mirror of
https://github.com/nomic-ai/gpt4all
synced 2024-11-06 09:20:33 +00:00
1054 lines
34 KiB
C++
1054 lines
34 KiB
C++
#define BERT_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE
|
|
#include "bert_impl.h"
|
|
#include "llmodel_shared.h"
|
|
#include "ggml.h"
|
|
|
|
#include <cassert>
|
|
#include <cmath>
|
|
#include <cstdio>
|
|
#include <cstring>
|
|
#include <fstream>
|
|
#include <map>
|
|
#include <string>
|
|
#include <vector>
|
|
#include <iostream>
|
|
#include <regex>
|
|
#include <thread>
|
|
#include <algorithm>
|
|
#include <numeric>
|
|
|
|
//#define DEBUG_BERT
|
|
|
|
namespace {
|
|
const char *modelType_ = "Bert";
|
|
}
|
|
|
|
typedef int32_t bert_vocab_id;
|
|
|
|
// default hparams (all-MiniLM-L6-v2)
|
|
struct bert_hparams
|
|
{
|
|
int32_t n_vocab = 30522;
|
|
int32_t n_max_tokens = 512;
|
|
int32_t n_embd = 256;
|
|
int32_t n_intermediate = 1536;
|
|
int32_t n_head = 12;
|
|
int32_t n_layer = 6;
|
|
int32_t f16 = 1;
|
|
};
|
|
|
|
struct bert_layer
|
|
{
|
|
// normalization
|
|
struct ggml_tensor *ln_att_w;
|
|
struct ggml_tensor *ln_att_b;
|
|
|
|
struct ggml_tensor *ln_out_w;
|
|
struct ggml_tensor *ln_out_b;
|
|
|
|
// attention
|
|
struct ggml_tensor *q_w;
|
|
struct ggml_tensor *q_b;
|
|
struct ggml_tensor *k_w;
|
|
struct ggml_tensor *k_b;
|
|
struct ggml_tensor *v_w;
|
|
struct ggml_tensor *v_b;
|
|
|
|
struct ggml_tensor *o_w;
|
|
struct ggml_tensor *o_b;
|
|
|
|
// ff
|
|
struct ggml_tensor *ff_i_w;
|
|
struct ggml_tensor *ff_i_b;
|
|
|
|
struct ggml_tensor *ff_o_w;
|
|
struct ggml_tensor *ff_o_b;
|
|
};
|
|
|
|
struct bert_vocab
|
|
{
|
|
std::map<std::string, bert_vocab_id> token_to_id;
|
|
std::map<std::string, bert_vocab_id> subword_token_to_id;
|
|
|
|
std::map<bert_vocab_id, std::string> _id_to_token;
|
|
std::map<bert_vocab_id, std::string> _id_to_subword_token;
|
|
};
|
|
|
|
struct bert_model
|
|
{
|
|
bert_hparams hparams;
|
|
|
|
// embeddings weights
|
|
struct ggml_tensor *word_embeddings;
|
|
struct ggml_tensor *token_type_embeddings;
|
|
struct ggml_tensor *position_embeddings;
|
|
struct ggml_tensor *ln_e_w;
|
|
struct ggml_tensor *ln_e_b;
|
|
|
|
std::vector<bert_layer> layers;
|
|
|
|
struct ggml_context *ctx;
|
|
std::map<std::string, struct ggml_tensor *> tensors;
|
|
};
|
|
|
|
// Replacement for std::vector<uint8_t> that doesn't require zero-initialization.
