mirror of https://github.com/nomic-ai/gpt4all
implement local Nomic Embed via llama.cpp (#2086)
Signed-off-by: Jared Van Bortel <jared@nomic.ai>pull/2053/head
Jared Van Bortel
2 months ago
committed by
GitHub
parent
171f4e488e
commit
406e88b59a
@ -1,910 +0,0 @@
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#define BERT_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE
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#include "bert_impl.h"
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#include "llmodel_shared.h"
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#include "ggml.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 <string>
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#include <vector>
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#include <iostream>
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#include <regex>
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#include <thread>
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#include <algorithm>
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#include <numeric>
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//#define DEBUG_BERT
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namespace {
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const char *modelType_ = "Bert";
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}
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typedef int32_t bert_vocab_id;
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// default hparams (all-MiniLM-L6-v2)
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struct bert_hparams
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{
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int32_t n_vocab = 30522;
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int32_t n_max_tokens = 512;
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int32_t n_embd = 256;
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int32_t n_intermediate = 1536;
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int32_t n_head = 12;
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int32_t n_layer = 6;
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};
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struct bert_layer
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{
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// normalization
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struct ggml_tensor *ln_att_w;
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struct ggml_tensor *ln_att_b;
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struct ggml_tensor *ln_out_w;
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struct ggml_tensor *ln_out_b;
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// attention
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struct ggml_tensor *q_w;
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struct ggml_tensor *q_b;
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struct ggml_tensor *k_w;
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struct ggml_tensor *k_b;
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struct ggml_tensor *v_w;
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struct ggml_tensor *v_b;
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struct ggml_tensor *o_w;
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struct ggml_tensor *o_b;
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// ff
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struct ggml_tensor *ff_i_w;
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struct ggml_tensor *ff_i_b;
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struct ggml_tensor *ff_o_w;
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struct ggml_tensor *ff_o_b;
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};
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struct bert_vocab
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{
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std::map<std::string, bert_vocab_id> token_to_id;
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std::map<std::string, bert_vocab_id> subword_token_to_id;
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std::map<bert_vocab_id, std::string> _id_to_token;
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std::map<bert_vocab_id, std::string> _id_to_subword_token;
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};
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struct bert_model
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{
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bert_hparams hparams;
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// embeddings weights
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struct ggml_tensor *word_embeddings;
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struct ggml_tensor *token_type_embeddings;
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struct ggml_tensor *position_embeddings;
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struct ggml_tensor *ln_e_w;
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struct ggml_tensor *ln_e_b;
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std::vector<bert_layer> layers;
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struct ggml_context *ctx;
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};
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// Replacement for std::vector<uint8_t> that doesn't require zero-initialization.
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struct bert_ctx
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{
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bert_model model;
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bert_vocab vocab;
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size_t mem_per_token;
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int64_t mem_per_input;
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int32_t max_batch_n;
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llm_buffer buf_compute;
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llm_buffer work_buf;
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};
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int32_t bert_n_embd(bert_ctx * ctx)
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{
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return ctx->model.hparams.n_embd;
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}
