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1027 lines
35 KiB
C++
1027 lines
35 KiB
C++
#define REPLIT_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE
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#include "replit_impl.h"
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#include "utils.h"
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#include "llmodel_shared.h"
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#include <cassert>
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#include <cinttypes>
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#include <cmath>
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#include <cstddef>
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#include <cstdio>
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#include <cstring>
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#include <sstream>
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#include <fstream>
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#include <iostream>
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#include <map>
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#include <stdint.h>
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#include <string>
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#include <thread>
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#if defined(_WIN32) && defined(_MSC_VER)
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#define WIN32_LEAN_AND_MEAN
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#ifndef NOMINMAX
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#define NOMINMAX
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#endif
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#include <windows.h>
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#include <io.h>
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#include <stdio.h>
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#else
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#include <unistd.h>
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#endif
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#include <unordered_map>
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#include <unordered_set>
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#include <utility>
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#include <vector>
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#include <regex>
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#include <ggml.h>
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#ifdef GGML_USE_METAL
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#include <ggml-metal.h>
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#endif
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/**
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IMPORTANT: This model backend and convert script were developed for the original Huggingface
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Replit model (hugginface commit hash: 9eceafb041eb8abd565dabfbfadd328869140011)
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*/
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using piece_t = std::pair<std::size_t, float>;
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using piece_map_t = std::unordered_map<std::string, piece_t>;
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namespace {
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const char *modelType_ = "Replit";
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const std::string ws_symbol = "\342\226\201";
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}
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struct replit_tokenizer {
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gpt_vocab raw_vocab;
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piece_map_t piece_map;
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std::vector<std::string> vocab;
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};
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std::pair<std::vector<LLModel::Token>, float> encode_word(const std::string & word, const piece_map_t & model) {
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std::vector<int> best_segmentations_starts(word.length() + 1, -1);
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best_segmentations_starts[0] = 0;
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std::vector<float> best_segmentations_scores(word.length() + 1, -std::numeric_limits<float>::infinity());
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best_segmentations_scores[0] = 1.0;
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for (size_t start_idx = 0; start_idx < word.length(); ++start_idx) {
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float best_score_at_start = best_segmentations_scores[start_idx];
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for (size_t end_idx = start_idx + 1; end_idx <= word.length(); ++end_idx) {
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std::string token = word.substr(start_idx, end_idx - start_idx);
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if (model.count(token) && best_score_at_start != -std::numeric_limits<float>::infinity()) {
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float token_score = model.at(token).second;
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float score = token_score + best_score_at_start;
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if (best_segmentations_scores[end_idx] == -std::numeric_limits<float>::infinity() ||
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best_segmentations_scores[end_idx] > score) {
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best_segmentations_starts[end_idx] = start_idx;
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best_segmentations_scores[end_idx] = score;
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}
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}
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}
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}
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if (best_segmentations_scores.back() == -std::numeric_limits<float>::infinity()) {
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return std::make_pair(std::vector<LLModel::Token>{0}, 0.0f);
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}
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float score = best_segmentations_scores.back();