|
|
struct bert_ctx
|
|
{
|
|
bert_model model;
|
|
bert_vocab vocab;
|
|
|
|
size_t mem_per_token;
|
|
int64_t mem_per_input;
|
|
int32_t max_batch_n;
|
|
llm_buffer buf_compute;
|
|
llm_buffer work_buf;
|
|
};
|
|
|
|
int32_t bert_n_embd(bert_ctx * ctx)
|
|
{
|
|
return ctx->model.hparams.n_embd;
|
|
}
|
|
|
|
int32_t bert_n_max_tokens(bert_ctx * ctx)
|
|
{
|
|
return ctx->model.hparams.n_max_tokens;
|
|
}
|
|
|
|
const char* bert_vocab_id_to_token(bert_ctx * ctx, bert_vocab_id id) {
|
|
bert_vocab & vocab = ctx->vocab;
|
|
auto it = vocab._id_to_token.find(id);
|
|
if (it != vocab._id_to_token.end())
|
|
{
|
|
return it->second.c_str();
|
|
}
|
|
it = vocab._id_to_subword_token.find(id);
|
|
if (it != vocab._id_to_subword_token.end())
|
|
{
|
|
return it->second.c_str();
|
|
}
|
|
return "[UNK TOKEN from bert_vocab]";
|
|
}
|
|
|
|
//
|
|
// Tokenizing
|
|
//
|
|
|
|
static size_t utf8_len(char src)
|
|
{
|
|
const size_t lookup[] = {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4};
|
|
uint8_t highbits = static_cast<uint8_t>(src) >> 4;
|
|
return lookup[highbits];
|
|
}
|
|
|
|
std::string stripAccents(const std::string &inputString)
|
|
{
|
|
std::string resultString;
|
|
std::map<std::string, char> accentMap = {{"À", 'A'},{"Á", 'A'},
|
|
{"Â", 'A'},{"Ã", 'A'},{"Ä", 'A'},{"Å", 'A'},{"à", 'a'},{"á", 'a'},
|
|
{"â", 'a'},{"ã", 'a'},{"ä", 'a'},{"å", 'a'},{"È", 'E'},{"É", 'E'},
|
|
{"Ê", 'E'},{"Ë", 'E'},{"è", 'e'},{"é", 'e'},{"ê", 'e'},{"ë", 'e'},
|
|
{"Ì", 'I'},{"Í", 'I'},{"Î", 'I'},{"Ï", 'I'},{"ì", 'i'},{"í", 'i'},
|
|
{"î", 'i'},{"ï", 'i'},{"Ò", 'O'},{"Ó", 'O'},{"Ô", 'O'},{"Õ", 'O'},
|
|
{"Ö", 'O'},{"ò", 'o'},{"ó", 'o'},{"ô", 'o'},{"õ", 'o'},{"ö", 'o'},
|
|
{"Ù", 'U'},{"Ú", 'U'},{"Û", 'U'},{"Ü", 'U'},{"ù", 'u'},{"ú", 'u'},
|
|
{"û", 'u'},{"ü", 'u'},{"Ý", 'Y'},{"ý", 'y'},{"Ç", 'C'},{"ç", 'c'},
|
|
{"Ñ", 'N'},{"ñ", 'n'},
|
|
};
|
|
|
|
for (size_t i = 0; i < inputString.length();)
|
|
{
|
|
int len = utf8_len(inputString[i]);
|
|
std::string curChar = inputString.substr(i, len);
|
|
auto iter = accentMap.find(curChar);
|
|
if (iter != accentMap.end())
|
|
{
|
|
resultString += iter->second;
|
|
}
|
|
else
|
|
{
|
|
resultString += curChar;
|
|
}
|
|
i += len;
|
|
}
|
|
|
|
return resultString;
|
|
}
|
|
|
|
std::string bert_normalize_prompt(const std::string &text)
|
|
{
|
|
// TODO: handle chinese characters? https://github.com/huggingface/tokenizers/blob/ef5f50605ddf9f8caef1598c0e4853862b9707a7/tokenizers/src/normalizers/bert.rs#L98
|
|
std::string text2 = stripAccents(text);
|
|
for (size_t i = 0; i < text2.size(); i += utf8_len(text2[i]))
|
|
{
|
|
char c = text2[i];
|
|
if (c >= 'A' && c <= 'Z')
|
|
text2[i] = c - 'A' + 'a';
|
|
}
|
|
return text2;
|
|
}
|
|
|
|
std::vector<bert_vocab_id> bert_tokenize(
|
|
struct bert_ctx * ctx,
|
|
const char * text)
|
|
{
|
|
const bert_vocab &vocab = ctx->vocab;
|
|
|
|
