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bark.cpp
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bark.cpp
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/*
Port of Suno's Bark to C/C++.
Author: Pierre-Antoine Bannier <[email protected]>
Note on tokenization
--------------------
Even if bark relies on GPT to generate semantic tokens, the tokenizer is based on
Bert's multilingual cased tokenizer. This uses the WordPiece algorithm to split raw text
into tokens.
This file contains an unofficial (Google has not released an official implementation of
WordPiece) implementation of WordPiece.
Source:
https://github.com/skeskinen/bert.cpp/
*/
#include "bark.h"
#include "ggml.h"
#include "bark-util.h"
#include <cassert>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <fstream>
#include <map>
#include <random>
#include <regex>
#include <string>
bool bark_vocab_load(const std::string& fname, bark_vocab& vocab, int32_t expected_size) {
auto fin = std::ifstream(fname, std::ios::binary);
if (!fin) {
fprintf(stderr, "%s: faield to open '%s'\n", __func__, fname.c_str());
return false;
}
// verify magic
{
uint32_t magic;
fin.read((char *) &magic, sizeof(magic));
if (magic != GGML_FILE_MAGIC) {
fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
return false;
}
}
int32_t n_vocab;
read_safe(fin, n_vocab);
// 5 special tokens: [UNK, SEP, MASK, PAD, CLS]
if (n_vocab != expected_size) {
fprintf(stderr, "%s: wrong voculary size (%d != %d)\n", __func__, n_vocab, expected_size);
return false;
}
std::string word;
std::vector<char> tmp;
tmp.reserve(128);
for (int i = 0; i < n_vocab; i++) {
uint32_t len;
read_safe(fin, len);
if (len > 0) {
tmp.resize(len);
fin.read(&tmp[0], tmp.size()); // read to buffer
word.assign(&tmp[0], tmp.size());
} else {
word = "";
}
vocab.token_to_id[word] = i;
vocab.id_to_token[i] = word;
}
return true;
}
bool gpt_model_load(const std::string& fname, gpt_model& model) {
auto fin = std::ifstream(fname, std::ios::binary);
if (!fin) {
fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
return false;
}
// verify magic
{
uint32_t magic;
fin.read((char *) &magic, sizeof(magic));
if (magic != GGML_FILE_MAGIC) {
fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
return false;
}
}
// load hparams
{
auto & hparams = model.hparams;
read_safe(fin, hparams.n_layer);
read_safe(fin, hparams.n_head);
read_safe(fin, hparams.n_embd);
read_safe(fin, hparams.block_size);
read_safe(fin, hparams.n_in_vocab);
read_safe(fin, hparams.n_out_vocab);
read_safe(fin, hparams.n_lm_heads);
read_safe(fin, hparams.n_wtes);
printf("%s: n_in_vocab = %d\n", __func__, hparams.n_in_vocab);
printf("%s: n_out_vocab = %d\n", __func__, hparams.n_out_vocab);
printf("%s: block_size = %d\n", __func__, hparams.block_size);
printf("%s: n_embd = %d\n", __func__, hparams.n_embd);
printf("%s: n_head = %d\n", __func__, hparams.n_head);
printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
printf("%s: n_lm_heads = %d\n", __func__, hparams.n_lm_heads);
printf("%s: n_wtes = %d\n", __func__, hparams.n_wtes);
}
// 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_ftype_to_ggml_type((ggml_ftype) (model.hparams.ftype));
// if (wtype == GGML_TYPE_COUNT) {
// fprintf(stderr, "%s: invalid model file '%s' (bad ftype value %d)\n",
// __func__, fname.c_str(), model.hparams.ftype);
// return false;
// }
ggml_type wtype = GGML_TYPE_F32;
auto & ctx = model.ctx;
size_t ctx_size = 0;
{
const auto & hparams = model.hparams;
const int n_embd = hparams.n_embd;
const int n_layer = hparams.n_layer;
const int block_size = hparams.block_size;
const int n_in_vocab = hparams.n_in_vocab;
const int n_out_vocab = hparams.n_out_vocab;
