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https://github.com/nomic-ai/gpt4all.git
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Add sbert backend.
This commit is contained in:
Adam Treat
AT
parent
1f749d7633
commit
ae8eb297ac
@ -125,6 +125,10 @@ foreach(BUILD_VARIANT IN LISTS BUILD_VARIANTS)
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add_library(mpt-${BUILD_VARIANT} SHARED
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mpt.cpp utils.h utils.cpp llmodel_shared.cpp llmodel_shared.h)
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prepare_target(mpt ggml-230511)
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add_library(bert-${BUILD_VARIANT} SHARED
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bert.cpp utils.h utils.cpp llmodel_shared.cpp llmodel_shared.h)
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prepare_target(bert llama-mainline)
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endif()
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endforeach()
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979
gpt4all-backend/bert.cpp
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979
gpt4all-backend/bert.cpp
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@ -0,0 +1,979 @@
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#include "bert.h"
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#include "ggml.h"
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#include <cassert>
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#include <cmath>
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#include <cstdio>
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#include <cstring>
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#include <fstream>
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#include <map>
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#include <string>
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#include <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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//#define DEBUG_BERT
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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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int32_t f16 = 1;
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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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std::map<std::string, struct ggml_tensor *> tensors;
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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_buffer {
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uint8_t * data = NULL;
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size_t size = 0;
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void resize(size_t size) {
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delete[] data;
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data = new uint8_t[size];
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this->size = size;
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}
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~bert_buffer() {
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delete[] data;
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}
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};
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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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bert_buffer buf_compute;
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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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void bert_tokenize(
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struct bert_ctx * ctx,
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const char * text,
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bert_vocab_id * tokens,
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int32_t * n_tokens,
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int32_t n_max_tokens)
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{
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int cls_tok_id = 101;
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int sep_tok_id = 102;
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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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int32_t t = 0;
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tokens[t++] = cls_tok_id;
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// find the longest tokens that form the words:
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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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loop:
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while (i < n)
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{
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if (t >= n_max_tokens - 1)
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break;
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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[t++] = it->second;
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i = j;
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token_map = &vocab.subword_token_to_id;
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goto loop;
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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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tokens[t++] = sep_tok_id;
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*n_tokens = t;