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int start = best_segmentations_starts.back();
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int end = word.length();
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std::vector<LLModel::Token> tokens;
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while (start != 0) {
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const auto token_id = model.at(word.substr(start, end - start)).first;
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tokens.insert(tokens.begin(), token_id);
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int next_start = best_segmentations_starts[start];
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end = start;
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start = next_start;
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}
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const auto token_id = model.at(word.substr(start, end - start)).first;
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tokens.insert(tokens.begin(), token_id);
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return std::make_pair(tokens, score);
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}
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bool replit_tokenizer_load(replit_tokenizer & tokenizer, std::istream & fin, int max_vocab_size) {
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std::string word;
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std::vector<char> buf(128);
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for (LLModel::Token i = 0; i < max_vocab_size; i++) {
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uint32_t len;
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fin.read((char *)&len, sizeof(len));
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buf.resize(len);
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fin.read((char *) buf.data(), len);
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word.assign(buf.data(), len);
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float score;
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fin.read((char *)&score, sizeof(score));
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tokenizer.piece_map[word] = std::make_pair(i, -score);
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tokenizer.raw_vocab.id_to_token[i] = word;
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tokenizer.raw_vocab.token_to_id[word] = i;
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}
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return true;
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}
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std::string replace_all(const std::string & str, // where to work
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const std::string & find, // substitute 'find'
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const std::string & replace // by 'replace'
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) {
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std::string result;
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size_t find_len = find.size();
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size_t pos, from = 0;
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while (std::string::npos != (pos = str.find(find, from))) {
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result.append(str, from, pos - from);
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result.append(replace);
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from = pos + find_len;
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}
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result.append(str, from, std::string::npos);
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return result;
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}
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std::vector<LLModel::Token> replit_tokenizer_tokenize(replit_tokenizer & tokenizer, const std::string & text) {
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std::vector<LLModel::Token> tokens;
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auto normalized_text = replace_all(text, " ", ws_symbol);
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auto tokenized = encode_word(normalized_text, tokenizer.piece_map);
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return tokenized.first;
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}
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std::string replit_tokenizer_detokenize(replit_tokenizer & tokenizer, const std::vector<LLModel::Token> & tokens) {
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std::string text;
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for (auto token : tokens) {
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text += tokenizer.raw_vocab.id_to_token[token];
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}
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return replace_all(text, ws_symbol, " ");
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}
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// no defaults for now
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struct mpt_hparams {
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int32_t n_vocab = 0;
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int32_t n_ctx = 0; //d_model
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int32_t n_embd = 0; //max_seq_len
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int32_t n_head = 0; // n_heads
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int32_t n_layer = 0; //n_layers
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int32_t ftype = 0;
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};
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struct replit_layer {
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// pre normalization
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struct ggml_tensor * ln_1_weight;
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// attention
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struct ggml_tensor * c_attn_wqkv_weight;
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struct ggml_tensor * c_attn_out_proj_weight;
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// post normalization
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struct ggml_tensor * ln_2_weight;
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// ff
