab.token_to_id.find(word.substr(i, j-i));
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if (it != vocab.token_to_id.end()) {
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tokens.push_back(it->second);
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i = j;
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break;
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}
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--j;
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}
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if (i == n) {
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break;
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}
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if (j == i) {
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auto sub = word.substr(i, 1);
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if (vocab.token_to_id.find(sub) != vocab.token_to_id.end()) {
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tokens.push_back(vocab.token_to_id.at(sub));
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} else {
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fprintf(stderr, "%s: unknown token '%s'\n", __func__, sub.data());
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}
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++i;
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}
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}
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}
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return tokens;
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}
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gpt_vocab::id gpt_sample_top_k_top_p(
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const gpt_vocab & vocab,
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const float * logits,
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int top_k,
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double top_p,
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double temp,
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std::mt19937 & rng) {
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int n_logits = vocab.id_to_token.size();
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std::vector<std::pair<double, gpt_vocab::id>> logits_id;
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logits_id.reserve(n_logits);
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for (int i = 0; i < n_logits; i++) {
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logits_id.push_back(std::make_pair(logits[i], i));
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}
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// find the top K tokens
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std::partial_sort(
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logits_id.begin(),
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logits_id.begin() + top_k, logits_id.end(),
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[](const std::pair<double, gpt_vocab::id> & a, const std::pair<double, gpt_vocab::id> & b) {
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return a.first > b.first;
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});
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logits_id.resize(top_k);
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// normalize
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{
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double sum = 0.0f;
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for (int i = 0; i < (int)logits_id.size(); i++) {
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sum += logits_id[i].first;
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}
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sum = 1.0/sum;
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for (int i = 0; i < (int)logits_id.size(); i++) {
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logits_id[i].first *= sum;
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}
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}
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if (top_p < 1.0f) {
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{
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double cumsum = 0.0f;
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for (int i = 0; i < top_k; i++) {
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cumsum += logits_id[i].first;
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if (cumsum >= top_p) {
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logits_id.resize(i+1);
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break;
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}
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}
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}
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// normalize again
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{
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double sum = 0.0f;
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for (int i = 0; i < (int)logits_id.size(); i++) {
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sum += logits_id[i].first;
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}
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sum = 1.0/sum;
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for (int i = 0; i < (int)logits_id.size(); i++) {
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logits_id[i].first *= sum;
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}
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}
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}
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//printf("\n");