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int32_t bert_n_max_tokens(bert_ctx * ctx)
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{
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return ctx->model.hparams.n_max_tokens;
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}
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const char* bert_vocab_id_to_token(bert_ctx * ctx, bert_vocab_id id) {
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bert_vocab & vocab = ctx->vocab;
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auto it = vocab._id_to_token.find(id);
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if (it != vocab._id_to_token.end())
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{
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return it->second.c_str();
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}
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it = vocab._id_to_subword_token.find(id);
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if (it != vocab._id_to_subword_token.end())
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{
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return it->second.c_str();
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}
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return "[UNK TOKEN from bert_vocab]";
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}
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//
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// Tokenizing
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//
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static size_t utf8_len(char src)
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{
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const size_t lookup[] = {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4};
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uint8_t highbits = static_cast<uint8_t>(src) >> 4;
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return lookup[highbits];
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}
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std::string stripAccents(const std::string &inputString)
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{
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std::string resultString;
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std::map<std::string, char> accentMap = {{"À", 'A'},{"Á", 'A'},
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{"Â", 'A'},{"Ã", 'A'},{"Ä", 'A'},{"Å", 'A'},{"à", 'a'},{"á", 'a'},
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{"â", 'a'},{"ã", 'a'},{"ä", 'a'},{"å", 'a'},{"È", 'E'},{"É", 'E'},
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{"Ê", 'E'},{"Ë", 'E'},{"è", 'e'},{"é", 'e'},{"ê", 'e'},{"ë", 'e'},
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{"Ì", 'I'},{"Í", 'I'},{"Î", 'I'},{"Ï", 'I'},{"ì", 'i'},{"í", 'i'},
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{"î", 'i'},{"ï", 'i'},{"Ò", 'O'},{"Ó", 'O'},{"Ô", 'O'},{"Õ", 'O'},
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{"Ö", 'O'},{"ò", 'o'},{"ó", 'o'},{"ô", 'o'},{"õ", 'o'},{"ö", 'o'},
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{"Ù", 'U'},{"Ú", 'U'},{"Û", 'U'},{"Ü", 'U'},{"ù", 'u'},{"ú", 'u'},
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{"û", 'u'},{"ü", 'u'},{"Ý", 'Y'},{"ý", 'y'},{"Ç", 'C'},{"ç", 'c'},
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{"Ñ", 'N'},{"ñ", 'n'},
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};
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for (size_t i = 0; i < inputString.length();)
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{
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int len = utf8_len(inputString[i]);
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std::string curChar = inputString.substr(i, len);
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auto iter = accentMap.find(curChar);
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if (iter != accentMap.end())
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{
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resultString += iter->second;
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}
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else
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{
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resultString += curChar;
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}
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i += len;
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}
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return resultString;
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}
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std::string bert_normalize_prompt(const std::string &text)
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{
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// TODO: handle chinese characters? https://github.com/huggingface/tokenizers/blob/ef5f50605ddf9f8caef1598c0e4853862b9707a7/tokenizers/src/normalizers/bert.rs#L98
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std::string text2 = stripAccents(text);
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for (size_t i = 0; i < text2.size(); i += utf8_len(text2[i]))
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{
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char c = text2[i];
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if (c >= 'A' && c <= 'Z')
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text2[i] = c - 'A' + 'a';
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}
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return text2;
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}
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std::vector<bert_vocab_id> bert_tokenize(
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struct bert_ctx * ctx,
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const char * text)
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{
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const bert_vocab &vocab = ctx->vocab;
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std::string str = text;
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std::vector<std::string> words;
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// first split the text into words
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{
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str = bert_normalize_prompt(str);
