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969 lines
32 KiB
C++
969 lines
32 KiB
C++
#define MPT_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE
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#include "mpt_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 <cstdio>
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#include <cstring>
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#include <map>
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#include <random>
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#include <string>
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#include <vector>
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#include <iostream>
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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 <sstream>
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#include <thread>
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#include <unordered_set>
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#include <regex>
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#include <ggml.h>
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namespace {
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const char *modelType_ = "MPT";
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}
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// default hparams (MPT 7B)
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struct mpt_hparams {
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int32_t n_vocab = 50432;
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int32_t n_ctx = 2048;
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int32_t n_embd = 4096;
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int32_t n_head = 32;
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int32_t n_layer = 32;
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float alibi_bias_max = 8;
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float clip_qkv = 0;
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float norm_eps = 1e-5;
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int32_t expand = 4;
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};
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struct mpt_layer {
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// normalization
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struct ggml_tensor * norm_1_w;
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struct ggml_tensor * norm_2_w;
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// attention
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struct ggml_tensor * attn_Wqkv_w;
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struct ggml_tensor * attn_out_proj_w;
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// ff
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struct ggml_tensor * ffn_up_proj_w;
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struct ggml_tensor * ffn_down_proj_w;
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};
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struct mpt_model {
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mpt_hparams hparams;
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// normalization
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struct ggml_tensor * norm_f_w;
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struct ggml_tensor * wte; // position embedding
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// mpt does weight tying
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std::vector<mpt_layer> layers;
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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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~mpt_model() {
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if (ctx) {
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ggml_free(ctx);
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}
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}
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};
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enum mpt_token_type {
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MPT_TOKEN_TYPE_NORMAL = 1,
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MPT_TOKEN_TYPE_CONTROL = 3,
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};
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using replit_piece_t = std::pair<std::size_t, float>;
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using replit_piece_map_t = std::unordered_map<std::string, replit_piece_t>;
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static const std::string replit_ws_symbol = "\342\226\201";
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struct mpt_vocab {
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bool is_replit = false;
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gpt_vocab raw;
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replit_piece_map_t piece_map;
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std::vector<std::string> vocab;
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const char * end_of_text() const {
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return is_replit ? "<|endoftext|>" : "<|im_end|>";
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}
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};