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struct ggml_tensor * c_mlp_mlp_up_weight;
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struct ggml_tensor * c_mlp_mlp_down_weight;
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};
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struct replit_model {
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mpt_hparams hparams;
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struct ggml_tensor * wte_weight; // position embedding
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struct ggml_tensor * ln_f_weight; // language model head
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std::vector<replit_layer> layers;
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// key + value memory
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struct llm_kv_cache kv_self;
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struct ggml_context * ctx;
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llm_buffer eval_buf;
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llm_buffer scr0_buf;
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llm_buffer scr1_buf;
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#ifdef GGML_USE_METAL
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struct ggml_metal_context * ctx_metal;
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#endif
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std::map<std::string, struct ggml_tensor *> tensors;
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};
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static bool kv_cache_init(
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const struct mpt_hparams & hparams,
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struct llm_kv_cache & cache,
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ggml_type wtype,
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int n_ctx) {
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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 int64_t n_mem = (int64_t)n_layer*n_ctx;
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const int64_t n_elements = n_embd*n_mem;
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cache.buf.resize(2u*n_elements*ggml_type_size(wtype) + 2_MiB);
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struct ggml_init_params params;
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params.mem_size = cache.buf.size;
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params.mem_buffer = cache.buf.addr;
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params.no_alloc = false;
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cache.ctx = ggml_init(params);
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if (!cache.ctx) {
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fprintf(stderr, "%s: failed to allocate memory for kv cache\n", __func__);
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return false;
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}
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cache.k = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
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cache.v = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
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return true;
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}
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// load the model's weights from a stream
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bool replit_model_load(const std::string & fname, std::istream &fin, replit_model & model, replit_tokenizer & vocab, size_t *mem_req) {
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printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
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if (mem_req != nullptr) {
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*mem_req = 0;
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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 != 0x7265706c) {
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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.ftype, sizeof(hparams.ftype));
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const int32_t qntvr = hparams.ftype / GGML_QNT_VERSION_FACTOR;
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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: ftype = %d\n", __func__, hparams.ftype);
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printf("%s: qntvr = %d\n", __func__, qntvr);
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hparams.ftype %= GGML_QNT_VERSION_FACTOR;
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}
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// load vocab
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replit_tokenizer_load(vocab, fin, model.hparams.n_vocab);
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// for the big tensors, we have the option to store the data in 16-bit
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// floats or quantized in order to save memory and also to speed up the
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// computation
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ggml_type wtype = GGML_TYPE_COUNT;
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switch (model.hparams.ftype) {
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case 0: wtype = GGML_TYPE_F32; break;
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case 1: wtype = GGML_TYPE_F16; break;
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case 2: wtype = GGML_TYPE_Q4_0; break;
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case 3: wtype = GGML_TYPE_Q4_1; 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.ftype);
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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(wtype); // wte_weight
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ctx_size += n_embd * ggml_type_sizef(GGML_TYPE_F32); // ln_f_weight
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ctx_size += n_layer * (n_embd * ggml_type_sizef(GGML_TYPE_F32)); // ln_1_weight
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ctx_size += n_layer * (3 * n_embd * n_embd * ggml_type_sizef(wtype)); // attn_Wqkv_weight
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ctx_size += n_layer * (n_embd * n_embd * ggml_type_sizef(wtype)); // attn_out_proj_weight
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ctx_size += n_layer * (n_embd * ggml_type_sizef(GGML_TYPE_F32)); // ln_2_weight