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//for (int i = 0; i < (int)logits_id.size(); i++) {
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// printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), logits_id[i].first);
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//}
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//exit(0);
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// sample from the obtained distribution
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std::vector<double> probs;
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probs.reserve(logits_id.size());
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for (int i = 0; i < (int) logits_id.size(); i++) {
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probs.push_back(logits_id[i].first);
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}
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std::discrete_distribution<> dist(probs.begin(), probs.end());
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int idx = dist(rng);
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return logits_id[idx].second;
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}
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// default hparams (GPT-2 117M)
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struct gpt2_hparams {
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int32_t n_vocab = 50257;
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int32_t n_ctx = 1024;
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int32_t n_embd = 768;
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int32_t n_head = 12;
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int32_t n_layer = 12;
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int32_t f16 = 1;
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};
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struct gpt2_layer {
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// normalization
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struct ggml_tensor * ln_1_g;
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struct ggml_tensor * ln_1_b;
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struct ggml_tensor * ln_2_g;
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struct ggml_tensor * ln_2_b;
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// attention
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struct ggml_tensor * c_attn_attn_w;
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struct ggml_tensor * c_attn_attn_b;
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struct ggml_tensor * c_attn_proj_w;
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struct ggml_tensor * c_attn_proj_b;
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// mlp
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struct ggml_tensor * c_mlp_fc_w;
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struct ggml_tensor * c_mlp_fc_b;
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struct ggml_tensor * c_mlp_proj_w_trans; // transposed for efficiency
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struct ggml_tensor * c_mlp_proj_b;
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};
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struct gpt2_model {
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gpt2_hparams hparams;
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// normalization
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struct ggml_tensor * ln_f_g;
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struct ggml_tensor * ln_f_b;
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struct ggml_tensor * wte; // position embedding
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struct ggml_tensor * wpe; // token embedding
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std::vector<gpt2_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::map<std::string, struct ggml_tensor *> tensors;
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};
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// load the model's weights from a file
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bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab & vocab) {
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printf("%s: loading model from '%s'\n", __func__, fname.c_str());
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auto fin = std::ifstream(fname, std::ios::binary);
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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 != 0x67676d6c) {
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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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}
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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_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.f16, sizeof(hparams.f16));
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printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
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printf("%s: n_ctx = %d\n", __func__, hparams.n_ctx);
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printf("%s: n_embd = %d\n", __func__, hparams.n_embd);
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printf("%s: n_head = %d\n", __func__, hparams.n_head);
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printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
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printf("%s: f16 = %d\n", __func__, hparams.f16);
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}
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// load vocab
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{
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int32_t n_vocab = 0;