const int n_lm_heads = hparams.n_lm_heads;
const int n_wtes = hparams.n_wtes;
ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // ln_f_g
ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // ln_f_b
ctx_size += n_wtes*n_in_vocab*n_embd*ggml_type_sizef(wtype); // wte
ctx_size += block_size*n_embd*ggml_type_sizef(GGML_TYPE_F32); // wpe
ctx_size += n_lm_heads*n_out_vocab*n_embd*ggml_type_sizef(wtype); // lm_head
ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_1_g
ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_1_b
ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_2_g
ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_2_b
ctx_size += n_layer*(3*n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_attn_w
ctx_size += n_layer*( 3*n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_attn_attn_b
ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_proj_w
ctx_size += n_layer*( n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_attn_proj_b
ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_sizef(wtype)); // c_mlp_fc_w
ctx_size += n_layer*( 4*n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_mlp_fc_b
ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_sizef(wtype)); // c_mlp_proj_w
ctx_size += n_layer*( n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_mlp_proj_b
ctx_size += block_size*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_k
ctx_size += block_size*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_v
ctx_size += (6 + 12*n_layer)*512; // object overhead
printf("%s: ggml tensor size = %d bytes\n", __func__, (int) sizeof(ggml_tensor));
printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
}
// create the ggml context
{
struct ggml_init_params params = {
/*.mem_size =*/ ctx_size,
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ false,
};
model.ctx = ggml_init(params);
if (!model.ctx) {
fprintf(stderr, "%s: ggml_init() failed\n", __func__);
return false;
}
}
// 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 block_size = hparams.block_size;
const int n_in_vocab = hparams.n_in_vocab;
const int n_out_vocab = hparams.n_out_vocab;
const int n_lm_heads = hparams.n_lm_heads;
const int n_wtes = hparams.n_wtes;
model.layers.resize(n_layer);
model.lm_heads.resize(n_lm_heads);
model.wtes.resize(n_wtes);
model.ln_f_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
model.ln_f_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
model.wpe = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, block_size);
for (int i = 0; i < n_wtes; i++) {
model.wtes[i] = ggml_new_tensor_2d(ctx, wtype, n_embd, n_in_vocab);
model.tensors["model/wte/" + std::to_string(i)] = model.wtes[i];
}
for (int i = 0; i < n_lm_heads; i++) {
model.lm_heads[i] = ggml_new_tensor_2d(ctx, wtype, n_embd, n_out_vocab);
model.tensors["model/lm_head/" + std::to_string(i)] = model.lm_heads[i];
}
model.tensors["model/ln_f/g"] = model.ln_f_g;
model.tensors["model/ln_f/b"] = model.ln_f_b;
model.tensors["model/wpe"] = model.wpe;
for (int i = 0; i < n_layer; ++i) {
auto & layer = model.layers[i];
layer.ln_1_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
layer.ln_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
layer.ln_2_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
layer.ln_2_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
layer.c_attn_attn_w = ggml_new_tensor_2d(ctx, wtype, n_embd, 3*n_embd);
layer.c_attn_attn_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 3*n_embd);
layer.c_attn_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
layer.c_attn_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
layer.c_mlp_fc_w = ggml_new_tensor_2d(ctx, wtype, n_embd, 4*n_embd);