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std::string pat = R"([[:punct:]]|[[:alpha:]]+|[[:digit:]]+)";
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std::regex re(pat);
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std::smatch m;
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while (std::regex_search(str, m, re))
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{
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for (std::string x : m)
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{
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words.push_back(x);
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}
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str = m.suffix();
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}
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}
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// find the longest tokens that form the words:
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std::vector<bert_vocab_id> tokens;
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int cls_tok_id = 101;
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tokens.push_back(cls_tok_id);
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for (const auto &word : words)
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{
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if (word.size() == 0)
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continue;
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int i = 0;
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int n = word.size();
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auto *token_map = &vocab.token_to_id;
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while (i < n)
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{
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int j = n;
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while (j > i)
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{
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auto it = token_map->find(word.substr(i, j - i));
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if (it != token_map->end())
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{
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tokens.push_back(it->second);
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i = j;
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token_map = &vocab.subword_token_to_id;
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}
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--j;
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}
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if (j == i)
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{
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fprintf(stderr, "%s: unknown token '%s'\n", __func__, word.substr(i, 1).data());
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token_map = &vocab.subword_token_to_id;
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++i;
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}
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}
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}
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return tokens;
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}
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void bert_resize_ctx(bert_ctx * ctx, int32_t new_size) {
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int64_t buf_size_new = ctx->mem_per_input * new_size;
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// TODO: Max memory should be a param? Now just 1 GB
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int64_t GB = 1 << 30;
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#if defined(DEBUG_BERT)
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printf("%s: requested_buf_size %lldMB\n", __func__, buf_size_new / (1 << 20));
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#endif
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if (buf_size_new > GB) {
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int32_t adjusted_new_size = GB / ctx->mem_per_input;
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if (adjusted_new_size < 1) adjusted_new_size = 1;
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#if defined(DEBUG_BERT)
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printf("%s: requested batch size %d, actual new batch size %d\n", __func__, new_size, adjusted_new_size);
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#endif
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new_size = adjusted_new_size;
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buf_size_new = ctx->mem_per_input * new_size;
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}
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if (new_size > ctx->max_batch_n) {
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ctx->buf_compute.resize(buf_size_new);
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ctx->max_batch_n = new_size;
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}
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}
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void bert_eval(
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struct bert_ctx *ctx,
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int32_t n_threads,
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const bert_vocab_id *raw_tokens,
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int32_t n_tokens,
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float *embeddings)
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{
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const bert_model& model = ctx->model;
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bool mem_req_mode = !embeddings;
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// batch_embeddings is nullptr for the initial memory requirements run
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if (!mem_req_mode && 1 > ctx->max_batch_n)
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bert_resize_ctx(ctx, 1);
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const int N = n_tokens;
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const auto &tokens = raw_tokens;
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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_max_tokens = hparams.n_max_tokens;
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const int n_head = hparams.n_head;
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const int d_head = n_embd / n_head;