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fin.read((char *) &n_vocab, sizeof(n_vocab));
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if (n_vocab != model.hparams.n_vocab) {
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fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",
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__func__, fname.c_str(), n_vocab, model.hparams.n_vocab);
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return false;
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}
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std::string word;
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for (int i = 0; i < 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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fin.read((char *) word.data(), len);
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vocab.token_to_id[word] = i;
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vocab.id_to_token[i] = word;
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}
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}
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// for the big tensors, we have the option to store the data in 16-bit floats
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// in order to save memory and also to speed up the computation
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const ggml_type wtype = model.hparams.f16 ? GGML_TYPE_F16 : GGML_TYPE_F32;
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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*ggml_type_size(GGML_TYPE_F32); // ln_f_g
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ctx_size += n_embd*ggml_type_size(GGML_TYPE_F32); // ln_f_b
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ctx_size += n_vocab*n_embd*ggml_type_size(wtype); // wte
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ctx_size += n_ctx*n_embd*ggml_type_size(GGML_TYPE_F32); // wpe
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ctx_size += n_layer*(n_embd*ggml_type_size(GGML_TYPE_F32)); // ln_1_g
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ctx_size += n_layer*(n_embd*ggml_type_size(GGML_TYPE_F32)); // ln_1_b
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ctx_size += n_layer*(n_embd*ggml_type_size(GGML_TYPE_F32)); // ln_2_g
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ctx_size += n_layer*(n_embd*ggml_type_size(GGML_TYPE_F32)); // ln_2_b
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ctx_size += n_layer*(3*n_embd*n_embd*ggml_type_size(wtype)); // c_attn_attn_w
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ctx_size += n_layer*( 3*n_embd*ggml_type_size(GGML_TYPE_F32)); // c_attn_attn_b
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ctx_size += n_layer*(n_embd*n_embd*ggml_type_size(wtype)); // c_attn_proj_w
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ctx_size += n_layer*( n_embd*ggml_type_size(GGML_TYPE_F32)); // c_attn_proj_b
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ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_size(wtype)); // c_mlp_fc_w
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ctx_size += n_layer*( 4*n_embd*ggml_type_size(GGML_TYPE_F32)); // c_mlp_fc_b
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ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_size(wtype)); // c_mlp_proj_w
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ctx_size += n_layer*( n_embd*ggml_type_size(GGML_TYPE_F32)); // c_mlp_proj_b
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ctx_size += n_ctx*n_layer*n_embd*ggml_type_size(GGML_TYPE_F32); // memory_k
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ctx_size += n_ctx*n_layer*n_embd*ggml_type_size(GGML_TYPE_F32); // memory_v
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ctx_size += (6 + 12*n_layer)*256; // object overhead
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printf("%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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params.mem_size = ctx_size;
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params.mem_buffer = NULL;
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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_ctx = hparams.n_ctx;
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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.ln_f_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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model.ln_f_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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model.wte = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
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model.wpe = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ctx);
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// map by name
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model.tensors["model/ln_f/g"] = model.ln_f_g;
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model.tensors["model/ln_f/b"] = model.ln_f_b;
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model.tensors["model/wte"] = model.wte;
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model.tensors["model/wpe"] = model.wpe;
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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.ln_1_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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layer.ln_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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layer.ln_2_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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layer.ln_2_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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layer.c_attn_attn_w = ggml_new_tensor_2d(ctx, wtype, 3*n_embd, n_embd);