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}
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//
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// Loading and setup
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//
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struct bert_ctx * bert_load_from_file(const char *fname)
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{
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#if defined(DEBUG_BERT)
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printf("%s: loading model from '%s' - please wait ...\n", __func__, fname);
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#endif
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auto fin = std::ifstream(fname, std::ios::binary);
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if (!fin)
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{
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fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname);
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return nullptr;
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}
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// verify magic
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{
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uint32_t magic;
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fin.read((char *)&magic, sizeof(magic));
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if (magic != 0x67676d6c)
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{
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fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname);
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return nullptr;
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}
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}
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bert_ctx * new_bert = new bert_ctx;
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bert_model & model = new_bert->model;
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bert_vocab & vocab = new_bert->vocab;
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// load hparams
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{
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auto &hparams = model.hparams;
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fin.read((char *)&hparams.n_vocab, sizeof(hparams.n_vocab));
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fin.read((char *)&hparams.n_max_tokens, sizeof(hparams.n_max_tokens));
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fin.read((char *)&hparams.n_embd, sizeof(hparams.n_embd));
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fin.read((char *)&hparams.n_intermediate, sizeof(hparams.n_intermediate));
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fin.read((char *)&hparams.n_head, sizeof(hparams.n_head));
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fin.read((char *)&hparams.n_layer, sizeof(hparams.n_layer));
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fin.read((char *)&hparams.f16, sizeof(hparams.f16));
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#if defined(DEBUG_BERT)
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printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
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printf("%s: n_max_tokens = %d\n", __func__, hparams.n_max_tokens);
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printf("%s: n_embd = %d\n", __func__, hparams.n_embd);
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printf("%s: n_intermediate = %d\n", __func__, hparams.n_intermediate);
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printf("%s: n_head = %d\n", __func__, hparams.n_head);
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printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
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printf("%s: f16 = %d\n", __func__, hparams.f16);
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#endif
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}
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// load vocab
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{
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int32_t n_vocab = model.hparams.n_vocab;
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std::string word;
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for (int i = 0; i < n_vocab; i++)
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{
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uint32_t len;
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fin.read((char *)&len, sizeof(len));
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word.resize(len);
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fin.read((char *)word.data(), len);
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if (word[0] == '#' && word[1] == '#')
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{
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vocab.subword_token_to_id[word.substr(2)] = i;
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vocab._id_to_subword_token[i] = word;
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}
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if (vocab.token_to_id.count(word) == 0)
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{
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vocab.token_to_id[word] = i;
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vocab._id_to_token[i] = word;
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}