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std::vector<float> result;
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if (N > n_max_tokens)
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{
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fprintf(stderr, "Too many tokens, maximum is %d\n", n_max_tokens);
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return;
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}
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auto & mem_per_token = ctx->mem_per_token;
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auto & buf_compute = ctx->buf_compute;
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struct ggml_init_params params = {
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.mem_size = buf_compute.size,
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.mem_buffer = buf_compute.addr,
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.no_alloc = false,
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};
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struct ggml_context *ctx0 = ggml_init(params);
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struct ggml_cgraph *gf = ggml_new_graph(ctx0);
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// Embeddings. word_embeddings + token_type_embeddings + position_embeddings
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struct ggml_tensor *token_layer = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
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memcpy(token_layer->data, tokens, N * ggml_element_size(token_layer));
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struct ggml_tensor *token_types = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
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ggml_set_zero(token_types);
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struct ggml_tensor *positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
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for (int i = 0; i < N; i++)
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{
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ggml_set_i32_1d(positions, i, i);
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}
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struct ggml_tensor *inpL = ggml_get_rows(ctx0, model.word_embeddings, token_layer);
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inpL = ggml_add(ctx0,
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ggml_get_rows(ctx0, model.token_type_embeddings, token_types),
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inpL);
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inpL = ggml_add(ctx0,
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ggml_get_rows(ctx0, model.position_embeddings, positions),
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inpL);
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// embd norm
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{
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inpL = ggml_norm(ctx0, inpL, 1e-12f);
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inpL = ggml_add(ctx0,
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ggml_mul(ctx0,
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ggml_repeat(ctx0, model.ln_e_w, inpL),
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inpL),
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ggml_repeat(ctx0, model.ln_e_b, inpL));
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}
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// layers
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for (int il = 0; il < n_layer; il++)
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{
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struct ggml_tensor *cur = inpL;
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// self-attention
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{
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struct ggml_tensor *Qcur = cur;
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Qcur = ggml_reshape_3d(ctx0,
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ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].q_b, Qcur),
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ggml_mul_mat(ctx0, model.layers[il].q_w, Qcur)),
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d_head, n_head, N);
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struct ggml_tensor *Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
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struct ggml_tensor *Kcur = cur;
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Kcur = ggml_reshape_3d(ctx0,
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ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].k_b, Kcur),
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ggml_mul_mat(ctx0, model.layers[il].k_w, Kcur)),
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d_head, n_head, N);
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struct ggml_tensor *K = ggml_permute(ctx0, Kcur, 0, 2, 1, 3);
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struct ggml_tensor *Vcur = cur;
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Vcur = ggml_reshape_3d(ctx0,
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ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].v_b, Vcur),
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ggml_mul_mat(ctx0, model.layers[il].v_w, Vcur)),
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d_head, n_head, N);
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struct ggml_tensor *V = ggml_permute(ctx0, Vcur, 0, 2, 1, 3);
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struct ggml_tensor *KQ = ggml_mul_mat(ctx0, K, Q);
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// KQ = soft_max(KQ / sqrt(head width))
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KQ = ggml_soft_max(
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ctx0, ggml_scale(ctx0, KQ, 1.0f / sqrt((float)d_head))
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);
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V = ggml_cont(ctx0, ggml_transpose(ctx0, V));
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struct ggml_tensor *KQV = ggml_mul_mat(ctx0, V, KQ);
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KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
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cur = ggml_cpy(ctx0,