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std::pair<std::vector<LLModel::Token>, float> encode_word(const std::string & word, const replit_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(mpt_vocab & tokenizer, gguf_context * ggufctx, int tokens_keyidx, int max_vocab_size) {
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int scores_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.scores");
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if (scores_keyidx == -1) {
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fprintf(stderr, "%s: llama token scores not found!\n", __func__);
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return false;
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}
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const auto *scores = reinterpret_cast<const float *>(gguf_get_arr_data(ggufctx, scores_keyidx));
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for (LLModel::Token i = 0; i < max_vocab_size; i++) {
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std::string word = gguf_get_arr_str(ggufctx, tokens_keyidx, i);
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tokenizer.piece_map[word] = std::make_pair(i, -scores[i]);
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tokenizer.raw.id_to_token[i] = word;
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tokenizer.raw.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(mpt_vocab & tokenizer, const std::string & text) {
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std::vector<LLModel::Token> tokens;
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auto normalized_text = replace_all(text, " ", replit_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(mpt_vocab & 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.id_to_token[token];
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}
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return replace_all(text, replit_ws_symbol, " ");
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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 file path. if mem_req ptr is passed the model is
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// only partially parsed to estimate required memory
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bool mpt_model_load(const std::string &fname, mpt_model & model, mpt_vocab & 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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// create the ggml context
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struct gguf_init_params params = {
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/*.no_alloc = */ false,
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/*.ctx = */ &model.ctx,
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};
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gguf_context *ggufctx = gguf_init_from_file(fname.c_str(), params);
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if (!ggufctx) {
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fprintf(stderr, "%s: gguf_init_from_file() failed\n", __func__);
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return false;
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}
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printf("%s: gguf version = %d\n", __func__, gguf_get_version(ggufctx));
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printf("%s: gguf alignment = %zu\n", __func__, gguf_get_alignment(ggufctx));
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printf("%s: gguf data offset = %zu\n", __func__, gguf_get_data_offset(ggufctx));
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// print some standard metadata
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{
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int keyidx;
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keyidx = gguf_find_key(ggufctx, "general.name");
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if (keyidx != -1) { printf("%s: model name = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
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keyidx = gguf_find_key(ggufctx, "general.description");
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if (keyidx != -1) { printf("%s: model description = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
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keyidx = gguf_find_key(ggufctx, "general.author");
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if (keyidx != -1) { printf("%s: model author = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
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keyidx = gguf_find_key(ggufctx, "general.license");
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if (keyidx != -1) { printf("%s: model license = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
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keyidx = gguf_find_key(ggufctx, "general.architecture");
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if (keyidx != -1) { printf("%s: model architecture = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
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keyidx = gguf_find_key(ggufctx, "general.file_type");
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if (keyidx != -1) { printf("%s: model file type = %" PRIu32 "\n", __func__, gguf_get_val_u32(ggufctx, keyidx)); }
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keyidx = gguf_find_key(ggufctx, "gptneox.tensor_data_layout");