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}
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}
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// for the big tensors, we have the option to store the data in 16-bit floats or quantized
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// in order to save memory and also to speed up the computation
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ggml_type wtype = GGML_TYPE_COUNT;
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switch (model.hparams.f16)
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{
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case 0:
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wtype = GGML_TYPE_F32;
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break;
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case 1:
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wtype = GGML_TYPE_F16;
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break;
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case 2:
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wtype = GGML_TYPE_Q4_0;
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break;
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case 3:
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wtype = GGML_TYPE_Q4_1;
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break;
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default:
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{
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fprintf(stderr, "%s: invalid model file '%s' (bad f16 value %d)\n",
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__func__, fname, model.hparams.f16);
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bert_free(new_bert);
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return nullptr;
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}
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}
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auto &ctx = model.ctx;
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size_t model_mem_req = 0;
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{
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const auto &hparams = model.hparams;
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const int n_embd = hparams.n_embd;
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const int n_layer = hparams.n_layer;
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const int n_max_tokens = hparams.n_max_tokens;
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const int n_intermediate = hparams.n_intermediate;
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const int n_vocab = hparams.n_vocab;
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// Calculate size requirements
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model_mem_req += n_embd * n_vocab * ggml_type_sizef(wtype); // word_embeddings
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model_mem_req += n_embd * 2 * ggml_type_sizef(wtype); // token_type_embeddings
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model_mem_req += n_embd * n_max_tokens * ggml_type_sizef(wtype); // position_embeddings
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model_mem_req += 2 * n_embd * ggml_type_sizef(GGML_TYPE_F32); // ln_e_*
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model_mem_req += 4 * n_layer * (n_embd * ggml_type_sizef(GGML_TYPE_F32)); // ln_*
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model_mem_req += 4 * n_layer * (n_embd * n_embd * ggml_type_sizef(wtype)); // kqvo weights
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model_mem_req += 4 * n_layer * (n_embd * ggml_type_sizef(GGML_TYPE_F32)); // kqvo bias
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model_mem_req += 2 * n_layer * (n_embd * n_intermediate * ggml_type_sizef(wtype)); // ff_*_w
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model_mem_req += n_layer * (n_intermediate * ggml_type_sizef(GGML_TYPE_F32)); // ff_i_b
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model_mem_req += n_layer * (n_embd * ggml_type_sizef(GGML_TYPE_F32)); // ff_o_b
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model_mem_req += (5 + 16 * n_layer) * 256; // object overhead
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#if defined(DEBUG_BERT)
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printf("%s: ggml ctx size = %6.2f MB\n", __func__, model_mem_req / (1024.0 * 1024.0));
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#endif
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}
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// create the ggml context
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{
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struct ggml_init_params params = {
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.mem_size = model_mem_req,
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.mem_buffer = NULL,
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.no_alloc = false,
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};
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model.ctx = ggml_init(params);
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if (!model.ctx)
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{
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fprintf(stderr, "%s: ggml_init() failed\n", __func__);
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bert_free(new_bert);
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return nullptr;
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}