layer.c_mlp_fc_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*n_embd);
layer.c_mlp_proj_w = ggml_new_tensor_2d(ctx, wtype, 4*n_embd, n_embd);
layer.c_mlp_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
// map by name
model.tensors["model/h" + std::to_string(i) + "/ln_1/g"] = layer.ln_1_g;
model.tensors["model/h" + std::to_string(i) + "/ln_1/b"] = layer.ln_1_b;
model.tensors["model/h" + std::to_string(i) + "/ln_2/g"] = layer.ln_2_g;
model.tensors["model/h" + std::to_string(i) + "/ln_2/b"] = layer.ln_2_b;
model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/w"] = layer.c_attn_attn_w;
model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/b"] = layer.c_attn_attn_b;
model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/w"] = layer.c_attn_proj_w;
model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/b"] = layer.c_attn_proj_b;
model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/w"] = layer.c_mlp_fc_w;
model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/b"] = layer.c_mlp_fc_b;
model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/w"] = layer.c_mlp_proj_w;
model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/b"] = layer.c_mlp_proj_b;
}
}
// key + value memory
{
const auto & hparams = model.hparams;
const int n_embd = hparams.n_embd;
const int n_layer = hparams.n_layer;
const int block_size = hparams.block_size;
const int n_mem = n_layer*block_size;
const int n_elements = n_embd*n_mem;
model.memory_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);
printf("%s: memory size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);
}
// load weights
{
size_t total_size = 0;
while(true) {
int32_t n_dims;
int32_t length;
int32_t ttype;
read_safe(fin, n_dims);
read_safe(fin, length);
read_safe(fin, ttype);
if (fin.eof()) {
break;
}
int32_t nelements = 1;
int32_t ne[2] = { 1, 1 };
for (int i = 0; i < n_dims; ++i) {
read_safe(fin, ne[i]);
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());
return false;
}
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());
return false;
}
if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
__func__, name.data(), (int) tensor->ne[0], (int) tensor->ne[1], ne[0], ne[1]);
return false;
}
const size_t bpe = ggml_type_size(ggml_type(ttype));
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 %zu\n",
__func__, name.data(), ggml_nbytes(tensor), nelements*bpe);
return false;
}
fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
// printf("%48s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], "float", ggml_nbytes(tensor)/1024.0/1024.0);
total_size += ggml_nbytes(tensor);
}
printf("%s: model size = %8.2f MB\n", __func__, total_size/1024.0/1024.0);
model.memsize = total_size;
}
fin.close();
return true;
}
bool bark_model_load(const std::string & dirname, bark_model & model) {
printf("%s: loading model from '%s'\n", __func__, dirname.c_str());
// text
{
printf("%s: reading bark text model\n", __func__);
const std::string fname = dirname + "/ggml_weights_text.bin";
if(!gpt_model_load(fname, model.text_model)) {
fprintf(stderr, "%s: invalid model file '%s' (bad text)\n", __func__, fname.c_str());
return false;
}
model.memsize += model.text_model.memsize;
}
// vocab
{
printf("%s: reading bark vocab\n", __func__);
const std::string fname = dirname + "/ggml_vocab.bin";
const gpt_hparams hparams = model.text_model.hparams;
const int32_t expected_size = hparams.n_in_vocab - hparams.n_out_vocab - 5;
if(!bark_vocab_load(fname, model.vocab, expected_size)) {
fprintf(stderr, "%s: invalid model file '%s' (bad text)\n", __func__, fname.c_str());
return false;
}
}
// coarse
{
printf("\n%s: reading bark coarse model\n", __func__);