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ctx_size += n_layer * (4 * n_embd * n_embd * ggml_type_sizef(wtype)); // mlp_mlp_up_weight
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ctx_size += n_layer * (n_embd * n_embd * 4 * ggml_type_sizef(wtype)); // mlp_mlp_down_weight
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ctx_size += n_ctx * n_layer * n_embd * ggml_type_sizef(GGML_TYPE_F16); // memory_k
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ctx_size += n_ctx * n_layer * n_embd * ggml_type_sizef(GGML_TYPE_F16); // memory_v
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ctx_size += (1 + 6 * n_layer) * 512; // 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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if (mem_req != nullptr) {
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*mem_req += ctx_size;
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const int n_embd = model.hparams.n_embd;
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const int n_layer = model.hparams.n_layer;
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const int64_t n_mem = (int64_t)n_layer*model.hparams.n_ctx;
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const int64_t n_elements = n_embd*n_mem;
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*mem_req += (2u*n_elements*ggml_type_size(wtype) + 2_MiB);
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return false;
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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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.no_alloc = false,
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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.wte_weight = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
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model.ln_f_weight = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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// map by name
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model.tensors["transformer.wte.weight"] = model.wte_weight;
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model.tensors["transformer.ln_f.weight"] = model.ln_f_weight;
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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_weight = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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layer.c_attn_wqkv_weight = ggml_new_tensor_2d(ctx, wtype, n_embd, 3 * n_embd);
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layer.c_attn_out_proj_weight = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
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layer.ln_2_weight = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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layer.c_mlp_mlp_up_weight = ggml_new_tensor_2d(ctx, wtype, n_embd, 4 * n_embd);
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layer.c_mlp_mlp_down_weight = ggml_new_tensor_2d(ctx, wtype, 4 * n_embd, n_embd);
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// map by name
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model.tensors["transformer.blocks." + std::to_string(i) + ".ln_1.weight"] = layer.ln_1_weight;
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model.tensors["transformer.blocks." + std::to_string(i) + ".attn.Wqkv.weight"] = layer.c_attn_wqkv_weight;
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model.tensors["transformer.blocks." + std::to_string(i) + ".attn.out_proj.weight"] =
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layer.c_attn_out_proj_weight;
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model.tensors["transformer.blocks." + std::to_string(i) + ".ln_2.weight"] = layer.ln_2_weight;
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model.tensors["transformer.blocks." + std::to_string(i) + ".mlp.mlp_up.weight"] = layer.c_mlp_mlp_up_weight;
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model.tensors["transformer.blocks." + std::to_string(i) + ".mlp.mlp_down.weight"] =
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layer.c_mlp_mlp_down_weight;
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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_layer = hparams.n_layer;
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const int n_ctx = hparams.n_ctx;
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const int64_t n_mem = n_layer * n_ctx;
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if (!kv_cache_init(hparams, model.kv_self, GGML_TYPE_F16, model.hparams.n_ctx)) {
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fprintf(stderr, "%s: kv_cache_init() failed for self-attention cache\n", __func__);
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ggml_free(ctx);
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return false;
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}
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const size_t memory_size = ggml_nbytes(model.kv_self.k) + ggml_nbytes(model.kv_self.v);
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printf("%s: memory_size = %8.2f MB, n_mem = %" PRId64 "\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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int n_tensors = 0;
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size_t total_size = 0;
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printf("%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 ttype;
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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 *>(&ttype), sizeof(ttype));
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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);
|
|
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 [%5d, "
|
|
"%5d], expected [%5d, %5d]\n",
|
|
__func__, name.data(), (int)tensor->ne[0], (int)tensor->ne[1], ne[0], ne[1]);
|
|
return false;
|
|
}
|
|
|
|
// for debugging
|
|
if (0) {
|
|
printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1],
|
|
ggml_type_name(ggml_type(ttype)), ggml_nbytes(tensor) / 1024.0 / 1024.0, ggml_nbytes(tensor));
|
|
}
|
|
|
|
const size_t bpe = ggml_type_size(ggml_type(ttype));
|
|
|
|
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;
|
|
}
|
|
|
|
fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
|
|
|
|
total_size += ggml_nbytes(tensor);
|
|
if (++n_tensors % 8 == 0) {
|
|
printf(".");
|
|
fflush(stdout);
|
|
}
|
|
}
|
|
|
|
printf(" done\n");
|
|
|
|
printf("%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size / 1024.0 / 1024.0, n_tensors);