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if (keyidx != -1) { printf("%s: model data layout = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
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keyidx = gguf_find_key(ggufctx, "general.source.huggingface.repository");
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if (keyidx != -1) { printf("%s: model source HF repo = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
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}
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// check required metadata
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{
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// check model architecture kv
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int keyidx = gguf_find_key(ggufctx, "general.architecture");
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if (keyidx == -1) {
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fprintf(stderr, "%s: gguf model architecture not found!\n", __func__);
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return false;
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}
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if (strcmp(gguf_get_val_str(ggufctx, keyidx), "mpt") != 0) {
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fprintf(stderr, "%s: model architecture not supported!\n", __func__);
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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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bool ok = false;
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int keyidx;
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do {
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keyidx = gguf_find_key(ggufctx, "mpt.context_length");
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if (keyidx == -1) { break; }
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hparams.n_ctx = gguf_get_val_u32(ggufctx, keyidx);
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keyidx = gguf_find_key(ggufctx, "mpt.embedding_length");
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if (keyidx == -1) { break; }
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hparams.n_embd = gguf_get_val_u32(ggufctx, keyidx);
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keyidx = gguf_find_key(ggufctx, "mpt.attention.head_count");
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if (keyidx == -1) { break; }
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hparams.n_head = gguf_get_val_u32(ggufctx, keyidx);
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keyidx = gguf_find_key(ggufctx, "mpt.block_count");
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if (keyidx == -1) { break; }
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hparams.n_layer = gguf_get_val_u32(ggufctx, keyidx);
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keyidx = gguf_find_key(ggufctx, "mpt.attention.max_alibi_bias");
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if (keyidx == -1) { break; }
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hparams.alibi_bias_max = gguf_get_val_f32(ggufctx, keyidx);
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keyidx = gguf_find_key(ggufctx, "mpt.attention.clamp_kqv");
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if (keyidx != -1) { // optional
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hparams.clip_qkv = gguf_get_val_f32(ggufctx, keyidx);
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}
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keyidx = gguf_find_key(ggufctx, "mpt.attention.layer_norm_epsilon");
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if (keyidx == -1) { break; }
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hparams.norm_eps = gguf_get_val_f32(ggufctx, keyidx);
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ok = true;
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} while (false);
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if (!ok) {
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fprintf(stderr, "%s: required hparam missing!\n", __func__);
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return false;
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}
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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: alibi_bias_max = %f\n", __func__, hparams.alibi_bias_max);
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printf("%s: clip_qkv = %f\n", __func__, hparams.clip_qkv);
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}
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// load vocab
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{
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auto & hparams = model.hparams;
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int tokens_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.tokens");
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if (tokens_keyidx == -1) {
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fprintf(stderr, "%s: tokenizer vocab not found!\n", __func__);
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return false;
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}
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int keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.model");
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if (keyidx == -1) {