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KQV,
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ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
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}
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// attention output
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cur = ggml_add(ctx0,
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ggml_repeat(ctx0, model.layers[il].o_b, cur),
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ggml_mul_mat(ctx0, model.layers[il].o_w, cur));
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// re-add the layer input
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cur = ggml_add(ctx0, cur, inpL);
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// attention norm
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{
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cur = ggml_norm(ctx0, cur, 1e-12f);
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cur = ggml_add(ctx0,
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ggml_mul(ctx0,
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ggml_repeat(ctx0, model.layers[il].ln_att_w, cur),
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cur),
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ggml_repeat(ctx0, model.layers[il].ln_att_b, cur));
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}
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struct ggml_tensor *att_output = cur;
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// intermediate_output = self.intermediate(attention_output)
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cur = ggml_mul_mat(ctx0, model.layers[il].ff_i_w, cur);
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cur = ggml_add(ctx0,
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ggml_repeat(ctx0, model.layers[il].ff_i_b, cur),
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cur);
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cur = ggml_gelu(ctx0, cur);
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// layer_output = self.output(intermediate_output, attention_output)
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cur = ggml_mul_mat(ctx0, model.layers[il].ff_o_w, cur);
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cur = ggml_add(ctx0,
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ggml_repeat(ctx0, model.layers[il].ff_o_b, cur),
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cur);
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// attentions bypass the intermediate layer
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cur = ggml_add(ctx0, att_output, cur);
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// output norm
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{
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cur = ggml_norm(ctx0, cur, 1e-12f);
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cur = ggml_add(ctx0,
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ggml_mul(ctx0,
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ggml_repeat(ctx0, model.layers[il].ln_out_w, cur),
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cur),
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ggml_repeat(ctx0, model.layers[il].ln_out_b, cur));
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}
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inpL = cur;
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}
|
||||
inpL = ggml_cont(ctx0, ggml_transpose(ctx0, inpL));
|
||||
// pooler
|
||||
struct ggml_tensor *sum = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, N, 1);
|
||||
ggml_set_f32(sum, 1.0f / N);
|
||||
inpL = ggml_mul_mat(ctx0, inpL, sum);
|
||||
|
||||
ggml_tensor *output = inpL;
|
||||
// run the computation
|
||||
ggml_build_forward_expand(gf, output);
|
||||
//ggml_graph_compute_g4a()
|
||||
ggml_graph_compute_g4a(ctx->work_buf, gf, n_threads);
|
||||
//ggml_graph_compute(ctx0, gf);
|
||||
|
||||
|
||||
// float *dat = ggml_get_data_f32(output);
|
||||
// pretty_print_tensor(dat, output->ne, output->nb, output->n_dims - 1, "");
|
||||
|
||||
#ifdef GGML_PERF
|
||||
// print timing information per ggml operation (for debugging purposes)
|
||||
// requires GGML_PERF to be defined
|
||||
ggml_graph_print(gf);
|
||||
#endif
|
||||
|
||||
if (!mem_req_mode) {
|
||||
memcpy(embeddings, (float *)ggml_get_data(output), sizeof(float) * n_embd);
|
||||
} else {
|
||||
mem_per_token = ggml_used_mem(ctx0) / N;
|
||||
}
|
||||
|
||||
// printf("used_mem = %zu KB \n", ggml_used_mem(ctx0) / 1024);
|
||||
// printf("mem_per_token = %zu KB \n", mem_per_token / 1024);
|
||||
|
||||
ggml_free(ctx0);
|
||||
}
|
||||
|
||||
//
|
||||
// Loading and setup
|
||||
//
|
||||
|
||||
void bert_free(bert_ctx * ctx) {
|
||||
delete ctx;
|
||||
}
|
||||
|
||||
struct bert_ctx * bert_load_from_file(const char *fname)
|
||||
{
|
||||
#if defined(DEBUG_BERT)
|
||||
printf("%s: loading model from '%s' - please wait ...\n", __func__, fname);
|
||||
#endif
|
||||
|
||||
bert_ctx * new_bert = new bert_ctx;
|
||||
|
||||
bert_model & model = new_bert->model;
|
||||
bert_vocab & vocab = new_bert->vocab;
|
||||
|
||||
struct gguf_init_params params = {
|
||||
/*.no_alloc = */ false,
|
||||
/*.ctx = */ &model.ctx,
|
||||
};
|
||||
gguf_context *ggufctx = gguf_init_from_file(fname, params);
|
||||
if (!ggufctx) {
|
||||
fprintf(stderr, "%s: gguf_init_from_file() failed\n", __func__);
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
printf("%s: gguf version = %d\n", __func__, gguf_get_version(ggufctx));
|
||||
printf("%s: gguf alignment = %zu\n", __func__, gguf_get_alignment(ggufctx));
|
||||
printf("%s: gguf data offset = %zu\n", __func__, gguf_get_data_offset(ggufctx));
|
||||
|
||||
// print some standard metadata
|
||||
{
|
||||
int keyidx;
|
||||
|
||||
keyidx = gguf_find_key(ggufctx, "general.name");
|
||||
if (keyidx != -1) { printf("%s: model name = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
keyidx = gguf_find_key(ggufctx, "general.description");
|
||||
if (keyidx != -1) { printf("%s: model description = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
keyidx = gguf_find_key(ggufctx, "general.author");
|
||||
if (keyidx != -1) { printf("%s: model author = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
keyidx = gguf_find_key(ggufctx, "general.license");
|
||||
if (keyidx != -1) { printf("%s: model license = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
keyidx = gguf_find_key(ggufctx, "general.architecture");
|
||||