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layer.c_attn_attn_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 3*n_embd);
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layer.c_attn_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
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layer.c_attn_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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layer.c_mlp_fc_w = ggml_new_tensor_2d(ctx, wtype, 4*n_embd, n_embd);
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layer.c_mlp_fc_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*n_embd);
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layer.c_mlp_proj_w_trans = ggml_new_tensor_2d(ctx, wtype, 4*n_embd, n_embd);
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layer.c_mlp_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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// map by name
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model.tensors["model/h" + std::to_string(i) + "/ln_1/g"] = layer.ln_1_g;
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model.tensors["model/h" + std::to_string(i) + "/ln_1/b"] = layer.ln_1_b;
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model.tensors["model/h" + std::to_string(i) + "/ln_2/g"] = layer.ln_2_g;
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model.tensors["model/h" + std::to_string(i) + "/ln_2/b"] = layer.ln_2_b;
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model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/w"] = layer.c_attn_attn_w;
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model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/b"] = layer.c_attn_attn_b;
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model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/w"] = layer.c_attn_proj_w;
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model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/b"] = layer.c_attn_proj_b;
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model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/w"] = layer.c_mlp_fc_w;
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model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/b"] = layer.c_mlp_fc_b;
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model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/w"] = layer.c_mlp_proj_w_trans;
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model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/b"] = layer.c_mlp_proj_b;
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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, GGML_TYPE_F32, n_elements);
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model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 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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printf("%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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// load weights
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{
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size_t total_size = 0;
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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));
|
|
|
|
if (fin.eof()) {
|
|
break;
|
|
}
|
|
|
|
int32_t nelements = 1;
|
|
int32_t ne[2] = { 1, 1 };
|
|
for (int i = 0; i < n_dims; ++i) {
|
|
fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
|
|
nelements *= ne[i];
|
|
}
|
|
|
|
std::string name(length, 0);
|
|
fin.read(&name[0], length);
|
|
|
|
if (model.tensors.find(name.data()) == model.tensors.end()) {
|
|
fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
|
|
return false;
|
|
}
|
|
|
|
auto tensor = model.tensors[name.data()];
|
|
if (ggml_nelements(tensor) != nelements) {
|
|
fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
|
|
return false;
|
|
}
|
|
|
|
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;
|
|
}
|
|
|
|
const size_t bpe = (ftype == 0) ? sizeof(float) : sizeof(ggml_fp16_t);
|
|
|
|
if (nelements*bpe != 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;
|
|
}
|
|
|
|
fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
|
|
|
|
//printf("%24s - [%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);
|
|
total_size += ggml_nbytes(tensor);
|
|
}
|
|
|
|
printf("%s: model size = %8.2f MB\n", __func__, total_size/1024.0/1024.0);
|
|
}
|
|
|
|
fin.close();
|
|
|
|
return true;
|
|
}
|
|
|
|
// evaluate the transformer
|
|
//
|
|
// - model: the model
|
|
// - n_threads: number of threads to use
|
|
// - n_past: the context size so far
|
|
// - embd_inp: the embeddings of the tokens in the context
|
|
// - embd_w: the predicted probabilities of the next token
|
|
//
|
|
bool gpt2_eval(
|
|
const gpt2_model & model,
|
|
const int n_threads,
|
|
const int n_past,
|
|
const std::vector<gpt_vocab::id> & embd_inp,
|
|
std::vector<float> & embd_w,
|
|
size_t & mem_per_token) {
|
|
const int N = embd_inp.size();
|
|
|
|
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;
|
|
|
|
static size_t buf_size = 640u*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 = 1.1*(mem_per_token*N); // add 10% to account for ggml object overhead
|
|
printf("\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;
|
|
params.mem_size = buf_size;
|
|
params.mem_buffer = buf;
|
|
|
|
struct ggml_context * ctx0 = ggml_init(params);
|
|
|
|
struct ggml_cgraph gf = { };
|
|
gf.n_threads = n_threads;
|
|
|
|
struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
|
|
memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd));
|
|
|
|
struct ggml_tensor * position = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
|
|
for (int i = 0; i < N; ++i) {
|
|
((int32_t *) position->data)[i] = n_past + i;
|
|
}
|
|
|
|
// wte + wpe
|
|
struct ggml_tensor * inpL =
|
|
ggml_add(ctx0,
|
|