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fprintf(stderr, "%s: tokenizer model not found!\n", __func__);
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return false;
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}
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std::string tokenizer_model(gguf_get_val_str(ggufctx, keyidx));
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hparams.n_vocab = gguf_get_arr_n(ggufctx, tokens_keyidx);
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printf("%s: %s tokenizer vocab = %d\n", __func__, tokenizer_model.c_str(), int(hparams.n_vocab));
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if (tokenizer_model == "llama") { // Replit
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vocab.is_replit = true;
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if (!replit_tokenizer_load(vocab, ggufctx, tokens_keyidx, hparams.n_vocab)) {
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return false;
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}
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} else if (tokenizer_model == "gpt2") {
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int toktypes_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.token_type");
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if (toktypes_keyidx == -1) {
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fprintf(stderr, "%s: gpt2 token types not found!\n", __func__);
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return false;
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}
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const auto *toktypes = reinterpret_cast<const uint32_t *>(gguf_get_arr_data(ggufctx, toktypes_keyidx));
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for (int i = 0; i < hparams.n_vocab; i++) {
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std::string word = gguf_get_arr_str(ggufctx, tokens_keyidx, i);
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bool special = false;
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if (toktypes[i] == MPT_TOKEN_TYPE_CONTROL) {
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special = true;
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} else if (toktypes[i] != MPT_TOKEN_TYPE_NORMAL) {
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fprintf(stderr, "%s: unknown token type: %d\n", __func__, int(toktypes[i]));
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return false;
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}
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vocab.raw.token_to_id[word] = i;
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vocab.raw.id_to_token[i] = word;
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if (special) {
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vocab.raw.add_special_token(word);
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}
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}
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} else {
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fprintf(stderr, "%s: tokenizer model not supported!\n", __func__);
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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 = ggml_get_mem_size(ctx);
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printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size / (1024.0 * 1024.0));
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if (mem_req != nullptr) {
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*mem_req = ctx_size;
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gguf_free(ggufctx);
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return false;
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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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model.layers.resize(hparams.n_layer);
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model.wte = ggml_get_tensor(ctx, "token_embd.weight");
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model.norm_f_w = ggml_get_tensor(ctx, "output_norm.weight");
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auto name = [](int i, std::string n) {
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static std::string key;
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key = "blk." + std::to_string(i) + "." + n;
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return key.c_str();
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};
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for (int i = 0; i < hparams.n_layer; ++i) {
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auto &layer = model.layers[i];
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layer.norm_1_w = ggml_get_tensor(ctx, name(i, "attn_norm.weight"));
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layer.norm_2_w = ggml_get_tensor(ctx, name(i, "ffn_norm.weight"));
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layer.attn_Wqkv_w = ggml_get_tensor(ctx, name(i, "attn_qkv.weight"));
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layer.attn_out_proj_w = ggml_get_tensor(ctx, name(i, "attn_output.weight"));
|
|
layer.ffn_up_proj_w = ggml_get_tensor(ctx, name(i, "ffn_up.weight"));
|
|
layer.ffn_down_proj_w = ggml_get_tensor(ctx, name(i, "ffn_down.weight"));
|
|
}
|
|
}
|
|
|
|
// key + value memory
|
|
{
|
|
const auto &hparams = model.hparams;
|
|
if (!kv_cache_init(hparams, model.kv_self, GGML_TYPE_F16, model.hparams.n_ctx)) {