std::string str = text;
|
|
|
|
std::vector<std::string> words;
|
|
// first split the text into words
|
|
{
|
|
str = bert_normalize_prompt(str);
|
|
|
|
std::string pat = R"([[:punct:]]|[[:alpha:]]+|[[:digit:]]+)";
|
|
|
|
std::regex re(pat);
|
|
std::smatch m;
|
|
|
|
while (std::regex_search(str, m, re))
|
|
{
|
|
for (std::string x : m)
|
|
{
|
|
words.push_back(x);
|
|
}
|
|
str = m.suffix();
|
|
}
|
|
}
|
|
|
|
// find the longest tokens that form the words:
|
|
std::vector<bert_vocab_id> tokens;
|
|
int cls_tok_id = 101;
|
|
tokens.push_back(cls_tok_id);
|
|
for (const auto &word : words)
|
|
{
|
|
if (word.size() == 0)
|
|
continue;
|
|
|
|
int i = 0;
|
|
int n = word.size();
|
|
auto *token_map = &vocab.token_to_id;
|
|
while (i < n)
|
|
{
|
|
int j = n;
|
|
while (j > i)
|
|
{
|
|
auto it = token_map->find(word.substr(i, j - i));
|
|
if (it != token_map->end())
|
|
{
|
|
tokens.push_back(it->second);
|
|
i = j;
|
|
token_map = &vocab.subword_token_to_id;
|
|
}
|
|
--j;
|
|
}
|
|
if (j == i)
|
|
{
|
|
fprintf(stderr, "%s: unknown token '%s'\n", __func__, word.substr(i, 1).data());
|
|
token_map = &vocab.subword_token_to_id;
|
|
++i;
|
|
}
|
|
}
|
|
}
|
|
|
|
return tokens;
|
|
}
|
|
|
|
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;
|
|
#if defined(DEBUG_BERT)
|
|
printf("%s: requested_buf_size %lldMB\n", __func__, buf_size_new / (1 << 20));
|
|
#endif
|
|
if (buf_size_new > GB) {
|
|
int32_t adjusted_new_size = GB / ctx->mem_per_input;
|
|
if (adjusted_new_size < 1) adjusted_new_size = 1;
|
|
#if defined(DEBUG_BERT)
|
|
printf("%s: requested batch size %d, actual new batch size %d\n", __func__, new_size, adjusted_new_size);
|
|
#endif
|
|
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_eval(
|
|
struct bert_ctx *ctx,
|
|
int32_t n_threads,
|
|
const bert_vocab_id *raw_tokens,
|
|
int32_t n_tokens,
|
|
float *embeddings)
|
|
{
|
|
const bert_model& model = ctx->model;
|
|
bool mem_req_mode = !embeddings;
|
|
|
|
// batch_embeddings is nullptr for the initial memory requirements run
|
|
if (!mem_req_mode && 1 > ctx->max_batch_n)
|
|
bert_resize_ctx(ctx, 1);
|
|
|
|
const int N = n_tokens;
|
|
const auto &tokens = raw_tokens;
|
|
|
|
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.addr,
|
|
.no_alloc = false,
|
|
};
|
|
|
|
struct ggml_context *ctx0 = ggml_init(params);
|
|
struct ggml_cgraph gf = {};
|
|
|
|
// 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);
|
|
|
|
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) {
|
|
ggml_free(ctx->model.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
|
|
|
|
auto fin = std::ifstream(fname, std::ios::binary);
|
|
if (!fin)
|
|
{
|
|
fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname);
|
|
return nullptr;
|
|
}
|
|
|
|
// verify magic
|
|
{
|
|
uint32_t magic;
|
|
fin.read((char *)&magic, sizeof(magic));
|
|
if (magic != 0x62657274)