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}
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// prepare memory for the weights
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{
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const auto &hparams = model.hparams;
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const int n_embd = hparams.n_embd;
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const int n_layer = hparams.n_layer;
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const int n_intermediate = hparams.n_intermediate;
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const int n_max_tokens = hparams.n_max_tokens;
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const int n_vocab = hparams.n_vocab;
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model.layers.resize(n_layer);
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model.word_embeddings = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
|
||||
model.token_type_embeddings = ggml_new_tensor_2d(ctx, wtype, n_embd, 2);
|
||||
model.position_embeddings = ggml_new_tensor_2d(ctx, wtype, n_embd, n_max_tokens);
|
||||
|
||||
model.ln_e_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
||||
model.ln_e_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
||||
|
||||
// map by name
|
||||
model.tensors["embeddings.word_embeddings.weight"] = model.word_embeddings;
|
||||
model.tensors["embeddings.token_type_embeddings.weight"] = model.token_type_embeddings;
|
||||
model.tensors["embeddings.position_embeddings.weight"] = model.position_embeddings;
|
||||
|
||||
model.tensors["embeddings.LayerNorm.weight"] = model.ln_e_w;
|
||||
model.tensors["embeddings.LayerNorm.bias"] = model.ln_e_b;
|
||||
|
||||
for (int i = 0; i < n_layer; ++i)
|
||||
{
|
||||
auto &layer = model.layers[i];
|
||||
|
||||
layer.ln_att_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
||||
layer.ln_att_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
||||
layer.ln_out_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
||||
layer.ln_out_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
||||
|
||||
layer.q_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
|
||||
layer.q_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
||||
layer.k_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
|
||||
layer.k_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
||||
layer.v_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
|
||||
layer.v_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
||||
layer.o_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
|
||||
layer.o_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
||||
|
||||
layer.ff_i_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_intermediate);
|
||||
layer.ff_i_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_intermediate);
|
||||
|
||||
layer.ff_o_w = ggml_new_tensor_2d(ctx, wtype, n_intermediate, n_embd);
|
||||
layer.ff_o_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
||||
|
||||
// map by name
|
||||
|
||||
model.tensors["encoder.layer." + std::to_string(i) + ".attention.self.query.weight"] = layer.q_w;
|
||||
model.tensors["encoder.layer." + std::to_string(i) + ".attention.self.query.bias"] = layer.q_b;
|
||||
model.tensors["encoder.layer." + std::to_string(i) + ".attention.self.key.weight"] = layer.k_w;
|
||||
model.tensors["encoder.layer." + std::to_string(i) + ".attention.self.key.bias"] = layer.k_b;
|
||||
model.tensors["encoder.layer." + std::to_string(i) + ".attention.self.value.weight"] = layer.v_w;
|
||||
model.tensors["encoder.layer." + std::to_string(i) + ".attention.self.value.bias"] = layer.v_b;
|
||||
model.tensors["encoder.layer." + std::to_string(i) + ".attention.output.LayerNorm.weight"] = layer.ln_att_w;
|
||||
model.tensors["encoder.layer." + std::to_string(i) + ".attention.output.LayerNorm.bias"] = layer.ln_att_b;
|
||||
model.tensors["encoder.layer." + std::to_string(i) + ".attention.output.dense.weight"] = layer.o_w;
|
||||
model.tensors["encoder.layer." + std::to_string(i) + ".attention.output.dense.bias"] = layer.o_b;
|
||||
|
||||
model.tensors["encoder.layer." + std::to_string(i) + ".intermediate.dense.weight"] = layer.ff_i_w;
|
||||
model.tensors["encoder.layer." + std::to_string(i) + ".intermediate.dense.bias"] = layer.ff_i_b;
|
||||
|
||||
model.tensors["encoder.layer." + std::to_string(i) + ".output.LayerNorm.weight"] = layer.ln_out_w;
|
||||
model.tensors["encoder.layer." + std::to_string(i) + ".output.LayerNorm.bias"] = layer.ln_out_b;
|
||||
model.tensors["encoder.layer." + std::to_string(i) + ".output.dense.weight"] = layer.ff_o_w;
|
||||
model.tensors["encoder.layer." + std::to_string(i) + ".output.dense.bias"] = layer.ff_o_b;
|
||||
}
|
||||
}
|
||||
|
||||
// load weights
|
||||
{
|
||||
int n_tensors = 0;
|
||||
size_t total_size = 0;
|
||||
|
||||
#if defined(DEBUG_BERT)
|
||||
printf("%s: ", __func__);
|
||||
#endif
|
||||
|
||||
while (true)
|
||||
{
|
||||
int32_t n_dims;
|
||||
int32_t length;
|
||||
int32_t ftype;
|
||||
|
||||
fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
|
||||
fin.read(reinterpret_cast<char *>(&length), sizeof(length));
|
||||
fin.read(reinterpret_cast<char *>(&ftype), sizeof(ftype));
|
||||
|
||||
if (fin.eof())
|
||||
{
|
||||
break;
|
||||
}
|
||||
|
||||
int64_t nelements = 1;
|
||||
int64_t ne[2] = {1, 1};
|
||||
for (int i = 0; i < n_dims; ++i)
|
||||
{
|
||||
int32_t ne_cur;
|
||||
fin.read(reinterpret_cast<char *>(&ne_cur), sizeof(ne_cur));
|
||||
ne[i] = ne_cur;
|
||||
nelements *= ne[i];
|
||||
}
|
||||
|
||||
std::string name(length, 0);
|
||||
fin.read(&name[0], length);