const std::string fname = dirname + "/ggml_weights_coarse.bin";
if(!gpt_model_load(fname, model.coarse_model)) {
fprintf(stderr, "%s: invalid model file '%s' (bad coarse)\n", __func__, fname.c_str());
return false;
}
model.memsize += model.coarse_model.memsize;
}
// fine
{
printf("\n%s: reading bark fine model\n", __func__);
const std::string fname = dirname + "/ggml_weights_fine.bin";
if(!gpt_model_load(fname, model.fine_model)) {
fprintf(stderr, "%s: invalid model file '%s' (bad fine)\n", __func__, fname.c_str());
return false;
}
model.memsize += model.fine_model.memsize;
}
// codec
{
printf("\n%s: reading bark codec model\n", __func__);
const std::string fname = dirname + "/ggml_weights_codec.bin";
if(!encodec_model_load(fname, model.codec_model)) {
fprintf(stderr, "%s: invalid model file '%s' (bad codec)\n", __func__, fname.c_str());
return false;
}
model.memsize += model.coarse_model.memsize;
}
printf("\n%s: total model size = %8.2f MB\n", __func__, model.memsize/1024.0/1024.0);
return true;
}
std::string strip_accents(const std::string &in_str) {
std::string out_str;
std::map<std::string, char> accent_map = {{"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 < in_str.length();) {
int len = utf8_len(in_str[i]);
std::string cur = in_str.substr(i, len);
auto iter = accent_map.find(cur);
if (iter != accent_map.end())
out_str += iter->second;
else
out_str += cur;
i += len;
}
return out_str;
}
void bert_tokenize(
const bark_vocab& vocab,
const char * text,
int32_t * tokens,
int32_t * n_tokens,
int32_t n_max_tokens) {
std::string str = text;
std::vector<std::string> words;
// first split the text into words
{
str = strip_accents(text);
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;
// 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;
}
}
}
*n_tokens = t;
}
bool fine_gpt_eval(
const gpt_model & model,
const int n_threads,
const int codebook_ix,
const std::vector<std::vector<bark_vocab::id>> & embd_inp,
std::vector<std::vector<float>> & logits,
size_t & mem_per_token) {
// embd_inp: (n_channels, seq_length)
const int n_past = 0;
const int n_codes = embd_inp.size();
const int N = embd_inp[0].size();
const auto & hparams = model.hparams;
const int n_embd = hparams.n_embd;
const int n_layer = hparams.n_layer;
const int n_ctx = hparams.block_size;
const int n_head = hparams.n_head;
const int n_vocab = hparams.n_out_vocab;
const int n_codes_given = hparams.n_codes_given;
static size_t buf_size = 256u*1024*1024;
static void * buf = malloc(buf_size);
if (mem_per_token > 0 && mem_per_token*N*n_codes > buf_size) {
const size_t buf_size_new = 1.2*(mem_per_token*N*n_codes); // add 20% to account for ggml object overhead
// reallocate
buf_size = buf_size_new;
buf = realloc(buf, buf_size);
if (buf == nullptr) {
fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size);
return false;
}
}
struct ggml_init_params params = {
/*.mem_size =*/ buf_size,
/*.mem_buffer =*/ buf,
/*.no_alloc =*/ false,
};
struct ggml_context * ctx0 = ggml_init(params);
struct ggml_cgraph gf = {};
gf.n_threads = n_threads;
struct ggml_tensor * input = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, N, n_codes);
for (int c = 0; c < n_codes; c++) {
int offset = ggml_element_size(input)*c*N;
memcpy((void *) ((char *) input->data + offset), embd_inp[c].data(), N*ggml_element_size(input));
}
struct ggml_tensor * embd = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd, N, codebook_ix+1);
struct ggml_tensor * curr;
for (int wte_ix = 0; wte_ix < codebook_ix+1; wte_ix++) {
curr = ggml_get_rows(ctx0,
model.wtes[wte_ix],
ggml_view_1d(ctx0, input, N, N*wte_ix*ggml_element_size(input)));
embd = ggml_set_2d(ctx0, embd, curr,