|
|
}
|
|
|
|
model.eval_buf.resize(512u * 1024 * 1024);
|
|
model.scr0_buf.resize(256u * 1024 * 1024);
|
|
model.scr1_buf.resize(256u * 1024 * 1024);
|
|
|
|
#ifdef GGML_USE_METAL
|
|
model.ctx_metal = ggml_metal_init();
|
|
void* data_ptr = ggml_get_mem_buffer(model.ctx);
|
|
size_t data_size = ggml_get_mem_size(model.ctx);
|
|
const size_t max_size = ggml_get_max_tensor_size(model.ctx);
|
|
|
|
#define GGML_CHECK_BUF(result) if (!(result)) { \
|
|
std::cerr << __func__ << ": failed to add buffer" << std::endl; \
|
|
ggml_free(model.ctx); \
|
|
return false; \
|
|
}
|
|
|
|
GGML_CHECK_BUF(ggml_metal_add_buffer(model.ctx_metal, "data", data_ptr, data_size, max_size));
|
|
GGML_CHECK_BUF(ggml_metal_add_buffer(model.ctx_metal, "kv", ggml_get_mem_buffer(model.kv_self.ctx),
|
|
ggml_get_mem_size(model.kv_self.ctx), 0));
|
|
GGML_CHECK_BUF(ggml_metal_add_buffer(model.ctx_metal, "eval", model.eval_buf.addr, model.eval_buf.size, 0));
|
|
GGML_CHECK_BUF(ggml_metal_add_buffer(model.ctx_metal, "scr0", model.scr0_buf.addr, model.scr0_buf.size, 0));
|
|
GGML_CHECK_BUF(ggml_metal_add_buffer(model.ctx_metal, "scr1", model.scr1_buf.addr, model.scr1_buf.size, 0));
|
|
#endif
|
|
|
|
return true;
|
|
}
|
|
|
|
// load the model's weights from a file path
|
|
bool replit_model_load(const std::string & fname, replit_model & model, replit_tokenizer & vocab) {
|
|
|
|
auto fin = std::ifstream(fname, std::ios::binary);
|
|
if (!fin) {
|
|
fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
|
|
return false;
|
|
}
|
|
|
|
bool loaded = replit_model_load(fname, fin, model, vocab, nullptr);
|
|
fin.close();
|
|
return loaded;
|
|
}
|
|
|
|
// 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 logits for the next token
|
|
//
|
|
bool replit_eval(const replit_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;
|
|
|
|
struct ggml_init_params eval_ctx_params = {
|
|
.mem_size = model.eval_buf.size,
|
|
.mem_buffer = model.eval_buf.addr,
|
|
.no_alloc = false,
|
|
};
|
|
struct ggml_context * ctx0 = ggml_init(eval_ctx_params);
|
|
struct ggml_cgraph 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 * inpL = ggml_get_rows(ctx0, model.wte_weight, embd);
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
ggml_set_scratch(ctx0, {0, model.scr0_buf.size, model.scr0_buf.addr, });
|
|
struct ggml_tensor * cur;
|
|
|
|
// a = self.ln_1(x)
|
|
{
|
|
cur = ggml_norm(ctx0, inpL);
|
|
|
|
cur = ggml_mul(ctx0, ggml_repeat(ctx0, model.layers[il].ln_1_weight, cur), cur);
|
|
}
|
|
|
|
// self-attention
|
|
// b, _, past_key_value = self.attn(a, past_key_value=past_key_value,
|
|
// attn_bias=attn_bias, attention_mask=attention_mask,
|
|
// is_causal=is_causal)
|
|
{
|
|
|
|
// compute QKV
|
|
{ cur = ggml_mul_mat(ctx0, model.layers[il].c_attn_wqkv_weight, cur); }
|
|
|
|
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
|
|
{
|
|
struct ggml_tensor * k =
|
|
ggml_view_1d(ctx0, model.kv_self.k, N * n_embd,
|
|
(ggml_element_size(model.kv_self.k) * n_embd) * (il * n_ctx + n_past));
|
|
struct ggml_tensor * v =
|
|
ggml_view_1d(ctx0, model.kv_self.v, N * n_embd,
|
|
(ggml_element_size(model.kv_self.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.kv_self.k, (n_past + N) * n_embd,
|
|
il * n_ctx * ggml_element_size(model.kv_self.v) * n_embd),
|
|
n_embd / n_head, n_head, n_past + N),
|
|
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)));
|
|
|
|
// Alibi
|
|
struct ggml_tensor * KQ_scaled_alibi = ggml_alibi(ctx0, KQ_scaled, n_past, n_head, 8.0f);
|
|
|
|
// KQ_masked = mask_past(KQ_scaled)
|
|
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled_alibi, 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() [n_past + N, 64, 12]
|
|
struct ggml_tensor * V_trans = ggml_cpy(
|
|
ctx0,
|
|
ggml_permute(ctx0,
|
|
ggml_reshape_3d(ctx0,
|
|
ggml_view_1d(ctx0, model.kv_self.v, (n_past + N) * n_embd,
|
|
il * n_ctx * ggml_element_size(model.kv_self.v) * n_embd),
|
|
n_embd / n_head, n_head, n_past + N),
|
|
1, 2, 0, 3),
|
|
ggml_new_tensor_3d(ctx0, model.kv_self.v->type, n_past + N, n_embd / n_head, n_head));
|
|
|
|
// 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
|
|
{ cur = ggml_mul_mat(ctx0, model.layers[il].c_attn_out_proj_weight, cur); }
|
|
}
|
|
ggml_set_scratch(ctx0, {0, model.scr1_buf.size, model.scr1_buf.addr, });
|
|
|
|
inpL = ggml_add(ctx0, inpL, cur);
|
|
|
|
// m = self.ln_2(x)
|
|
{
|
|
cur = ggml_norm(ctx0, inpL);
|
|
|
|
cur = ggml_mul(ctx0, ggml_repeat(ctx0, model.layers[il].ln_2_weight, cur), cur);
|
|
}
|
|
|
|
// n = self.mlp(m)
|
|
{
|
|
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].c_mlp_mlp_up_weight, cur);
|
|
|
|
// GELU activation
|
|
cur = ggml_gelu(ctx0, cur);
|
|
|
|
// projection
|
|
// cur = proj_w*cur + proj_b
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].c_mlp_mlp_down_weight, cur);
|
|
}
|
|
|
|
// x = x + n
|
|
inpL = ggml_add(ctx0, inpL, cur);
|
|
}
|
|
ggml_set_scratch(ctx0, {0, model.scr0_buf.size, model.scr0_buf.addr, });
|
|
// norm
|
|
{
|
|
inpL = ggml_norm(ctx0, inpL);
|
|
// inpL = ln_f_g*inpL
|
|
inpL = ggml_mul(ctx0, ggml_repeat(ctx0, model.ln_f_weight, inpL), inpL);
|
|
}
|
|
|
|
ggml_set_scratch(ctx0, {0, 0, nullptr, });
|
|
// output embedding weight tied to input embedding
|
|
inpL = ggml_mul_mat(ctx0, model.wte_weight, inpL);
|
|
|
|
// logits -> probs
|
|
// inpL = ggml_soft_max(ctx0, inpL);
|
|
|
|
// run the computation
|
|
ggml_build_forward_expand(&gf, inpL);
|
|
#ifdef GGML_USE_METAL
|
|
if (N == 1) {
|
|
// llama.cpp doesn't use metal for batch/prompt processing presently
|
|
// pending changes to the metal matmul kernel - only use it for generation (N=1)
|
|
ggml_metal_graph_compute(model.ctx_metal, &gf);
|
|
ggml_metal_get_tensor(model.ctx_metal, inpL);
|
|
} else {
|
|
// We need to sync the GPU KV cache with the CPU KV cache
|
|
ggml_metal_get_tensor(model.ctx_metal, model.kv_self.k);
|
|
ggml_metal_get_tensor(model.ctx_metal, model.kv_self.v);
|
|
|
|
ggml_graph_compute(ctx0, &gf);
|
|
}
|
|
#else
|
|
ggml_graph_compute(ctx0, &gf);
|
|
#endif
|
|
|
|
// std::cout << "Qcur" << std::endl;
|
|
// print_tensor(Qcur);
|
|
|
|
// if (n_past%100 == 0) {
|
|
// ggml_graph_print(&gf);
|
|
// ggml_graph_dump_dot(&gf, NULL, "replit-model.dot");
|
|
// }
|
|
|
|
// 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;
|
|
}
|
|
|
|
|
|
#define REPLIT_MAX_RNG_STATE 64*1024
|
|
|
|
size_t replit_get_state_size(const replit_model &model)
|
|
{
|
|
// we don't know size of rng until we actually serialize it. so reserve more than enough memory for its serialized state.