|
||||
|
||||
if (model.tensors.find(name.data()) == model.tensors.end())
|
||||
{
|
||||
fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
|
||||
bert_free(new_bert);
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
auto tensor = model.tensors[name.data()];
|
||||
if (ggml_nelements(tensor) != nelements)
|
||||
{
|
||||
fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
|
||||
bert_free(new_bert);
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1])
|
||||
{
|
||||
fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%ld, %ld], expected [%ld, %ld]\n",
|
||||
__func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]);
|
||||
bert_free(new_bert);
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
#if defined(DEBUG_BERT)
|
||||
static const char *ftype_str[] = {
|
||||
"f32",
|
||||
"f16",
|
||||
"q4_0",
|
||||
"q4_1",
|
||||
};
|
||||
printf("%24s - [%5ld, %5ld], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1], ftype_str[ftype], ggml_nbytes(tensor) / 1024.0 / 1024.0, ggml_nbytes(tensor));
|
||||
#endif
|
||||
|
||||
size_t bpe = 0;
|
||||
|
||||
switch (ftype)
|
||||
{
|
||||
case 0:
|
||||
bpe = ggml_type_size(GGML_TYPE_F32);
|
||||
break;
|
||||
case 1:
|
||||
bpe = ggml_type_size(GGML_TYPE_F16);
|
||||
break;
|
||||
case 2:
|
||||
bpe = ggml_type_size(GGML_TYPE_Q4_0);
|
||||
assert(ne[0] % 64 == 0);
|
||||
break;
|
||||
case 3:
|
||||
bpe = ggml_type_size(GGML_TYPE_Q4_1);
|
||||
assert(ne[0] % 64 == 0);
|
||||
break;
|
||||
default:
|
||||
{
|
||||
fprintf(stderr, "%s: unknown ftype %d in model file\n", __func__, ftype);
|
||||
bert_free(new_bert);
|
||||
return nullptr;
|
||||
}
|
||||
};
|
||||
|
||||
if ((nelements * bpe) / ggml_blck_size(tensor->type) != ggml_nbytes(tensor))
|
||||
{
|
||||
fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %lu\n",
|
||||
__func__, name.data(), ggml_nbytes(tensor), nelements * bpe);
|
||||
bert_free(new_bert);
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
|
||||
|
||||
// printf("%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
|
||||
total_size += ggml_nbytes(tensor);
|
||||
|
||||
if (++n_tensors % 8 == 0)
|
||||
{
|
||||
#if defined(DEBUG_BERT)
|
||||
printf(".");
|
||||
fflush(stdout);
|
||||
#endif
|
||||
}
|
||||
}
|
||||
|
||||
#if defined(DEBUG_BERT)
|
||||
printf(" done\n");
|
||||
printf("%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size / 1024.0 / 1024.0, n_tensors);
|
||||
#endif
|
||||
}
|
||||
|
||||
fin.close();
|
||||
|
||||
// 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 = 1.1 * (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;
|
||||
}
|
||||
|
||||
void bert_resize_ctx(bert_ctx * ctx, int32_t new_size) {
|
||||
int64_t buf_size_new = ctx->mem_per_input * new_size;
|
||||
|
||||
// TODO: Max memory should be a param? Now just 1 GB
|
||||
int64_t GB = 1 << 30;
|
||||
//printf("%s: requested_buf_size %ldMB\n", __func__, buf_size_new / (1 << 20));
|
||||
if (buf_size_new > GB) {
|
||||
int32_t adjusted_new_size = GB / ctx->mem_per_input;
|
||||
if (adjusted_new_size < 1) adjusted_new_size = 1;
|
||||
//printf("%s: requested batch size %d, actual new batch size %d\n", __func__, new_size, adjusted_new_size);
|
||||
new_size = adjusted_new_size;
|
||||
buf_size_new = ctx->mem_per_input * new_size;
|
||||
}
|
||||
if (new_size > ctx->max_batch_n) {
|
||||
ctx->buf_compute.resize(buf_size_new);
|
||||
ctx->max_batch_n = new_size;
|
||||
}
|
||||
}
|
||||
|
||||
void bert_free(bert_ctx * ctx) {
|
||||
ggml_free(ctx->model.ctx);
|
||||
delete ctx;
|
||||
}
|
||||
|
||||
void bert_eval(
|
||||
struct bert_ctx *ctx,
|
||||
int32_t n_threads,
|
||||
bert_vocab_id *tokens,
|
||||
int32_t n_tokens,
|
||||
float *embeddings)
|
||||
{
|
||||
bert_eval_batch(ctx, n_threads, 1, &tokens, &n_tokens, embeddings ? &embeddings : nullptr);
|
||||
}
|
||||
|
||||
void bert_eval_batch(
|
||||
bert_ctx * ctx,
|
||||
int32_t n_threads,
|
||||
int32_t n_batch_size,
|
||||
bert_vocab_id ** batch_tokens,
|
||||
int32_t * n_tokens,
|
||||
float ** batch_embeddings)
|
||||
{
|
||||
const bert_model& model = ctx->model;
|
||||
bool mem_req_mode = !batch_embeddings;
|
||||
// batch_embeddings is nullptr for the initial memory requirements run
|
||||
if (!mem_req_mode && n_batch_size > ctx->max_batch_n) {
|
||||
bert_resize_ctx(ctx, n_batch_size);
|
||||
if (n_batch_size > ctx->max_batch_n) {
|
||||
fprintf(stderr, "%s: tried to increase buffers to batch size %d but failed\n", __func__, n_batch_size);
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
// TODO: implement real batching
|
||||
for (int ba = 0; ba < n_batch_size; ba++)
|
||||
{
|
||||
const int N = n_tokens[ba];
|
||||
const auto &tokens = batch_tokens[ba];
|
||||
|
||||
const auto &hparams = model.hparams;
|
||||
|
||||
const int n_embd = hparams.n_embd;
|
||||
const int n_layer = hparams.n_layer;
|
||||
const int n_max_tokens = hparams.n_max_tokens;
|
||||
const int n_head = hparams.n_head;
|
||||
|
||||
const int d_head = n_embd / n_head;
|
||||
|
||||
std::vector<float> result;
|
||||
if (N > n_max_tokens)
|
||||
{
|
||||
fprintf(stderr, "Too many tokens, maximum is %d\n", n_max_tokens);
|
||||
return;
|
||||
}
|
||||
|
||||
auto & mem_per_token = ctx->mem_per_token;
|
||||
auto & buf_compute = ctx->buf_compute;
|
||||
|
||||
struct ggml_init_params params = {
|
||||
.mem_size = buf_compute.size,
|
||||
.mem_buffer = buf_compute.data,
|
||||
.no_alloc = false,
|
||||
};
|
||||
|
||||
struct ggml_context *ctx0 = ggml_init(params);
|
||||
struct ggml_cgraph gf = {};
|
||||
gf.n_threads = n_threads;
|
||||
|
||||
// Embeddings. word_embeddings + token_type_embeddings + position_embeddings