ggml_element_size(curr)*n_embd,
ggml_element_size(curr)*wte_ix*N*n_embd);
}
// [n_embd, N, codebook_ix+1] -> [codebook_ix+1, n_embd, N]
embd = ggml_cont(ctx0, ggml_permute(ctx0, embd, 1, 2, 0, 3));
// // [1, n_embd, N]
embd = ggml_sum_rows(ctx0, embd);
// [N, n_embd]
embd = ggml_cont(ctx0, ggml_permute(ctx0, embd, 2, 0, 1, 3));
struct ggml_tensor * position = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
for (int i = 0; i < N; ++i) {
((int32_t *) position->data)[i] = i;
}
struct ggml_tensor * pos_embd = ggml_get_rows(ctx0, model.wpe, position);
// wte + wpe
struct ggml_tensor * inpL = ggml_add(ctx0, embd, pos_embd);
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * cur;
// norm
{
// [ 768, N]
cur = ggml_norm(ctx0, inpL);
// cur = ln_1_g*cur + ln_1_b
// [ 768, N]
cur = ggml_add(ctx0,
ggml_mul(ctx0,
ggml_repeat(ctx0, model.layers[il].ln_1_g, cur),
cur),
ggml_repeat(ctx0, model.layers[il].ln_1_b, cur));
}
// attn
// [2304, 768] - model.layers[il].c_attn_attn_w
// [2304, 1] - model.layers[il].c_attn_attn_b
// [ 768, N] - cur (in)
// [2304, N] - cur (out)
//
// cur = attn_w*cur + attn_b
// [2304, N]
{
cur = ggml_mul_mat(ctx0,
model.layers[il].c_attn_attn_w,
cur);
cur = ggml_add(ctx0,
ggml_repeat(ctx0, model.layers[il].c_attn_attn_b, cur),
cur);
}
// self-attention
{
struct ggml_tensor * Qcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 0*sizeof(float)*n_embd);
struct ggml_tensor * Kcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 1*sizeof(float)*n_embd);
struct ggml_tensor * Vcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 2*sizeof(float)*n_embd);
// store key and value to memory
if (N >= 1) {
struct ggml_tensor * k = ggml_view_1d(ctx0, model.memory_k, N*n_embd, (ggml_element_size(model.memory_k)*n_embd)*(il*n_ctx + n_past));
struct ggml_tensor * v = ggml_view_1d(ctx0, model.memory_v, N*n_embd, (ggml_element_size(model.memory_v)*n_embd)*(il*n_ctx + n_past));
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
}
// Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3)
// [64, N, 12]
struct ggml_tensor * Q =
ggml_permute(ctx0,
ggml_cpy(ctx0,
Qcur,
ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd/n_head, n_head, N)),
0, 2, 1, 3);
// K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3)
// [64, n_past + N, 12]
struct ggml_tensor * K =
ggml_permute(ctx0,
ggml_reshape_3d(ctx0,
ggml_view_1d(ctx0, model.memory_k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_k)*n_embd),
n_embd/n_head, n_head, n_past + N),
0, 2, 1, 3);
// GG: flash attention
//struct ggml_tensor * V =
// ggml_cpy(ctx0,
// ggml_permute(ctx0,
// ggml_reshape_3d(ctx0,
// ggml_view_1d(ctx0, model.memory_v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_v)*n_embd),
// n_embd/n_head, n_head, n_past + N),
// 1, 2, 0, 3),
// ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_past + N, n_embd/n_head, n_head));
//struct ggml_tensor * KQV = ggml_flash_attn(ctx0, Q, K, V, true);
// K * Q
// [n_past + N, N, 12]
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
// KQ_scaled = KQ / sqrt(n_embd/n_head)
// [n_past + N, N, 12]
struct ggml_tensor * KQ_scaled =
ggml_scale_inplace(ctx0,
KQ,
ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head))
);
// KQ_masked = mask_past(KQ_scaled)
// [n_past + N, N, 12]
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past);
// KQ = soft_max(KQ_masked)
// [n_past + N, N, 12]
struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
// V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous()
// [n_past + N, 64, 12]
struct ggml_tensor * V_trans =
ggml_cpy(ctx0,
ggml_permute(ctx0,
ggml_reshape_3d(ctx0,
ggml_view_1d(ctx0, model.memory_v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_v)*n_embd),