ggml_get_rows(ctx0, model.wte, embd),
|
|
ggml_get_rows(ctx0, model.wpe, position));
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
struct ggml_tensor * cur;
|
|
|
|
// norm
|
|
{
|
|
// [ 768, N]
|
|
cur = ggml_norm(ctx0, inpL);
|
|
|
|
// cur = ln_1_g*cur + ln_1_b
|
|
// [ 768, N]
|
|
cur = ggml_add(ctx0,
|
|
ggml_mul(ctx0,
|
|
ggml_repeat(ctx0, model.layers[il].ln_1_g, cur),
|
|
cur),
|
|
ggml_repeat(ctx0, model.layers[il].ln_1_b, cur));
|
|
}
|
|
|
|
// attn
|
|
// [2304, 768] - model.layers[il].c_attn_attn_w
|
|
// [2304, 1] - model.layers[il].c_attn_attn_b
|
|
// [ 768, N] - cur (in)
|
|
// [2304, N] - cur (out)
|
|
//
|
|
// cur = attn_w*cur + attn_b
|
|
// [2304, N]
|
|
{
|
|
cur = ggml_mul_mat(ctx0,
|
|
ggml_transpose(ctx0, model.layers[il].c_attn_attn_w),
|
|
cur);
|
|
|
|
cur = ggml_add(ctx0,
|
|
ggml_repeat(ctx0, model.layers[il].c_attn_attn_b, cur),
|
|
cur);
|
|
}
|
|
|
|
// self-attention
|
|
{
|
|
struct ggml_tensor * Qcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 0*sizeof(float)*n_embd);
|
|
struct ggml_tensor * Kcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 1*sizeof(float)*n_embd);
|
|
struct ggml_tensor * Vcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 2*sizeof(float)*n_embd);
|
|
|
|
// 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)
|
|
// [64, N, 12]
|
|
struct ggml_tensor * Q =
|
|
ggml_permute(ctx0,
|
|
ggml_cpy(ctx0,
|
|
Qcur,
|
|
ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd/n_head, n_head, N)),
|
|
0, 2, 1, 3);
|
|
|
|
// K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3)
|
|
// [64, n_past + N, 12]
|
|
struct ggml_tensor * K =
|
|
ggml_permute(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),
|
|
0, 2, 1, 3);
|
|
|
|
// GG: flash attention
|
|
//struct ggml_tensor * V =
|
|
// ggml_cpy(ctx0,
|
|
// 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),
|
|
// ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_past + N, n_embd/n_head, n_head));
|
|
|
|
//struct ggml_tensor * KQV = ggml_flash_attn(ctx0, Q, K, V, true);
|
|
|
|
// K * Q
|
|
// [n_past + N, N, 12]
|
|
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
|
|
|
|
// KQ_scaled = KQ / sqrt(n_embd/n_head)
|
|
// [n_past + N, N, 12]
|
|
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)
|
|
// [n_past + N, N, 12]
|
|
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
|
|
|
|
// KQ = soft_max(KQ_masked)
|
|
// [n_past + N, N, 12]
|
|
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()
|
|
// [n_past + N, 64, 12]
|
|
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
|
|
// [64, N, 12]
|
|
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max);
|
|
|
|
// KQV_merged = KQV.permute(0, 2, 1, 3)
|
|
// [64, 12, N]
|
|
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
|
|
|
|
// cur = KQV_merged.contiguous().view(n_embd, N)
|
|
// [768, N]
|
|
cur = ggml_cpy(ctx0,
|
|
KQV_merged,
|
|
ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
|
|
}
|
|
|
|
// projection
|
|
// [ 768, 768] - model.layers[il].c_attn_proj_w
|
|
// [ 768, 1] - model.layers[il].c_attn_proj_b
|
|
// [ 768, N] - cur (in)
|
|
// [ 768, N] - cur (out)
|
|
//
|
|
// cur = proj_w*cur + proj_b
|
|
// [768, N]
|
|
{
|
|
cur = ggml_mul_mat(ctx0,
|
|
ggml_transpose(ctx0, model.layers[il].c_attn_proj_w),
|
|
cur);
|
|
|
|
cur = ggml_add(ctx0,
|
|
ggml_repeat(ctx0, model.layers[il].c_attn_proj_b, cur),
|
|
cur);
|
|
}
|
|
|
|
// add the input
|
|
cur = ggml_add(ctx0, cur, inpL);
|
|
|
|
struct ggml_tensor * inpFF = cur;
|
|
|
|
// feed-forward network
|
|
{
|
|
// norm
|
|
{
|
|
cur = ggml_norm(ctx0, inpFF);
|
|
|
|
// cur = ln_2_g*cur + ln_2_b
|
|
// [ 768, N]
|
|
cur = ggml_add(ctx0,
|
|
ggml_mul(ctx0,
|
|
ggml_repeat(ctx0, model.layers[il].ln_2_g, cur),
|
|
cur),
|
|
ggml_repeat(ctx0, model.layers[il].ln_2_b, cur));
|
|
}
|
|
|
|
// fully connected
|
|
// [3072, 768] - model.layers[il].c_mlp_fc_w
|
|
// [3072, 1] - model.layers[il].c_mlp_fc_b
|
|
// [ 768, N] - cur (in)
|
|
// [3072, N] - cur (out)
|
|
//
|
|
// cur = fc_w*cur + fc_b
|
|
// [3072, N]
|
|
cur = ggml_mul_mat(ctx0,
|
|
ggml_transpose(ctx0, model.layers[il].c_mlp_fc_w),
|
|
cur);
|
|
|
|
cur = ggml_add(ctx0,
|
|
ggml_repeat(ctx0, model.layers[il].c_mlp_fc_b, cur),
|
|
cur);
|
|
|
|
// GELU activation
|
|
// [3072, N]
|
|
cur = ggml_gelu(ctx0, cur);
|
|
|
|
// projection
|
|
// [ 768, 3072] - model.layers[il].c_mlp_proj_w
|
|
// [ 768, 1] - model.layers[il].c_mlp_proj_b
|
|
// [3072, N] - cur (in)
|
|
// [ 768, N] - cur (out)
|
|
//
|
|
// cur = proj_w*cur + proj_b
|
|
// [768, N]
|
|
cur = ggml_mul_mat(ctx0,
|
|
model.layers[il].c_mlp_proj_w_trans,
|
|
cur);
|
|
|
|
cur = ggml_add(ctx0,
|
|
ggml_repeat(ctx0, model.layers[il].c_mlp_proj_b, cur),
|
|
cur);
|
|
}
|
|
|
|
// input for next layer
|
|
inpL = ggml_add(ctx0, cur, inpFF);
|
|
}
|
|
|
|
// norm
|
|
{
|
|
// [ 768, N]
|
|
inpL = ggml_norm(ctx0, inpL);
|
|
|
|
// inpL = ln_f_g*inpL + ln_f_b
|
|
// [ 768, N]
|
|
inpL = ggml_add(ctx0,
|
|
ggml_mul(ctx0,
|
|
ggml_repeat(ctx0, model.ln_f_g, inpL),
|
|
inpL),
|
|
ggml_repeat(ctx0, model.ln_f_b, inpL));
|
|
}
|
|
|
|
// inpL = WTE * inpL
|
|
// [ 768, 50257] - model.wte
|
|
// [ 768, N] - inpL
|
|
inpL = ggml_mul_mat(ctx0, model.wte, 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);
|
|
|
|
// return result for just the last token
|
|
embd_w.resize(n_vocab);
|
|
memcpy(embd_w.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;
|
|
}
|
|
//printf("used_mem = %zu\n", ggml_used_mem(ctx0));
|
|
|
|
ggml_free(ctx0);
|
|
|
|
return true;
|
|
}
|
|
|
|
/////////////////////////////// GPT-2 END ////////////////////////////////
|
|
|
|
constexpr int N_THREAD = 8;
|
|
|
|
struct gpt2_context {
|
|
std::string prompt_base = R"(Hello, how are you?
|
|
I'm fine, thanks. How are you?
|
|
Thanks, I'm fine too. What are you doing?
|
|
I'm just sitting here.
|
|
It's a lovely day, isn't it?