|
||||
struct ggml_tensor *token_layer = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
|
||||
memcpy(token_layer->data, tokens, N * ggml_element_size(token_layer));
|
||||
|
||||
struct ggml_tensor *token_types = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
|
||||
ggml_set_zero(token_types);
|
||||
|
||||
struct ggml_tensor *positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
|
||||
for (int i = 0; i < N; i++)
|
||||
{
|
||||
ggml_set_i32_1d(positions, i, i);
|
||||
}
|
||||
|
||||
struct ggml_tensor *inpL = ggml_get_rows(ctx0, model.word_embeddings, token_layer);
|
||||
|
||||
inpL = ggml_add(ctx0,
|
||||
ggml_get_rows(ctx0, model.token_type_embeddings, token_types),
|
||||
inpL);
|
||||
inpL = ggml_add(ctx0,
|
||||
ggml_get_rows(ctx0, model.position_embeddings, positions),
|
||||
inpL);
|
||||
|
||||
// embd norm
|
||||
{
|
||||
inpL = ggml_norm(ctx0, inpL);
|
||||
|
||||
inpL = ggml_add(ctx0,
|
||||
ggml_mul(ctx0,
|
||||
ggml_repeat(ctx0, model.ln_e_w, inpL),
|
||||
inpL),
|
||||
ggml_repeat(ctx0, model.ln_e_b, inpL));
|
||||
}
|
||||
// layers
|
||||
for (int il = 0; il < n_layer; il++)
|
||||
{
|
||||
struct ggml_tensor *cur = inpL;
|
||||
|
||||
// self-attention
|
||||
{
|
||||
struct ggml_tensor *Qcur = cur;
|
||||
Qcur = ggml_reshape_3d(ctx0,
|
||||
ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].q_b, Qcur),
|
||||
ggml_mul_mat(ctx0, model.layers[il].q_w, Qcur)),
|
||||
d_head, n_head, N);
|
||||
struct ggml_tensor *Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
|
||||
|
||||
struct ggml_tensor *Kcur = cur;
|
||||
Kcur = ggml_reshape_3d(ctx0,
|
||||
ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].k_b, Kcur),
|
||||
ggml_mul_mat(ctx0, model.layers[il].k_w, Kcur)),
|
||||
d_head, n_head, N);
|
||||
struct ggml_tensor *K = ggml_permute(ctx0, Kcur, 0, 2, 1, 3);
|
||||
|
||||
struct ggml_tensor *Vcur = cur;
|
||||
Vcur = ggml_reshape_3d(ctx0,
|
||||
ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].v_b, Vcur),
|
||||
ggml_mul_mat(ctx0, model.layers[il].v_w, Vcur)),
|
||||
d_head, n_head, N);
|
||||
struct ggml_tensor *V = ggml_permute(ctx0, Vcur, 0, 2, 1, 3);
|
||||
|
||||
struct ggml_tensor *KQ = ggml_mul_mat(ctx0, K, Q);
|
||||
// KQ = soft_max(KQ / sqrt(head width))
|
||||
KQ = ggml_soft_max(ctx0,
|
||||
ggml_scale(ctx0,
|
||||
KQ,
|
||||
ggml_new_f32(ctx0, 1.0f / sqrt((float)d_head))));
|
||||
|
||||
V = ggml_cont(ctx0, ggml_transpose(ctx0, V));
|
||||
struct ggml_tensor *KQV = ggml_mul_mat(ctx0, V, KQ);
|
||||
KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
|
||||
|
||||
cur = ggml_cpy(ctx0,
|
||||
KQV,
|
||||
ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
|
||||
}
|
||||
// attention output
|
||||
cur = ggml_add(ctx0,
|
||||
ggml_repeat(ctx0, model.layers[il].o_b, cur),
|
||||
ggml_mul_mat(ctx0, model.layers[il].o_w, cur));
|
||||
|
||||
// re-add the layer input
|
||||
cur = ggml_add(ctx0, cur, inpL);
|
||||
|
||||
// attention norm
|
||||
{
|
||||
cur = ggml_norm(ctx0, cur);
|
||||
|
||||
cur = ggml_add(ctx0,
|
||||
ggml_mul(ctx0,
|
||||
ggml_repeat(ctx0, model.layers[il].ln_att_w, cur),
|
||||
cur),
|
||||
ggml_repeat(ctx0, model.layers[il].ln_att_b, cur));
|
||||
}
|
||||
struct ggml_tensor *att_output = cur;
|
||||
// intermediate_output = self.intermediate(attention_output)
|
||||
cur = ggml_mul_mat(ctx0, model.layers[il].ff_i_w, cur);
|
||||
cur = ggml_add(ctx0,
|
||||
ggml_repeat(ctx0, model.layers[il].ff_i_b, cur),
|
||||
cur);
|
||||
cur = ggml_gelu(ctx0, cur);
|
||||
|
||||
// layer_output = self.output(intermediate_output, attention_output)
|
||||
cur = ggml_mul_mat(ctx0, model.layers[il].ff_o_w, cur);
|
||||
cur = ggml_add(ctx0,
|
||||
ggml_repeat(ctx0, model.layers[il].ff_o_b, cur),
|
||||
cur);
|
||||
// attentions bypass the intermediate layer
|
||||
cur = ggml_add(ctx0, att_output, cur);
|
||||
|
||||
// output norm
|
||||
{
|
||||
cur = ggml_norm(ctx0, cur);
|
||||
|
||||
cur = ggml_add(ctx0,
|
||||
ggml_mul(ctx0,
|
||||
ggml_repeat(ctx0, model.layers[il].ln_out_w, cur),
|
||||
cur),
|
||||
ggml_repeat(ctx0, model.layers[il].ln_out_b, cur));
|
||||
}
|
||||
inpL = cur;
|
||||
}
|
||||
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);
|
||||
|
||||
// normalizer
|
||||
ggml_tensor *length = ggml_sqrt(ctx0,
|
||||
ggml_sum(ctx0, ggml_sqr(ctx0, inpL)));
|
||||
inpL = ggml_scale(ctx0, inpL, ggml_div(ctx0, ggml_new_f32(ctx0, 1.0f), length));
|
||||
|
||||
ggml_tensor *output = inpL;
|
||||
// run the computation
|
||||
ggml_build_forward_expand(&gf, output);
|
||||
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(batch_embeddings[ba], (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);
|
||||
}
|
||||
}
|
||||
|
||||
void bert_encode(
|
||||
struct bert_ctx *ctx,
|
||||
int32_t n_threads,
|
||||
const char *texts,
|
||||
float *embeddings)
|
||||
{
|
||||
bert_encode_batch(ctx, n_threads, 1, 1, &texts, &embeddings);
|
||||
}
|
||||
|
||||
void bert_encode_batch(
|
||||
struct bert_ctx *ctx,
|
||||
int32_t n_threads,
|
||||
int32_t n_batch_size,
|
||||
int32_t n_inputs,
|
||||
const char ** texts,
|
||||
float **embeddings)
|
||||
{
|
||||
// TODO: Disable batching for now
|
||||
n_batch_size = 1;
|
||||
/*
|
||||
if (n_batch_size > n_inputs) {
|
||||
n_batch_size = n_inputs;
|
||||
}
|
||||
if (n_batch_size > ctx->max_batch_n) {
|
||||
bert_resize_ctx(ctx, n_batch_size);
|
||||
n_batch_size = ctx->max_batch_n;
|
||||
}
|
||||
*/
|
||||
|
||||
int32_t N = bert_n_max_tokens(ctx);
|
||||
|
||||
std::vector<bert_vocab_id> buf_tokens;
|
||||
// Most of this buffer will be unused in typical case where inputs are not that long.