|
|
fprintf(stderr, "%s: kv_cache_init() failed for self-attention cache\n", __func__);
|
|
ggml_free(ctx);
|
|
return false;
|
|
}
|
|
|
|
const size_t memory_size = ggml_nbytes(model.kv_self.k) + ggml_nbytes(model.kv_self.v);
|
|
printf("%s: kv self size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
|
|
}
|
|
|
|
model.scr0_buf.resize(256u * 1024 * 1024);
|
|
model.scr1_buf.resize(256u * 1024 * 1024);
|
|
|
|
return true;
|
|
}
|
|
|
|
bool mpt_eval(
|
|
mpt_model & model,
|
|
const int n_threads,
|
|
const int n_past,
|
|
const std::vector<int> & 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;
|
|
|
|
const size_t init_buf_size = 1024_MiB;
|
|
if (!model.eval_buf.addr || model.eval_buf.size < init_buf_size)
|
|
model.eval_buf.resize(init_buf_size);
|
|
|
|
if (mem_per_token > 0 && mem_per_token*N > model.eval_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__, model.buf.size, buf_size_new);
|
|
|
|
// reallocate
|
|
model.eval_buf.resize(buf_size_new);
|
|
if (model.eval_buf.addr == nullptr) {
|
|
fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, model.eval_buf.size);
|
|
return false;
|
|
}
|
|
}
|
|
|
|
struct ggml_init_params params = {
|
|
.mem_size = model.eval_buf.size,
|
|
.mem_buffer = model.eval_buf.addr,
|
|
.no_alloc = false
|
|
};
|
|
|
|
struct ggml_context * ctx0 = ggml_init(params);
|
|
struct ggml_cgraph gf = {};
|
|
|
|
struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
|
|
memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd));
|
|
|
|
// wte
|
|
struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.wte, 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 * inpSA = inpL;
|
|
struct ggml_tensor * cur = inpSA;
|
|
// self-attention
|
|
{
|
|
|
|
// norm1
|
|
cur = ggml_norm(ctx0, cur, model.hparams.norm_eps);
|
|
cur = ggml_mul(ctx0,
|
|
ggml_repeat(ctx0, model.layers[il].norm_1_w, cur),
|
|
cur);
|
|
// compute QKV
|
|
cur = ggml_mul_mat(ctx0,
|
|
model.layers[il].attn_Wqkv_w,
|
|
cur);
|
|
|
|
// TODO: clip_qkv
|
|
struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 0*ggml_element_size(cur)*n_embd));
|
|
struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 1*ggml_element_size(cur)*n_embd));
|
|
struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 2*ggml_element_size(cur)*n_embd));
|
|
|
|
// TODO: qk_ln? (seems to be False in MPT-7B configs)
|
|
{
|
|
Vcur = ggml_transpose(ctx0, Vcur);
|
|
|
|
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_2d(ctx0, model.kv_self.v, N, n_embd,
|
|
( n_ctx)*ggml_element_size(model.kv_self.v),
|
|
(il*n_ctx)*ggml_element_size(model.kv_self.v)*n_embd + n_past*ggml_element_size(model.kv_self.v));
|
|
|
|
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
|
|
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
|
|
}
|
|
// Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3)
|
|
struct ggml_tensor * Q =
|
|
ggml_permute(ctx0,
|
|
ggml_reshape_3d(ctx0, Qcur, n_embd/n_head, n_head, N),
|
|
0, 2, 1, 3);
|
|
|
|
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.k)*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_biased = ggml_alibi(
|
|
ctx0, ggml_cont(ctx0, KQ_scaled), n_past, n_head, model.hparams.alibi_bias_max
|
|
);
|
|
|
|
// KQ_masked = mask_past(KQ_scaled)
|
|
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled_biased, n_past);
|
|
|
|
// KQ = soft_max(KQ_masked)
|
|
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
|
|
|
|
// V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous()
|
|
struct ggml_tensor * V =
|
|
ggml_view_3d(ctx0, model.kv_self.v,
|
|
n_past + N, n_embd/n_head, n_head,
|
|
n_ctx*ggml_element_size(model.kv_self.v),
|
|
n_ctx*ggml_element_size(model.kv_self.v)*n_embd/n_head,
|
|
il*n_ctx*ggml_element_size(model.kv_self.v)*n_embd);
|
|
|
|
// KQV = transpose(V) * KQ_soft_max
|
|
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
|
|
|
|
// KQV_merged = KQV.permute(0, 2, 1, 3)
|
|
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
|
|
|
|
// cur = KQV_merged.contiguous().view(n_embd, N)
|
|
cur = ggml_cpy(ctx0,
|
|
KQV_merged,
|
|
ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
|
|
|
|
// projection (no bias)
|
|
cur = ggml_mul_mat(ctx0,
|
|
model.layers[il].attn_out_proj_w,
|
|
cur);
|
|
}
|
|
|
|
ggml_set_scratch(ctx0, {0, model.scr1_buf.size, model.scr1_buf.addr, });
|
|
// residual
|
|
struct ggml_tensor * resSA = ggml_add(ctx0, cur, inpSA);
|
|
// feed-forward network
|
|
{
|
|
cur = resSA;
|
|
// norm2
|
|
cur = ggml_norm(ctx0, cur, model.hparams.norm_eps);
|
|
cur = ggml_mul(ctx0,
|
|
ggml_repeat(ctx0, model.layers[il].norm_2_w, cur),
|
|
cur);
|
|
// ffn
|
|
cur = ggml_mul_mat(ctx0,
|
|
model.layers[il].ffn_up_proj_w,
|
|
cur);
|
|
cur = ggml_gelu(ctx0, cur);
|
|
cur = ggml_mul_mat(ctx0,
|
|
model.layers[il].ffn_down_proj_w,
|
|
cur);
|
|
|
|
}
|
|
|
|
// self-attention + FF
|
|
inpL = ggml_add(ctx0, cur, resSA);
|
|
}
|
|
ggml_set_scratch(ctx0, {0, model.scr0_buf.size, model.scr0_buf.addr, });
|
|
|
|
struct ggml_tensor * out = inpL;
|
|
// -> logits
|
|
{
|
|
out = ggml_norm(ctx0, out, model.hparams.norm_eps);
|
|
out = ggml_mul(ctx0,
|
|
ggml_repeat(ctx0, model.norm_f_w, out),
|
|
out);
|
|
ggml_set_scratch(ctx0, { 0, 0, nullptr, });
|
|
out = ggml_mul_mat(ctx0, model.wte, out);
|
|
}
|
|
|
|
ggml_build_forward_expand(&gf, out);
|
|
|
|
|
|
// run the computation
|
|
{
|
|
std::unique_ptr<uint8_t []> data;
|
|
auto plan = ggml_graph_plan(&gf, n_threads);
|
|
if (plan.work_size > 0) {