|
|
// for reference, std::mt19937(1337) serializes to 6701 bytes.
|
|
const size_t s_rng_size = sizeof(size_t);
|
|
const size_t s_rng = REPLIT_MAX_RNG_STATE;
|
|
const size_t s_kv_size = sizeof(size_t);
|
|
const size_t s_kv_ntok = sizeof(int);
|
|
const size_t s_kv = model.kv_self.buf.size;
|
|
const size_t s_total = (
|
|
+ s_rng_size
|
|
+ s_rng
|
|
+ s_kv_size
|
|
+ s_kv_ntok
|
|
+ s_kv
|
|
);
|
|
fflush(stdout);
|
|
return s_total;
|
|
}
|
|
|
|
size_t replit_copy_state_data(const replit_model &model, const std::mt19937 &rng, uint8_t *dest)
|
|
{
|
|
uint8_t * out = dest;
|
|
fflush(stdout);
|
|
// copy rng
|
|
{
|
|
std::stringstream rng_ss;
|
|
rng_ss << rng;
|
|
|
|
const size_t rng_size = rng_ss.str().size();
|
|
char rng_buf[REPLIT_MAX_RNG_STATE];
|
|
|
|
memset(&rng_buf[0], 0, REPLIT_MAX_RNG_STATE);
|
|
memcpy(&rng_buf[0], rng_ss.str().data(), rng_ss.str().size());
|
|
|
|
memcpy(out, &rng_size, sizeof(rng_size)); out += sizeof(rng_size);
|
|
memcpy(out, &rng_buf[0], REPLIT_MAX_RNG_STATE); out += REPLIT_MAX_RNG_STATE;
|
|
}
|
|
|
|
// copy kv cache
|
|
{
|
|
const size_t kv_size = model.kv_self.buf.size;
|
|
const int kv_ntok = model.kv_self.n;
|
|
|
|
memcpy(out, &kv_size, sizeof(kv_size)); out += sizeof(kv_size);
|
|
memcpy(out, &kv_ntok, sizeof(kv_ntok)); out += sizeof(kv_ntok);
|
|
|
|
if (kv_size) {
|
|
memcpy(out, model.kv_self.buf.addr, kv_size); out += kv_size;
|
|
}
|
|
}
|
|
|
|
const size_t written = out - dest;
|
|
const size_t expected [[maybe_unused]] = replit_get_state_size(model);
|
|
assert(written == expected);
|
|
fflush(stdout);
|
|
return written;
|
|
}
|
|
|
|
size_t replit_set_state_data(replit_model *model, std::mt19937 *rng, const uint8_t *src)
|
|
{
|
|
const uint8_t * in = src;
|
|
|
|
// set rng
|
|
{
|
|
size_t rng_size;
|
|
char rng_buf[REPLIT_MAX_RNG_STATE];
|
|
|
|
memcpy(&rng_size, in, sizeof(rng_size)); in += sizeof(rng_size);
|
|
memcpy(&rng_buf[0], in, REPLIT_MAX_RNG_STATE); in += REPLIT_MAX_RNG_STATE;
|
|
|
|
std::stringstream rng_ss;
|
|
rng_ss.str(std::string(&rng_buf[0], rng_size));
|
|
rng_ss >> *rng;
|
|
|
|
assert(rng_ss.fail() == false);
|
|
}
|
|
|
|
// set kv cache
|
|
{
|
|
size_t kv_size;
|
|
int kv_ntok;
|
|
|
|
memcpy(&kv_size, in, sizeof(kv_size)); in += sizeof(kv_size);
|
|
memcpy(&kv_ntok, in, sizeof(kv_ntok)); in += sizeof(kv_ntok);
|
|
|
|
if (kv_size) {
|
|
assert(model->kv_self.buf.size == kv_size);
|
|
|
|
void * k_data = model->kv_self.k->data; // remember data pointers
|
|
void * v_data = model->kv_self.v->data; // because their value is stored in buf and overwritten by memcpy
|
|
|
|
memcpy(model->kv_self.buf.addr, in, kv_size); in += kv_size;
|
|
|