if (keyidx != -1) { printf("%s: model architecture = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
keyidx = gguf_find_key(ggufctx, "general.file_type");
|
||||
if (keyidx != -1) { printf("%s: model file type = %" PRIu32 "\n", __func__, gguf_get_val_u32(ggufctx, keyidx)); }
|
||||
keyidx = gguf_find_key(ggufctx, "gptneox.tensor_data_layout");
|
||||
if (keyidx != -1) { printf("%s: model data layout = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
keyidx = gguf_find_key(ggufctx, "general.source.huggingface.repository");
|
||||
if (keyidx != -1) { printf("%s: model source HF repo = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
}
|
||||
|
||||
// check required metadata
|
||||
{
|
||||
// check model architecture kv
|
||||
int keyidx = gguf_find_key(ggufctx, "general.architecture");
|
||||
if (keyidx == -1) {
|
||||
fprintf(stderr, "%s: gguf model architecture not found!\n", __func__);
|
||||
return nullptr;
|
||||
}
|
||||
if (strcmp(gguf_get_val_str(ggufctx, keyidx), "bert") != 0) {
|
||||
fprintf(stderr, "%s: model architecture not supported!\n", __func__);
|
||||
return nullptr;
|
||||
}
|
||||
}
|
||||
|
||||
// load hparams
|
||||
{
|
||||
auto &hparams = model.hparams;
|
||||
|
||||
bool ok = false;
|
||||
int keyidx;
|
||||
|
||||
do {
|
||||
keyidx = gguf_find_key(ggufctx, "bert.context_length");
|
||||
if (keyidx == -1) { break; }
|
||||
hparams.n_max_tokens = gguf_get_val_u32(ggufctx, keyidx);
|
||||
|
||||
keyidx = gguf_find_key(ggufctx, "bert.embedding_length");
|
||||
if (keyidx == -1) { break; }
|
||||
hparams.n_embd = gguf_get_val_u32(ggufctx, keyidx);
|
||||
|
||||
keyidx = gguf_find_key(ggufctx, "bert.feed_forward_length");
|
||||
if (keyidx == -1) { break; }
|
||||
hparams.n_intermediate = gguf_get_val_u32(ggufctx, keyidx);
|
||||
|
||||
keyidx = gguf_find_key(ggufctx, "bert.attention.head_count");
|
||||
if (keyidx == -1) { break; }
|
||||
hparams.n_head = gguf_get_val_u32(ggufctx, keyidx);
|
||||
|
||||
keyidx = gguf_find_key(ggufctx, "bert.block_count");
|
||||
if (keyidx == -1) { break; }
|
||||
hparams.n_layer = gguf_get_val_u32(ggufctx, keyidx);
|
||||
|
||||
ok = true;
|
||||
} while (false);
|
||||
|
||||
if (!ok) {
|
||||
fprintf(stderr, "%s: required hparam missing!\n", __func__);
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
#if defined(DEBUG_BERT)
|
||||
printf("%s: n_max_tokens = %d\n", __func__, hparams.n_max_tokens);
|
||||
printf("%s: n_embd = %d\n", __func__, hparams.n_embd);
|
||||
printf("%s: n_intermediate = %d\n", __func__, hparams.n_intermediate);
|
||||
printf("%s: n_head = %d\n", __func__, hparams.n_head);
|
||||
printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
|
||||
#endif
|
||||
}
|
||||
|
||||
// load vocab
|
||||
{
|
||||
auto & hparams = model.hparams;
|
||||
|
||||
int keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.model");
|
||||
if (keyidx == -1) {
|
||||
fprintf(stderr, "%s: tokenizer model not found!\n", __func__);
|
||||
return nullptr;
|
||||
}
|
||||
if (strcmp(gguf_get_val_str(ggufctx, keyidx), "bert") != 0) {
|
||||
fprintf(stderr, "%s: tokenizer model not supported!\n", __func__);
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
int tokens_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.tokens");
|
||||
if (tokens_keyidx == -1) {
|
||||
fprintf(stderr, "%s: bert tokenizer vocab not found!\n", __func__);
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
hparams.n_vocab = gguf_get_arr_n(ggufctx, tokens_keyidx);
|
||||
printf("%s: bert tokenizer vocab = %d\n", __func__, int(hparams.n_vocab));
|
||||
|
||||
for (int i = 0; i < hparams.n_vocab; i++) {
|
||||
std::string word = gguf_get_arr_str(ggufctx, tokens_keyidx, i);
|
||||
|
||||
if (word[0] == '#' && word[1] == '#')
|
||||
{
|
||||
vocab.subword_token_to_id[word.substr(2)] = i;
|
||||
vocab._id_to_subword_token[i] = word;
|
||||
}
|
||||
|
||||
if (vocab.token_to_id.count(word) == 0)
|
||||
{
|
||||
vocab.token_to_id[word] = i;
|
||||
vocab._id_to_token[i] = word;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
auto &ctx = model.ctx;
|
||||
|
||||
#if defined(DEBUG_BERT)
|
||||
printf("%s: ggml ctx size = %6.2f MB\n", __func__, ggml_get_mem_size(ctx) / (1024.0 * 1024.0));
|
||||
#endif
|
||||
|
||||
// prepare memory for the weights
|
||||
{
|
||||
const int n_layer = model.hparams.n_layer;
|
||||
model.layers.resize(n_layer);
|
||||
|
||||
model.word_embeddings = ggml_get_tensor(ctx, "token_embd.weight");
|
||||
model.token_type_embeddings = ggml_get_tensor(ctx, "token_types.weight");
|
||||
model.position_embeddings = ggml_get_tensor(ctx, "position_embd.weight");
|
||||
model.ln_e_w = ggml_get_tensor(ctx, "output_norm.weight");
|
||||
model.ln_e_b = ggml_get_tensor(ctx, "output_norm.bias");
|
||||
|
||||
auto name = [](int i, std::string n) {
|
||||
static std::string key;
|
||||
key = "blk." + std::to_string(i) + "." + n;
|
||||
return key.c_str();
|
||||
};
|
||||
|
||||
for (int i = 0; i < n_layer; ++i)
|
||||
{
|
||||
auto &layer = model.layers[i];
|
||||
|
||||
layer.ln_att_w = ggml_get_tensor(ctx, name(i, "attn_norm.weight"));
|
||||
layer.ln_att_b = ggml_get_tensor(ctx, name(i, "attn_norm.bias"));
|
||||
layer.ln_out_w = ggml_get_tensor(ctx, name(i, "ffn_norm.weight"));
|
||||
layer.ln_out_b = ggml_get_tensor(ctx, name(i, "ffn_norm.bias"));
|
||||
layer.q_w = ggml_get_tensor(ctx, name(i, "attn_q.weight"));
|
||||
layer.q_b = ggml_get_tensor(ctx, name(i, "attn_q.bias"));
|
||||
layer.k_w = ggml_get_tensor(ctx, name(i, "attn_k.weight"));
|
||||
layer.k_b = ggml_get_tensor(ctx, name(i, "attn_k.bias"));
|
||||
layer.v_w = ggml_get_tensor(ctx, name(i, "attn_v.weight"));
|
||||
layer.v_b = ggml_get_tensor(ctx, name(i, "attn_v.bias"));
|
||||
layer.o_w = ggml_get_tensor(ctx, name(i, "attn_output.weight"));
|
||||
layer.o_b = ggml_get_tensor(ctx, name(i, "attn_output.bias"));
|
||||
layer.ff_i_w = ggml_get_tensor(ctx, name(i, "ffn_up.weight"));
|
||||
layer.ff_i_b = ggml_get_tensor(ctx, name(i, "ffn_up.bias"));
|
||||
layer.ff_o_w = ggml_get_tensor(ctx, name(i, "ffn_down.weight"));
|
||||
layer.ff_o_b = ggml_get_tensor(ctx, name(i, "ffn_down.bias"));
|
||||
}
|
||||
}
|
||||
|
||||
// Calculate space requirements for setting up context buffers later
|
||||
{
|
||||
bert_vocab_id tokens[] = {0, 1, 2, 3};
|
||||
// TODO: We set the initial buffer size to 16MB and hope it's enough. Maybe there is a better way to do this?
|
||||
new_bert->buf_compute.resize(16 * 1024 * 1024);
|
||||
bert_eval(new_bert, 1, tokens, 4, nullptr);
|
||||
new_bert->max_batch_n = 0;
|
||||
|
||||
// TODO: Max tokens should be a param?