|
|
data.reset(new uint8_t[plan.work_size]);
|
|
plan.work_data = data.get();
|
|
}
|
|
ggml_graph_compute(&gf, &plan);
|
|
}
|
|
|
|
|
|
// return result for just the last token
|
|
embd_w.resize(n_vocab);
|
|
memcpy(embd_w.data(), (float *) ggml_get_data(out) + (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 MPT_MAX_RNG_STATE 64*1024
|
|
|
|
size_t mpt_get_state_size(const mpt_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 = MPT_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 mpt_copy_state_data(const mpt_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[MPT_MAX_RNG_STATE];
|
|
|
|
memset(&rng_buf[0], 0, MPT_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], MPT_MAX_RNG_STATE); out += MPT_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;
|
|
assert(written == mpt_get_state_size(model));
|
|
fflush(stdout);
|
|
return written;
|
|
}
|
|
|
|
size_t mpt_set_state_data(mpt_model *model, std::mt19937 *rng, const uint8_t *src)
|
|
{
|
|
const uint8_t * in = src;
|
|
|
|
// set rng
|
|
{
|
|
size_t rng_size;
|
|
char rng_buf[MPT_MAX_RNG_STATE];
|
|
|
|
memcpy(&rng_size, in, sizeof(rng_size)); in += sizeof(rng_size);
|
|
memcpy(&rng_buf[0], in, MPT_MAX_RNG_STATE); in += MPT_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;
|
|
assert(nread == mpt_get_state_size(*model));
|
|
fflush(stdout);
|
|
return nread;
|
|
}
|
|
|
|
struct MPTPrivate {
|
|
const std::string modelPath;
|
|
bool modelLoaded;
|
|
mpt_vocab vocab;
|
|
mpt_model *model = nullptr;
|
|
int64_t n_threads = 0;
|
|
size_t mem_per_token = 0;
|
|
std::mt19937 rng;
|
|
bool has_end_of_text = false;
|
|
};
|
|
|
|
MPT::MPT()
|
|
: d_ptr(new MPTPrivate) {
|
|
d_ptr->model = new mpt_model;
|
|
d_ptr->model->ctx = nullptr;
|
|
d_ptr->modelLoaded = false;
|
|
}
|
|
|
|
size_t MPT::requiredMem(const std::string &modelPath) {
|
|
mpt_model dummy_model;
|
|
mpt_vocab dummy_vocab;
|
|
size_t mem_req;
|
|
mpt_model_load(modelPath, dummy_model, dummy_vocab, &mem_req);
|
|
return mem_req;
|
|
}
|
|
|
|
bool MPT::loadModel(const std::string &modelPath) {
|
|
std::mt19937 rng(time(NULL));
|
|
d_ptr->rng = rng;
|
|
|
|
// load the model
|
|
if (!mpt_model_load(modelPath, *d_ptr->model, d_ptr->vocab, nullptr)) {
|
|
std::cerr << "MPT 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;
|
|
const auto & vocab = d_ptr->vocab;
|
|
d_ptr->has_end_of_text = vocab.raw.token_to_id.find(vocab.end_of_text()) != vocab.raw.token_to_id.end();
|
|
fflush(stdout);
|
|
return true;
|
|
}
|
|
|
|
void MPT::setThreadCount(int32_t n_threads) {
|
|
d_ptr->n_threads = n_threads;
|
|
}
|
|
|
|
int32_t MPT::threadCount() const
|
|
{
|
|
return d_ptr->n_threads;
|
|
}
|
|
|
|
MPT::~MPT()
|
|
{
|
|
delete d_ptr->model;
|
|
}
|
|
|
|
bool MPT::isModelLoaded() const
|
|
{
|
|
return d_ptr->modelLoaded;
|
|
}
|
|
|
|
size_t MPT::stateSize() const
|
|
{
|
|
return mpt_get_state_size(*d_ptr->model);
|
|
}
|
|
|
|
size_t MPT::saveState(uint8_t *dest) const
|
|
{
|
|
return mpt_copy_state_data(*d_ptr->model, d_ptr->rng, dest);
|
|
}
|
|
|
|
size_t MPT::restoreState(const uint8_t *src)
|
|
{
|
|
return mpt_set_state_data(d_ptr->model, &d_ptr->rng, src);
|
|
}
|
|
|
|
std::vector<LLModel::Token> MPT::tokenize(PromptContext &, const std::string &str) const
|
|
{
|
|
if (d_ptr->vocab.is_replit) {
|
|
return replit_tokenizer_tokenize(d_ptr->vocab, str);
|
|
}
|
|
return ::gpt_tokenize(d_ptr->vocab.raw, str);
|
|
}
|
|
|
|
std::string MPT::tokenToString(Token id) const
|
|
{
|
|
if (d_ptr->vocab.is_replit) {
|
|
return replit_tokenizer_detokenize(d_ptr->vocab, {id});
|
|
}
|
|
return d_ptr->vocab.raw.id_to_token[id];
|
|
}
|
|
|
|
LLModel::Token MPT::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 MPT::evalTokens(PromptContext &ctx, const std::vector<int32_t> &tokens) const
|
|
{
|
|
// determine the required inference memory per token:
|
|
static bool initialized = false;
|
|
if (!initialized) {
|
|
mpt_eval(*d_ptr->model, d_ptr->n_threads, 0, { 0, 1, 2, 3 }, ctx.logits,
|
|
d_ptr->mem_per_token);
|
|
initialized = true;
|
|
}
|
|
|
|
return mpt_eval(*d_ptr->model, d_ptr->n_threads, ctx.n_past, tokens, ctx.logits, d_ptr->mem_per_token);
|
|
}
|
|
|
|
int32_t MPT::contextLength() const
|
|
{
|
|
return d_ptr->model->hparams.n_ctx;
|
|
}
|
|
|
|
const std::vector<LLModel::Token> &MPT::endTokens() const
|
|
{
|
|
static std::vector<LLModel::Token> fres;
|
|
if (fres.empty()) {
|
|
fres = {0, d_ptr->vocab.raw.token_to_id[d_ptr->vocab.end_of_text()]};
|
|
}
|
|
return fres;
|
|
}
|
|
|
|
std::string get_arch_name(gguf_context *ctx_gguf) {
|
|
std::string arch_name;
|
|
const int kid = gguf_find_key(ctx_gguf, "general.architecture");
|
|
enum gguf_type ktype = gguf_get_kv_type(ctx_gguf, kid);
|
|
if (ktype != GGUF_TYPE_STRING) {
|
|
throw std::runtime_error("ERROR: Can't get general architecture from gguf file.");
|
|
}
|
|
return gguf_get_val_str(ctx_gguf, kid);
|
|
}
|
|
|
|
#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(const char * fname) {
|
|
struct ggml_context * ctx_meta = NULL;
|
|
struct gguf_init_params params = {
|
|
/*.no_alloc = */ true,
|
|
/*.ctx = */ &ctx_meta,
|
|
};
|
|
gguf_context *ctx_gguf = gguf_init_from_file(fname, params);
|
|
if (!ctx_gguf)
|
|
return false;
|
|
|
|
bool isValid = gguf_get_version(ctx_gguf) <= 2;
|
|
isValid = isValid && get_arch_name(ctx_gguf) == "mpt";
|
|
|
|
gguf_free(ctx_gguf);
|
|
return isValid;
|
|
}
|
|
|
|
DLL_EXPORT LLModel *construct() {
|
|
return new MPT;
|
|
}
|
|
}
|