|
|
{
|
|
fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname);
|
|
return nullptr;
|
|
}
|
|
}
|
|
|
|
bert_ctx * new_bert = new bert_ctx;
|
|
bert_model & model = new_bert->model;
|
|
bert_vocab & vocab = new_bert->vocab;
|
|
|
|
// load hparams
|
|
{
|
|
auto &hparams = model.hparams;
|
|
|
|
fin.read((char *)&hparams.n_vocab, sizeof(hparams.n_vocab));
|
|
fin.read((char *)&hparams.n_max_tokens, sizeof(hparams.n_max_tokens));
|
|
fin.read((char *)&hparams.n_embd, sizeof(hparams.n_embd));
|
|
fin.read((char *)&hparams.n_intermediate, sizeof(hparams.n_intermediate));
|
|
fin.read((char *)&hparams.n_head, sizeof(hparams.n_head));
|
|
fin.read((char *)&hparams.n_layer, sizeof(hparams.n_layer));
|
|
fin.read((char *)&hparams.f16, sizeof(hparams.f16));
|
|
|
|
#if defined(DEBUG_BERT)
|
|
printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
|
|
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);
|
|
printf("%s: f16 = %d\n", __func__, hparams.f16);
|
|
#endif
|
|
}
|
|
|
|
// load vocab
|
|
{
|
|
int32_t n_vocab = model.hparams.n_vocab;
|
|
|
|
std::string word;
|
|
for (int i = 0; i < n_vocab; i++)
|
|
{
|
|
uint32_t len;
|
|
fin.read((char *)&len, sizeof(len));
|
|
|
|
word.resize(len);
|
|
fin.read((char *)word.data(), len);
|
|
|
|
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;
|
|
}
|
|
}
|
|
}
|
|
|
|
// for the big tensors, we have the option to store the data in 16-bit floats or quantized
|
|
// in order to save memory and also to speed up the computation
|
|
ggml_type wtype = GGML_TYPE_COUNT;
|
|
switch (model.hparams.f16)
|
|
{
|
|
case 0:
|
|
wtype = GGML_TYPE_F32;
|
|
break;
|
|
case 1:
|
|
wtype = GGML_TYPE_F16;
|
|
break;
|
|
case 2:
|
|
wtype = GGML_TYPE_Q4_0;
|
|
break;
|
|
case 3:
|
|
wtype = GGML_TYPE_Q4_1;
|
|
break;
|
|
default:
|
|
{
|
|
fprintf(stderr, "%s: invalid model file '%s' (bad f16 value %d)\n",
|
|
__func__, fname, model.hparams.f16);
|
|
bert_free(new_bert);
|
|
return nullptr;
|
|
}
|
|
}
|
|
|
|
auto &ctx = model.ctx;
|
|
|
|
size_t model_mem_req = 0;
|
|
|
|
{
|
|
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_intermediate = hparams.n_intermediate;
|
|
const int n_vocab = hparams.n_vocab;
|
|
|
|
// Calculate size requirements
|
|
|
|
model_mem_req += n_embd * n_vocab * ggml_type_sizef(wtype); // word_embeddings
|
|
model_mem_req += n_embd * 2 * ggml_type_sizef(wtype); // token_type_embeddings
|
|
model_mem_req += n_embd * n_max_tokens * ggml_type_sizef(wtype); // position_embeddings
|
|
|
|
model_mem_req += 2 * n_embd * ggml_type_sizef(GGML_TYPE_F32); // ln_e_*
|
|
|
|
model_mem_req += 4 * n_layer * (n_embd * ggml_type_sizef(GGML_TYPE_F32)); // ln_*
|
|
|
|
model_mem_req += 4 * n_layer * (n_embd * n_embd * ggml_type_sizef(wtype)); // kqvo weights
|
|
model_mem_req += 4 * n_layer * (n_embd * ggml_type_sizef(GGML_TYPE_F32)); // kqvo bias
|
|
|
|