|
||||
buf_tokens.resize(N * n_inputs);
|
||||
std::vector<int32_t> n_tokens = std::vector<int32_t>(n_inputs);
|
||||
std::vector<bert_vocab_id*> unsorted_tokens(n_inputs);
|
||||
bert_vocab_id* it_tokens = buf_tokens.data();
|
||||
for (int i = 0; i < n_inputs; i++) {
|
||||
unsorted_tokens[i] = it_tokens;
|
||||
bert_tokenize(ctx, texts[i], it_tokens, &n_tokens[i], N);
|
||||
it_tokens += n_tokens[i];
|
||||
}
|
||||
|
||||
if (n_batch_size == n_inputs) {
|
||||
bert_eval_batch(ctx, n_threads, n_batch_size, unsorted_tokens.data(), n_tokens.data(), embeddings);
|
||||
} else {
|
||||
// sort the inputs by tokenized length, batch and eval
|
||||
|
||||
std::vector<int> indices;
|
||||
indices.reserve(n_inputs);
|
||||
for (int i = 0; i < n_inputs; i++)
|
||||
{
|
||||
indices.push_back(i);
|
||||
}
|
||||
|
||||
std::vector<int32_t> sorted_n_tokens = std::vector<int32_t>(n_inputs);
|
||||
|
||||
std::vector<bert_vocab_id *> sorted_tokens(n_inputs);
|
||||
|
||||
std::sort(indices.begin(), indices.end(), [&](int a, int b)
|
||||
{ return n_tokens[a] < n_tokens[b]; });
|
||||
|
||||
std::vector<float *> sorted_embeddings(n_inputs);
|
||||
memcpy(sorted_embeddings.data(), embeddings, n_inputs * sizeof(float *));
|
||||
|
||||
for (int i = 0; i < n_inputs; i++) {
|
||||
sorted_embeddings[i] = embeddings[indices[i]];
|
||||
sorted_tokens[i] = unsorted_tokens[indices[i]];
|
||||
sorted_n_tokens[i] = n_tokens[indices[i]];
|
||||
}
|
||||
|
||||
for (int i = 0; i < n_inputs; i += n_batch_size)
|
||||
{
|
||||
if (i + n_batch_size > n_inputs) {
|
||||
n_batch_size = n_inputs - i;
|
||||
}
|
||||
bert_eval_batch(ctx, n_threads, n_batch_size, &sorted_tokens[i], &sorted_n_tokens[i], &sorted_embeddings[i]);
|
||||
}
|
||||
}
|
||||
}
|
||||
71
gpt4all-backend/bert.h
Normal file
71
gpt4all-backend/bert.h
Normal file
@ -0,0 +1,71 @@
|
||||
#ifndef BERT_H
|
||||
#define BERT_H
|
||||
|
||||
#include <stddef.h>
|
||||
#include <stdint.h>
|
||||
#include <stdbool.h>
|
||||
|
||||
#ifdef __cplusplus
|
||||
extern "C" {
|
||||
#endif
|
||||
|
||||
struct bert_ctx;
|
||||
|
||||
typedef int32_t bert_vocab_id;
|
||||
|
||||
struct bert_ctx * bert_load_from_file(const char * fname);
|
||||
void bert_free(bert_ctx * ctx);
|
||||
|
||||
// Main api, does both tokenizing and evaluation
|
||||
|
||||
void bert_encode(
|
||||
struct bert_ctx * ctx,
|
||||
int32_t n_threads,
|
||||
const char * texts,
|
||||
float * embeddings);
|
||||
|
||||
// n_batch_size - how many to process at a time
|
||||
// n_inputs - total size of texts and embeddings arrays
|
||||
void bert_encode_batch(
|
||||
struct bert_ctx * ctx,
|
||||
int32_t n_threads,
|
||||
int32_t n_batch_size,
|
||||
int32_t n_inputs,
|
||||
const char ** texts,
|
||||
float ** embeddings);
|
||||
|
||||
// Api for separate tokenization & eval
|
||||
|
||||
void bert_tokenize(
|
||||
struct bert_ctx * ctx,
|
||||
const char * text,
|
||||
bert_vocab_id * tokens,
|
||||
int32_t * n_tokens,
|
||||
int32_t n_max_tokens);
|
||||
|
||||
void bert_eval(
|
||||
struct bert_ctx * ctx,
|
||||
int32_t n_threads,