|
model->kv_self.k->data = k_data; // restore correct data pointers
|
|
model->kv_self.v->data = v_data;
|
|
|
|
}
|
|
|
|
model->kv_self.n = kv_ntok;
|
|
}
|
|
|
|
const size_t nread = in - src;
|
|
const size_t expected [[maybe_unused]] = replit_get_state_size(*model);
|
|
assert(nread == expected);
|
|
fflush(stdout);
|
|
return nread;
|
|
}
|
|
|
|
struct ReplitPrivate {
|
|
const std::string modelPath;
|
|
bool modelLoaded;
|
|
replit_tokenizer vocab;
|
|
replit_model *model = nullptr;
|
|
int64_t n_threads = 0;
|
|
size_t mem_per_token = 0;
|
|
std::mt19937 rng;
|
|
bool has_end_of_text = false;
|
|
};
|
|
|
|
Replit::Replit()
|
|
: d_ptr(new ReplitPrivate) {
|
|
|
|
d_ptr->model = new replit_model;
|
|
d_ptr->model->ctx = nullptr;
|
|
d_ptr->modelLoaded = false;
|
|
}
|
|
|
|
size_t Replit::requiredMem(const std::string &modelPath) {
|
|
replit_model dummy_model;
|
|
replit_tokenizer dummy_vocab;
|
|
size_t mem_req;
|
|
auto fin = std::ifstream(modelPath, std::ios::binary);
|
|
replit_model_load(modelPath, fin, dummy_model, dummy_vocab, &mem_req);
|
|
return mem_req;
|
|
}
|
|
|
|
bool Replit::loadModel(const std::string &modelPath) {
|
|
std::mt19937 rng(time(NULL));
|
|
d_ptr->rng = rng;
|
|
|
|
auto fin = std::ifstream(modelPath, std::ios::binary);
|
|
|
|
// load the model
|
|
if (!replit_model_load(modelPath, fin, *d_ptr->model, d_ptr->vocab, nullptr)) {
|
|
std::cerr << "Replit ERROR: failed to load model from " << modelPath;
|
|
return false;
|
|
}
|
|
|
|
d_ptr->n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
|
|
d_ptr->modelLoaded = true;
|
|
d_ptr->has_end_of_text = d_ptr->vocab.raw_vocab.token_to_id.find("<|endoftext|>") != d_ptr->vocab.raw_vocab.token_to_id.end();
|
|
fflush(stdout);
|
|
return true;
|
|
}
|
|
|
|
void Replit::setThreadCount(int32_t n_threads) {
|
|
d_ptr->n_threads = n_threads;
|
|
}
|
|
|
|
int32_t Replit::threadCount() const
|
|
{
|
|
return d_ptr->n_threads;
|
|
}
|
|
|
|
Replit::~Replit()
|
|
{
|
|
#ifdef GGML_USE_METAL
|
|
if (d_ptr->model->ctx_metal) {
|
|
ggml_metal_free(d_ptr->model->ctx_metal);
|
|
}
|
|
#endif
|
|
if(d_ptr->model->ctx) {
|
|
ggml_free(d_ptr->model->ctx);
|
|
d_ptr->model->ctx = nullptr;
|
|
}
|
|
delete d_ptr->model;
|
|
}
|
|
|
|
bool Replit::isModelLoaded() const
|
|
{
|
|
return d_ptr->modelLoaded;
|
|
}
|
|
|
|
size_t Replit::stateSize() const
|
|
{
|
|
return replit_get_state_size(*d_ptr->model);
|
|
}
|
|
|
|
size_t Replit::saveState(uint8_t *dest) const
|
|
{
|
|
return replit_copy_state_data(*d_ptr->model, d_ptr->rng, dest);
|
|
}
|
|
|
|
size_t Replit::restoreState(const uint8_t *src)
|
|
{
|
|
return replit_set_state_data(d_ptr->model, &d_ptr->rng, src);
