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#include "bert.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>
// 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_buffer {
uint8_t * data = NULL;
size_t size = 0;
void resize(size_t size) {
delete[] data;
data = new uint8_t[size];
this->size = size;
}
~bert_buffer() {
delete[] data;
}
};
struct bert_ctx
{
bert_model model;
bert_vocab vocab;
size_t mem_per_token;
int64_t mem_per_input;
int32_t max_batch_n;
bert_buffer buf_compute;
};
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]";
}
//
// Cli interface
//
void bert_print_usage(char **argv, const bert_params ¶ms)
{
fprintf(stderr, "usage: %s [options]\n", argv[0]);
fprintf(stderr, "\n");
fprintf(stderr, "options:\n");
fprintf(stderr, " -h, --help show this help message and exit\n");
fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1)\n");
fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
fprintf(stderr, " -p PROMPT, --prompt PROMPT\n");
fprintf(stderr, " prompt to start generation with (default: random)\n");
fprintf(stderr, " --port p port to bind in server mode (default: %d)\n", params.port);
fprintf(stderr, " -m FNAME, --model FNAME\n");
fprintf(stderr, " model path (default: %s)\n", params.model);
fprintf(stderr, "\n");
}
bool bert_params_parse(int argc, char **argv, bert_params ¶ms)
{
for (int i = 1; i < argc; i++)
{
std::string arg = argv[i];
if (arg == "-t" || arg == "--threads")
{
params.n_threads = std::stoi(argv[++i]);
}
else if (arg == "-p" || arg == "--prompt")
{
params.prompt = argv[++i];
}
else if (arg == "--port")
{
params.port = std::stoi(argv[++i]);
}
else if (arg == "-m" || arg == "--model")
{
params.model = argv[++i];
}
else if (arg == "-h" || arg == "--help")
{
bert_print_usage(argv, params);
exit(0);
}
else
{
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
bert_print_usage(argv, params);
exit(0);
}
}
return true;
}
//
// 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;
}
void bert_tokenize(
struct bert_ctx * ctx,
const char * text,
bert_vocab_id * tokens,
int32_t * n_tokens,
int32_t n_max_tokens)
{
int cls_tok_id = 101;
int sep_tok_id = 102;
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();
}
}
int32_t t = 0;
tokens[t++] = cls_tok_id;
// find the longest tokens that form the words:
for (const auto &word : words)
{
if (word.size() == 0)
continue;
int i = 0;
int n = word.size();
auto *token_map = &vocab.token_to_id;
loop:
while (i < n)
{
if (t >= n_max_tokens - 1)
break;
int j = n;
while (j > i)
{
auto it = token_map->find(word.substr(i, j - i));
if (it != token_map->end())
{
tokens[t++] = it->second;
i = j;
token_map = &vocab.subword_token_to_id;
goto loop;
}
--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;
}
}
}
tokens[t++] = sep_tok_id;
*n_tokens = t;
}
//
// Loading and setup
//
struct bert_ctx * bert_load_from_file(const char *fname)
{
printf("%s: loading model from '%s' - please wait ...\n", __func__, fname);
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 != 0x67676d6c)
{
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));
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);
}
// 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) * 512; // object overhead
printf("%s: ggml ctx size = %6.2f MB\n", __func__, model_mem_req / (1024.0 * 1024.0));
}
// 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;
size_t total_size = 0;
printf("%s: ", __func__);
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 [%lld, %lld], expected [%lld, %lld]\n",
__func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]);
bert_free(new_bert);
return nullptr;
}
if (0)
{
static const char *ftype_str[] = {
"f32",
"f16",
"q4_0",
"q4_1",
};
printf("%24s - [%5lld, %5lld], 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));
}
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 %llu\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)
{
printf(".");
fflush(stdout);
}
}
printf(" done\n");
printf("%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size / 1024.0 / 1024.0, n_tensors);
}
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 32MB and hope it's enough. Maybe there is a better way to do this?
new_bert->buf_compute.resize(32 * 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
}
printf("%s: mem_per_token %zu KB, mem_per_input %lld MB\n", __func__, new_bert->mem_per_token / (1 << 10), new_bert->mem_per_input / (1 << 20));
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 %lldMB\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 = {};
// 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_with_ctx(ctx0, &gf, n_threads);
// 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);
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