|
||||
bert_vocab_id * tokens,
|
||||
int32_t n_tokens,
|
||||
float * embeddings);
|
||||
|
||||
// NOTE: for batch processing the longest input must be first
|
||||
void bert_eval_batch(
|
||||
struct bert_ctx * ctx,
|
||||
int32_t n_threads,
|
||||
int32_t n_batch_size,
|
||||
bert_vocab_id ** batch_tokens,
|
||||
int32_t * n_tokens,
|
||||
float ** batch_embeddings);
|
||||
|
||||
int32_t bert_n_embd(bert_ctx * ctx);
|
||||
int32_t bert_n_max_tokens(bert_ctx * ctx);
|
||||
|
||||
const char* bert_vocab_id_to_token(bert_ctx * ctx, bert_vocab_id id);
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
#endif
|
||||
|
||||
#endif // BERT_H
|
||||
@ -206,6 +206,8 @@ install(TARGETS replit-mainline-default DESTINATION lib COMPONENT ${COMPONENT_NA
|
||||
if(APPLE)
|
||||
install(TARGETS replit-mainline-metal DESTINATION lib COMPONENT ${COMPONENT_NAME_MAIN})
|
||||
endif()
|
||||
install(TARGETS bert-avxonly DESTINATION lib COMPONENT ${COMPONENT_NAME_MAIN})
|
||||
install(TARGETS bert-default DESTINATION lib COMPONENT ${COMPONENT_NAME_MAIN})
|
||||
|
||||
set(CPACK_GENERATOR "IFW")
|
||||
set(CPACK_VERBATIM_VARIABLES YES)
|
||||
|
||||
@ -7,16 +7,19 @@ file(GLOB MYMPTLIBS ${CPACK_TEMPORARY_INSTALL_DIRECTORY}/packages/${COMPONENT_NA
|
||||
file(GLOB MYLLAMALIBS ${CPACK_TEMPORARY_INSTALL_DIRECTORY}/packages/${COMPONENT_NAME_MAIN}/data/lib/libllama*)
|
||||
file(GLOB MYREPLITLIBS ${CPACK_TEMPORARY_INSTALL_DIRECTORY}/packages/${COMPONENT_NAME_MAIN}/data/lib/libreplit*)
|
||||
file(GLOB MYFALCONLLIBS ${CPACK_TEMPORARY_INSTALL_DIRECTORY}/packages/${COMPONENT_NAME_MAIN}/data/lib/libfalcon*)
|
||||
file(GLOB MYBERTLLIBS ${CPACK_TEMPORARY_INSTALL_DIRECTORY}/packages/${COMPONENT_NAME_MAIN}/data/lib/libbert*)
|
||||
file(GLOB MYLLMODELLIBS ${CPACK_TEMPORARY_INSTALL_DIRECTORY}/packages/${COMPONENT_NAME_MAIN}/data/lib/libllmodel.*)
|
||||
file(COPY ${MYGPTJLIBS}
|
||||
DESTINATION ${CPACK_TEMPORARY_INSTALL_DIRECTORY}/packages/${COMPONENT_NAME_MAIN}/data/bin/gpt4all.app/Contents/Frameworks)
|
||||
file(COPY ${MYMPTLIBS}
|
||||
DESTINATION ${CPACK_TEMPORARY_INSTALL_DIRECTORY}/packages/${COMPONENT_NAME_MAIN}/data/bin/gpt4all.app/Contents/Frameworks)
|
||||
file(COPY ${MYLLAMALIBS}
|
||||
DESTINATION ${CPACK_TEMPORARY_INSTALL_DIRECTORY}/packages/${COMPONENT_NAME_MAIN}/data/bin/gpt4all.app/Contents/Frameworks)
|
||||
file(COPY ${MYREPLITLIBS}
|
||||
DESTINATION ${CPACK_TEMPORARY_INSTALL_DIRECTORY}/packages/${COMPONENT_NAME_MAIN}/data/bin/gpt4all.app/Contents/Frameworks)
|
||||
file(COPY ${MYFALCONLLIBS}
|
||||
DESTINATION ${CPACK_TEMPORARY_INSTALL_DIRECTORY}/packages/${COMPONENT_NAME_MAIN}/data/bin/gpt4all.app/Contents/Frameworks)
|
||||
file(COPY ${MYLLAMALIBS}
|
||||
file(COPY ${MYBERTLLIBS}
|
||||
DESTINATION ${CPACK_TEMPORARY_INSTALL_DIRECTORY}/packages/${COMPONENT_NAME_MAIN}/data/bin/gpt4all.app/Contents/Frameworks)
|
||||
file(COPY ${MYLLMODELLIBS}
|
||||
DESTINATION ${CPACK_TEMPORARY_INSTALL_DIRECTORY}/packages/${COMPONENT_NAME_MAIN}/data/bin/gpt4all.app/Contents/Frameworks)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user