n_embd/n_head, n_head, n_past + N),
1, 2, 0, 3),
ggml_new_tensor_3d(ctx0, model.memory_v->type, n_past + N, n_embd/n_head, n_head));
// KQV = transpose(V) * KQ_soft_max
// [64, N, 12]
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max);
// KQV_merged = KQV.permute(0, 2, 1, 3)
// [64, 12, N]
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
// cur = KQV_merged.contiguous().view(n_embd, N)
// [768, N]
cur = ggml_cpy(ctx0,
KQV_merged,
ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
}
// projection
// [ 768, 768] - model.layers[il].c_attn_proj_w
// [ 768, 1] - model.layers[il].c_attn_proj_b
// [ 768, N] - cur (in)
// [ 768, N] - cur (out)
//
// cur = proj_w*cur + proj_b
// [768, N]
{
cur = ggml_mul_mat(ctx0,
model.layers[il].c_attn_proj_w,
cur);
cur = ggml_add(ctx0,
ggml_repeat(ctx0, model.layers[il].c_attn_proj_b, cur),
cur);
}
// add the input
cur = ggml_add(ctx0, cur, inpL);
struct ggml_tensor * inpFF = cur;
// feed-forward network
{
// norm
{
cur = ggml_norm(ctx0, inpFF);
// cur = ln_2_g*cur + ln_2_b
// [ 768, N]
cur = ggml_add(ctx0,
ggml_mul(ctx0,
ggml_repeat(ctx0, model.layers[il].ln_2_g, cur),
cur),
ggml_repeat(ctx0, model.layers[il].ln_2_b, cur));
}
// fully connected
// [3072, 768] - model.layers[il].c_mlp_fc_w
// [3072, 1] - model.layers[il].c_mlp_fc_b
// [ 768, N] - cur (in)
// [3072, N] - cur (out)
//
// cur = fc_w*cur + fc_b
// [3072, N]
cur = ggml_mul_mat(ctx0,
model.layers[il].c_mlp_fc_w,
cur);
cur = ggml_add(ctx0,
ggml_repeat(ctx0, model.layers[il].c_mlp_fc_b, cur),
cur);
// GELU activation
// [3072, N]
cur = ggml_gelu(ctx0, cur);
// projection
// [ 768, 3072] - model.layers[il].c_mlp_proj_w
// [ 768, 1] - model.layers[il].c_mlp_proj_b
// [3072, N] - cur (in)
// [ 768, N] - cur (out)
//
// cur = proj_w*cur + proj_b
// [768, N]
cur = ggml_mul_mat(ctx0,
model.layers[il].c_mlp_proj_w,
cur);
cur = ggml_add(ctx0,
ggml_repeat(ctx0, model.layers[il].c_mlp_proj_b, cur),
cur);
}
// input for next layer
inpL = ggml_add(ctx0, cur, inpFF);
}
// norm
{
// [ 768, N]
inpL = ggml_norm(ctx0, inpL);
// inpL = ln_f_g*inpL + ln_f_b
// [ 768, N]
inpL = ggml_add(ctx0,
ggml_mul(ctx0,
ggml_repeat(ctx0, model.ln_f_g, inpL),
inpL),
ggml_repeat(ctx0, model.ln_f_b, inpL));
}
// inpL = WTE * inpL
// [ 1024, 1056] - model.lm_head
// [ 1024, N] - inpL
inpL = ggml_mul_mat(ctx0, model.lm_heads[codebook_ix - n_codes_given], inpL);
// run the computation
// ggml_build_forward_expand(&gf, toy);
ggml_build_forward_expand(&gf, inpL);
ggml_graph_compute (ctx0, &gf);
// [1056, 1024]
logits.resize(N);
for (int i = 0; i < N; i++) {
std::vector<float> tmp(n_vocab);
memcpy(tmp.data(), (float *) ggml_get_data(inpL) + (i*n_vocab), sizeof(float)*n_vocab);
logits[i] = tmp;
}
if (mem_per_token == 0) {
mem_per_token = ggml_used_mem(ctx0)/N;
}
//printf("used_mem = %zu\n", ggml_used_mem(ctx0));
ggml_free(ctx0);
return true;
}
bool gpt_eval(
const gpt_model & model,
const int n_threads,
const int n_past,
const bool merge_ctx,
const std::vector<bark_vocab::id> & embd_inp,
std::vector<float> & embd_w,
size_t & mem_per_token) {
int N = embd_inp.size();
const auto & hparams = model.hparams;
const int n_embd = hparams.n_embd;
const int n_layer = hparams.n_layer;
const int n_ctx = hparams.block_size;
const int n_head = hparams.n_head;
const int n_vocab = hparams.n_out_vocab;
static size_t buf_size = 256u*1024*1024;
static void * buf = malloc(buf_size);
if (mem_per_token > 0 && mem_per_token*N > buf_size) {
const size_t buf_size_new = 1.2*(mem_per_token*N); // add 20% to account for ggml object overhead
// reallocate
buf_size = buf_size_new;
buf = realloc(buf, buf_size);
if (buf == nullptr) {
fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size);
return false;
}
}
struct ggml_init_params params = {
/*.mem_size =*/ buf_size,
/*.mem_buffer =*/ buf,
/*.no_alloc =*/ false,
};
struct ggml_context * ctx0 = ggml_init(params);
struct ggml_cgraph gf = {};
gf.n_threads = n_threads;
struct ggml_tensor * input = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
memcpy(input->data, embd_inp.data(), N*ggml_element_size(input));
struct ggml_tensor * embd;
if (!merge_ctx) {
// usually only one token is in the sequence (since the context is saved in
// memory_k and memory_v)
embd = ggml_get_rows(ctx0, model.wtes[0], input);
} else {
// first step (context merging)
struct ggml_tensor * seq_embd = ggml_get_rows(ctx0, model.wtes[0], ggml_view_1d(ctx0, input, 256, 0));
struct ggml_tensor * ctx_embd = ggml_get_rows(ctx0, model.wtes[0], ggml_view_1d(ctx0, input, 256, 256*ggml_element_size(input)));
struct ggml_tensor * rem_embd = ggml_get_rows(ctx0, model.wtes[0], ggml_view_1d(ctx0, input, 1, 512*ggml_element_size(input)));
struct ggml_tensor * merged_embd = ggml_add(ctx0, seq_embd, ctx_embd);
embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, merged_embd->ne[0], merged_embd->ne[1]+rem_embd->ne[1]);
embd = ggml_set_1d(ctx0, embd, merged_embd, 0);
embd = ggml_set_1d(ctx0, embd, rem_embd, merged_embd->ne[0]*merged_embd->ne[1]*ggml_element_size(merged_embd));
N -= 256; // merge context, input size is reduced
}
struct ggml_tensor * position = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
for (int i = 0; i < N; ++i) {
((int32_t *) position->data)[i] = n_past + i;
}
// wte + wpe
struct ggml_tensor * inpL = ggml_add(ctx0,
embd, ggml_get_rows(ctx0, model.wpe, position));
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * cur;
// norm
{
// [ 768, N]
cur = ggml_norm(ctx0, inpL);
// cur = ln_1_g*cur + ln_1_b
// [ 768, N]
cur = ggml_add(ctx0,
ggml_mul(ctx0,
ggml_repeat(ctx0, model.layers[il].ln_1_g, cur),
cur),
ggml_repeat(ctx0, model.layers[il].ln_1_b, cur));
}
// attn
// [2304, 768] - model.layers[il].c_attn_attn_w
// [2304, 1] - model.layers[il].c_attn_attn_b
// [ 768, N] - cur (in)
// [2304, N] - cur (out)
//
// cur = attn_w*cur + attn_b
// [2304, N]
{
cur = ggml_mul_mat(ctx0,
model.layers[il].c_attn_attn_w,
cur);
cur = ggml_add(ctx0,
ggml_repeat(ctx0, model.layers[il].c_attn_attn_b, cur),
cur);
}
// self-attention
{
struct ggml_tensor * Qcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 0*sizeof(float)*n_embd);
struct ggml_tensor * Kcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 1*sizeof(float)*n_embd);
struct ggml_tensor * Vcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 2*sizeof(float)*n_embd);
// store key and value to memory
if (N >= 1) {
struct ggml_tensor * k = ggml_view_1d(ctx0, model.memory_k, N*n_embd, (ggml_element_size(model.memory_k)*n_embd)*(il*n_ctx + n_past));
struct ggml_tensor * v = ggml_view_1d(ctx0, model.memory_v, N*n_embd, (ggml_element_size(model.memory_v)*n_embd)*(il*n_ctx + n_past));
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
}
// Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3)
// [64, N, 12]
struct ggml_tensor * Q =
ggml_permute(ctx0,
ggml_cpy(ctx0,
Qcur,
ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd/n_head, n_head, N)),
0, 2, 1, 3);
// K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3)
// [64, n_past + N, 12]
struct ggml_tensor * K =
ggml_permute(ctx0,
ggml_reshape_3d(ctx0,
ggml_view_1d(ctx0, model.memory_k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_k)*n_embd),
n_embd/n_head, n_head, n_past + N),
0, 2, 1, 3);
// GG: flash attention
//struct ggml_tensor * V =
// ggml_cpy(ctx0,
// ggml_permute(ctx0,
// ggml_reshape_3d(ctx0,