|
|
Yes, it is. I love the weather this time of year.
|
|
I wish it would rain a little bit.
|
|
Me too.
|
|
)";
|
|
|
|
std::mt19937 rng;
|
|
|
|
gpt_vocab vocab;
|
|
gpt2_model model;
|
|
|
|
int32_t n_threads = std::min(N_THREAD, (int) std::thread::hardware_concurrency());
|
|
|
|
// sampling parameters
|
|
int32_t top_k = 40;
|
|
float top_p = 0.9f;
|
|
float temp = 1.0f;
|
|
};
|
|
|
|
struct gpt2_context * gpt2_init(const char * path_model) {
|
|
gpt2_context * ctx = new gpt2_context;
|
|
|
|
ctx->rng = std::mt19937(time(NULL));
|
|
|
|
// load the model
|
|
{
|
|
const int64_t t_start_us = ggml_time_us();
|
|
|
|
if (!gpt2_model_load(path_model, ctx->model, ctx->vocab)) {
|
|
fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, "gpt-2.bin");
|
|
return nullptr;
|
|
}
|
|
|
|
const int64_t t_load_us = ggml_time_us() - t_start_us;
|
|
|
|
printf("gpt-2: model loaded in %d ms\n", (int) (t_load_us/1000));
|
|
}
|
|
|
|
return ctx;
|
|
}
|
|
|
|
void gpt2_free(struct gpt2_context * ctx) {
|
|
delete ctx;
|
|
}
|
|
|
|
const char * gpt2_get_prompt(struct gpt2_context * ctx) {
|
|
return ctx->prompt_base.c_str();
|
|
}
|
|
|
|
void gpt2_set_prompt(struct gpt2_context * ctx, const char * prompt) {
|
|
ctx->prompt_base = prompt;
|
|
}
|
|
|
|
std::vector<gpt_vocab::id> gpt2_tokenize(const gpt2_context * ctx, const char * text) {
|
|
return ::gpt_tokenize(ctx->vocab, text);
|
|
}
|
|
|
|
std::string gpt2_gen_text(gpt2_context * ctx, const char * text, int max_tokens) {
|
|
int n_past = 0;
|
|
|
|
std::vector<float> embd_w;
|
|
|
|
// tokenize the prompt
|
|
std::vector<gpt_vocab::id> embd_inp = ::gpt2_tokenize(ctx, text);
|
|
|
|
int n_predict = std::min(max_tokens, ctx->model.hparams.n_ctx - (int) embd_inp.size());
|
|
|
|
std::vector<gpt_vocab::id> embd = embd_inp;
|
|
|
|
size_t mem_per_token = 3000000;
|
|
|
|
std::string result;
|
|
|
|
for (int i = embd.size(); i < embd_inp.size() + n_predict; i++) {
|
|
// predict
|
|
if (embd.size() > 0) {
|
|
if (!gpt2_eval(ctx->model, ctx->n_threads, n_past, embd, embd_w, mem_per_token)) {
|
|
printf("gpt-2: failed to generate text\n");
|
|
return "";
|
|
}
|
|
}
|
|
|
|
n_past += embd.size();
|
|
embd.clear();
|
|
|
|
{
|
|
// sample next token
|
|
const int top_k = ctx->top_k;
|
|
const float top_p = ctx->top_p;
|
|
const float temp = ctx->temp;
|
|
|
|
const int n_vocab = ctx->model.hparams.n_vocab;
|
|
|
|
const gpt_vocab::id id = gpt_sample_top_k_top_p(ctx->vocab, embd_w.data() + (embd_w.size() - n_vocab), top_k, top_p, temp, ctx->rng);
|
|
|
|
// add it to the context
|
|
embd.push_back(id);
|
|
}
|
|
|
|
result += ctx->vocab.id_to_token[embd[0]];
|
|
|
|
// end of text token
|
|
if (embd.back() == 50256 ||
|
|
ctx->vocab.id_to_token[embd.back()] == "." ||
|
|
ctx->vocab.id_to_token[embd.back()] == "!" ||
|
|
ctx->vocab.id_to_token[embd.back()] == "?") {
|
|
break;
|
|
}
|
|
}
|
|
|
|
return result;
|
|
}
|