|
||||
int32_t N = new_bert->model.hparams.n_max_tokens;
|
||||
new_bert->mem_per_input = 2.2 * (new_bert->mem_per_token * N); // add 10% to account for ggml object overhead
|
||||
|
||||
}
|
||||
#if defined(DEBUG_BERT)
|
||||
printf("%s: mem_per_token %ld KB, mem_per_input %ld MB\n", __func__, new_bert->mem_per_token / (1 << 10), new_bert->mem_per_input / (1 << 20));
|
||||
#endif
|
||||
|
||||
return new_bert;
|
||||
}
|
||||
|
||||
struct BertPrivate {
|
||||
const std::string modelPath;
|
||||
bool modelLoaded;
|
||||
bert_ctx *ctx = nullptr;
|
||||
int64_t n_threads = 0;
|
||||
};
|
||||
|
||||
Bert::Bert() : d_ptr(new BertPrivate) {
|
||||
d_ptr->modelLoaded = false;
|
||||
}
|
||||
|
||||
Bert::~Bert() {
|
||||
bert_free(d_ptr->ctx);
|
||||
}
|
||||
|
||||
bool Bert::loadModel(const std::string &modelPath, int n_ctx, int ngl)
|
||||
{
|
||||
(void)n_ctx;
|
||||
(void)ngl;
|
||||
d_ptr->modelLoaded = false;
|
||||
|
||||
auto * ctx = bert_load_from_file(modelPath.c_str());
|
||||
fflush(stdout);
|
||||
if (!ctx)
|
||||
return false;
|
||||
|
||||
d_ptr->ctx = ctx;
|
||||
d_ptr->n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
|
||||
d_ptr->modelLoaded = true;
|
||||
return true;
|
||||
}
|
||||
|
||||
bool Bert::isModelLoaded() const
|
||||
{
|
||||
return d_ptr->modelLoaded;
|
||||
}
|
||||
|
||||
size_t Bert::requiredMem(const std::string &modelPath, int n_ctx, int ngl)
|
||||
{
|
||||
(void)modelPath;
|
||||
(void)n_ctx;
|
||||
(void)ngl;
|
||||
return 0;
|
||||
}
|
||||
|
||||
size_t Bert::stateSize() const
|
||||
{
|
||||
return 0;
|
||||
}
|
||||
|
||||
size_t Bert::saveState(uint8_t */*dest*/) const
|
||||
{
|
||||
return 0;
|
||||
}
|
||||
|
||||
size_t Bert::restoreState(const uint8_t */*src*/)
|
||||
{
|
||||
return 0;
|
||||
}
|
||||
|
||||
void Bert::setThreadCount(int32_t n_threads)
|
||||
{
|
||||
d_ptr->n_threads = n_threads;
|
||||
}
|
||||
|
||||
int32_t Bert::threadCount() const
|
||||
{
|
||||
return d_ptr->n_threads;
|
||||
}
|
||||
|
||||
std::vector<float> Bert::embedding(const std::string &text)
|
||||
{
|
||||
const int overlap = 32;
|
||||
const LLModel::Token clsToken = 101;
|
||||
const size_t contextLength = bert_n_max_tokens(d_ptr->ctx);
|
||||
typedef std::vector<LLModel::Token> TokenString;
|
||||
TokenString tokens = ::bert_tokenize(d_ptr->ctx, text.c_str());
|
||||
#if defined(DEBUG_BERT)
|
||||
std::cerr << "embedding: " << tokens.size()
|
||||
<< " contextLength " << contextLength
|
||||
<< "\n";
|
||||
#endif
|
||||
std::vector<double> embeddingsSum(bert_n_embd(d_ptr->ctx), 0);
|
||||
int embeddingsSumTotal = 0;
|
||||
size_t start_pos = 0;
|
||||
bool isFirstChunk = true;
|
||||
while (start_pos < tokens.size()) {
|
||||
TokenString chunk;
|
||||
if (!isFirstChunk)
|
||||
chunk.push_back(clsToken);
|
||||
const size_t l = isFirstChunk ? contextLength : contextLength - 1;
|
||||
if (tokens.size() - start_pos > l) {
|
||||
chunk.insert(chunk.end(), tokens.begin() + start_pos, tokens.begin() + start_pos + l);
|
||||
start_pos = start_pos + contextLength - overlap;
|
||||
} else {
|
||||
chunk.insert(chunk.end(), tokens.begin() + start_pos, tokens.end());
|
||||
start_pos = tokens.size();
|
||||
}
|
||||
#if defined(DEBUG_BERT)
|
||||
std::cerr << "chunk length: " << chunk.size()
|
||||
<< " embeddingsSumTotal " << embeddingsSumTotal
|
||||
<< " contextLength " << contextLength
|
||||
<< " start_pos " << start_pos
|
||||
<< "\n";
|
||||
#endif
|
||||
embeddingsSumTotal++;
|
||||
std::vector<float> embeddings(bert_n_embd(d_ptr->ctx));
|
||||
bert_eval(d_ptr->ctx, d_ptr->n_threads, chunk.data(), chunk.size(), embeddings.data());
|
||||
std::transform(embeddingsSum.begin(), embeddingsSum.end(), embeddings.begin(), embeddingsSum.begin(), std::plus<float>());
|
||||
isFirstChunk = false;
|
||||
}
|
||||
|
||||
std::transform(embeddingsSum.begin(), embeddingsSum.end(), embeddingsSum.begin(), [embeddingsSumTotal](float num){ return num / embeddingsSumTotal; });
|
||||
double magnitude = std::sqrt(std::inner_product(embeddingsSum.begin(), embeddingsSum.end(), embeddingsSum.begin(), 0.0));
|