model_mem_req += 2 * n_layer * (n_embd * n_intermediate * ggml_type_sizef(wtype)); // ff_*_w
|
|
model_mem_req += n_layer * (n_intermediate * ggml_type_sizef(GGML_TYPE_F32)); // ff_i_b
|
|
model_mem_req += n_layer * (n_embd * ggml_type_sizef(GGML_TYPE_F32)); // ff_o_b
|
|
|
|
model_mem_req += (5 + 16 * n_layer) * ggml_tensor_overhead(); // object overhead
|
|
|
|
#if defined(DEBUG_BERT)
|
|
printf("%s: ggml ctx size = %6.2f MB\n", __func__, model_mem_req / (1024.0 * 1024.0));
|
|
#endif
|
|
}
|
|
|
|
// create the ggml context
|
|
{
|
|
struct ggml_init_params params = {
|
|
.mem_size = model_mem_req,
|
|
.mem_buffer = NULL,
|
|
.no_alloc = false,
|
|
};
|
|
|
|
model.ctx = ggml_init(params);
|
|
if (!model.ctx)
|
|
{
|
|
fprintf(stderr, "%s: ggml_init() failed\n", __func__);
|
|
bert_free(new_bert);
|
|
return nullptr;
|
|
}
|
|
}
|
|
|
|
// prepare memory for the weights
|
|
{
|
|
const auto &hparams = model.hparams;
|
|
|
|
const int n_embd = hparams.n_embd;
|
|
const int n_layer = hparams.n_layer;
|
|
const int n_intermediate = hparams.n_intermediate;
|
|
const int n_max_tokens = hparams.n_max_tokens;
|
|
const int n_vocab = hparams.n_vocab;
|
|
|
|
model.layers.resize(n_layer);
|
|
|
|
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;
|
|
#if defined(DEBUG_BERT)
|
|
size_t total_size = 0;
|
|
#endif
|
|
|
|
#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));
|
|
|
|
#if defined(DEBUG_BERT)
|
|
// 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);
|
|
#endif
|
|
|
|
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 = 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)
|
|
{
|
|
d_ptr->ctx = bert_load_from_file(modelPath.c_str());
|
|
d_ptr->n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
|
|
d_ptr->modelLoaded = d_ptr->ctx != nullptr;
|
|
fflush(stdout);
|
|
return true;
|
|
}
|
|
|
|
bool Bert::isModelLoaded() const
|
|
{
|
|
return d_ptr->modelLoaded;
|
|
}
|
|
|
|
size_t Bert::requiredMem(const std::string &/*modelPath*/)
|
|
{
|
|
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 &, const std::string &str) const
|
|
{
|
|
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;
|
|
}
|
|
|
|
#if defined(_WIN32)
|
|
#define DLL_EXPORT __declspec(dllexport)
|
|
#else
|
|
#define DLL_EXPORT __attribute__ ((visibility ("default")))
|
|
#endif
|
|
|
|
extern "C" {
|
|
DLL_EXPORT bool is_g4a_backend_model_implementation() {
|
|
return true;
|
|
}
|
|
|
|
DLL_EXPORT const char *get_model_type() {
|
|
return modelType_;
|
|
}
|
|
|
|
DLL_EXPORT const char *get_build_variant() {
|
|
return GGML_BUILD_VARIANT;
|
|
}
|
|
|
|
DLL_EXPORT bool magic_match(std::istream& f) {
|
|
uint32_t magic = 0;
|
|
f.read(reinterpret_cast<char*>(&magic), sizeof(magic));
|
|
if (magic != 0x62657274) {
|
|
return false;
|
|
}
|
|
return true;
|
|
}
|
|
|
|
DLL_EXPORT LLModel *construct() {
|
|
return new Bert;
|
|
}
|
|
}
|