|
|
}
|
|
|
|
std::vector<LLModel::Token> Replit::tokenize(PromptContext &, const std::string &str) const
|
|
{
|
|
return replit_tokenizer_tokenize(d_ptr->vocab, str);
|
|
}
|
|
|
|
std::string Replit::tokenToString(LLModel::Token id) const
|
|
{
|
|
return replit_tokenizer_detokenize(d_ptr->vocab, {id});
|
|
}
|
|
|
|
LLModel::Token Replit::sampleToken(PromptContext &promptCtx) const
|
|
{
|
|
const size_t n_prev_toks = std::min((size_t) promptCtx.repeat_last_n, promptCtx.tokens.size());
|
|
return gpt_sample_top_k_top_p(d_ptr->model->hparams.n_vocab,
|
|
promptCtx.tokens.data() + promptCtx.tokens.size() - n_prev_toks,
|
|
n_prev_toks,
|
|
promptCtx.logits,
|
|
promptCtx.top_k, promptCtx.top_p, promptCtx.temp,
|
|
promptCtx.repeat_penalty,
|
|
d_ptr->rng);
|
|
}
|
|
|
|
bool Replit::evalTokens(PromptContext &ctx, const std::vector<int32_t> &tokens) const
|
|
{
|
|
return replit_eval(*d_ptr->model, d_ptr->n_threads, ctx.n_past, tokens, ctx.logits, d_ptr->mem_per_token);
|
|
}
|
|
|
|
int32_t Replit::contextLength() const
|
|
{
|
|
return d_ptr->model->hparams.n_ctx;
|
|
}
|
|
|
|
const std::vector<LLModel::Token> &Replit::endTokens() const
|
|
{
|
|
static const std::vector<LLModel::Token> fres = {0, d_ptr->vocab.raw_vocab.token_to_id["<|endoftext|>"]};
|
|
return fres;
|
|
}
|
|
|
|
#if defined(_WIN32)
|
|
#define DLL_EXPORT __declspec(dllexport)
|
|
#else
|
|
#define DLL_EXPORT __attribute__ ((visibility ("default")))
|
|
#endif
|
|
|
|
extern "C" {
|
|
DLL_EXPORT bool is_g4a_backend_model_implementation() {
|
|
return true;
|
|
}
|
|
|
|
DLL_EXPORT const char *get_model_type() {
|
|
return modelType_;
|
|
}
|
|
|
|
DLL_EXPORT const char *get_build_variant() {
|
|
return GGML_BUILD_VARIANT;
|
|
}
|
|
|
|
DLL_EXPORT bool magic_match(std::istream& f) {
|
|
uint32_t magic = 0;
|
|
f.read(reinterpret_cast<char*>(&magic), sizeof(magic));
|
|
if (magic != 0x7265706c) return false;
|
|
#ifdef GGML_USE_METAL
|
|
off_t offset = sizeof(uint32_t) * 5; // n_vocab, n_ctx, n_embd, n_head, n_layer
|
|
f.seekg(offset, std::ios_base::cur);
|
|
uint32_t ftype;
|
|
f.read(reinterpret_cast<char*>(&ftype), sizeof(ftype)); // ftype
|
|
const int32_t qntvr = ftype / GGML_QNT_VERSION_FACTOR;
|
|
ftype %= GGML_QNT_VERSION_FACTOR;
|
|
switch (ftype) {
|
|
case 1: return true; // GGML_TYPE_F16
|
|
case 2: // GGML_TYPE_Q4_0
|
|
if (qntvr != GGML_QNT_VERSION)
|
|
{
|
|
std::cerr << "replit: not using metal (unsupported qnt ver)" << std::endl;
|
|
return false;
|
|
}
|
|
return true;
|
|
default: return false;
|
|
}
|
|
#else
|
|
return true;
|
|
#endif
|
|
}
|
|
|
|
DLL_EXPORT LLModel *construct() {
|
|
return new Replit;
|
|
}
|
|
}
|