||||
for (auto &value : embeddingsSum)
|
||||
value /= magnitude;
|
||||
std::vector<float> finalEmbeddings(embeddingsSum.begin(), embeddingsSum.end());
|
||||
return finalEmbeddings;
|
||||
}
|
||||
|
||||
std::vector<LLModel::Token> Bert::tokenize(PromptContext &ctx, const std::string &str, bool special) const
|
||||
{
|
||||
(void)ctx;
|
||||
(void)special;
|
||||
return ::bert_tokenize(d_ptr->ctx, str.c_str());
|
||||
}
|
||||
|
||||
LLModel::Token Bert::sampleToken(PromptContext &/*promptCtx*/) const
|
||||
{
|
||||
return 999 /*!*/;
|
||||
}
|
||||
|
||||
std::string Bert::tokenToString(Token id) const
|
||||
{
|
||||
return bert_vocab_id_to_token(d_ptr->ctx, id);
|
||||
}
|
||||
|
||||
bool Bert::evalTokens(PromptContext &ctx, const std::vector<int32_t> &tokens) const
|
||||
{
|
||||
std::vector<float> embeddings(bert_n_embd(d_ptr->ctx));
|
||||
int32_t cls = 101;
|
||||
const bool useCLS = tokens.front() != cls;
|
||||
if (useCLS) {
|
||||
std::vector<int32_t> myTokens;
|
||||
myTokens.push_back(cls);
|
||||
myTokens.insert(myTokens.end(), tokens.begin(), tokens.end());
|
||||
bert_eval(d_ptr->ctx, d_ptr->n_threads, myTokens.data(), myTokens.size(), embeddings.data());
|
||||
} else
|
||||
bert_eval(d_ptr->ctx, d_ptr->n_threads, tokens.data(), tokens.size(), embeddings.data());
|
||||
ctx.n_past = 0; // bert does not store any context
|
||||
return true;
|
||||
}
|
||||
|
||||
int32_t Bert::contextLength() const
|
||||
{
|
||||
return bert_n_max_tokens(d_ptr->ctx);
|
||||
}
|
||||
|
||||
const std::vector<LLModel::Token> &Bert::endTokens() const
|
||||
{
|
||||
static const std::vector<LLModel::Token> out = { 102 /*sep*/};
|
||||
return out;
|
||||
}
|
||||
|
||||
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) <= 3;
|
||||
isValid = isValid && get_arch_name(ctx_gguf) == "bert";
|
||||
|
||||
gguf_free(ctx_gguf);
|
||||
return isValid;
|
||||
}
|
||||
|
||||
DLL_EXPORT LLModel *construct() {
|
||||
return new Bert;
|
||||
}
|
||||
}
|
||||
@ -1,45 +0,0 @@
|
||||
#ifndef BERT_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE
|
||||
#error This file is NOT meant to be included outside of bert.cpp. Doing so is DANGEROUS. Be sure to know what you are doing before proceeding to #define BERT_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE
|
||||
#endif
|
||||
#ifndef BERT_H
|
||||
#define BERT_H
|
||||
|
||||
#include <string>
|
||||
#include <functional>
|
||||
#include <vector>
|
||||
#include <memory>
|
||||
#include "llmodel.h"
|
||||
|
||||
struct BertPrivate;
|
||||
class Bert : public LLModel {
|
||||
public:
|
||||
Bert();
|
||||
~Bert();
|
||||
|
||||
bool supportsEmbedding() const override { return true; }
|
||||
bool supportsCompletion() const override { return true; }
|
||||
bool loadModel(const std::string &modelPath, int n_ctx, int ngl) override;
|
||||
bool isModelLoaded() const override;
|
||||
size_t requiredMem(const std::string &modelPath, int n_ctx, int ngl) override;
|
||||
size_t stateSize() const override;
|
||||
size_t saveState(uint8_t *dest) const override;
|
||||
size_t restoreState(const uint8_t *src) override;
|
||||
void setThreadCount(int32_t n_threads) override;
|
||||
int32_t threadCount() const override;
|
||||
|
||||
std::vector<float> embedding(const std::string &text) override;
|
||||
|
||||
private:
|
||||
std::unique_ptr<BertPrivate> d_ptr;
|
||||
|
||||
protected:
|
||||
std::vector<Token> tokenize(PromptContext &ctx, const std::string &str, bool special) const override;
|
||||
Token sampleToken(PromptContext &ctx) const override;
|
||||
std::string tokenToString(Token id) const override;
|
||||
bool evalTokens(PromptContext &ctx, const std::vector<int32_t> &tokens) const override;
|
||||
int32_t contextLength() const override;
|
||||
const std::vector<Token> &endTokens() const override;
|
||||
bool shouldAddBOS() const override { return true; }
|
||||
};
|
||||
|
||||
#endif // BERT_H
|
||||
@ -1 +1 @@
|
||||
Subproject commit 2a086f71f5b570a0f047f88d88cf5704aae7ec7c
|
||||
Subproject commit 43c20ce8004a4eac25ffe89e52bdf94bc7c47c02
|
||||
Loading…
Reference in New Issue