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whisper.cpp/examples/talk.wasm/emscripten.cpp

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talk.wasm : GPT-2 meets Whisper in WebAssembly (#155) * talk : initial real-time transcription in the browser * talk : polishing the UI * talk : ready for beta testing * talk.wasm : rename example
2 years ago
#include "ggml.h"
#include "whisper.h"
#include <emscripten.h>
#include <emscripten/bind.h>
#include <atomic>
#include <cassert>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <fstream>
#include <map>
#include <mutex>
#include <string>
#include <thread>
#include <vector>
#include <regex>
#include <random>
std::string to_timestamp(int64_t t) {
int64_t sec = t/100;
int64_t msec = t - sec*100;
int64_t min = sec/60;
sec = sec - min*60;
char buf[32];
snprintf(buf, sizeof(buf), "%02d:%02d.%03d", (int) min, (int) sec, (int) msec);
return std::string(buf);
}
/////////////////////// GPT-2 BEGIN /////////////////////////
// TODO: move to a separate file
//
// Vocab utils
//
struct gpt_vocab {
using id = int32_t;
using token = std::string;
std::map<token, id> token_to_id;
std::map<id, token> id_to_token;
};
void replace(std::string & str, const std::string & needle, const std::string & replacement) {
size_t pos = 0;
while ((pos = str.find(needle, pos)) != std::string::npos) {
str.replace(pos, needle.length(), replacement);
pos += replacement.length();
}
}
std::map<std::string, int32_t> json_parse(const std::string & fname) {
std::map<std::string, int32_t> result;
// read file into string
std::string json;
{
std::ifstream ifs(fname);
if (!ifs) {
fprintf(stderr, "Failed to open %s\n", fname.c_str());
exit(1);
}
json = std::string((std::istreambuf_iterator<char>(ifs)),
(std::istreambuf_iterator<char>()));
}
if (json[0] != '{') {
return result;
}
// parse json
{
bool has_key = false;
bool in_token = false;
std::string str_key = "";
std::string str_val = "";
int n = json.size();
for (int i = 1; i < n; ++i) {
if (!in_token) {
if (json[i] == ' ') continue;
if (json[i] == '"') {
in_token = true;
continue;
}
} else {
if (json[i] == '\\' && i+1 < n) {
if (has_key == false) {
str_key += json[i];
} else {
str_val += json[i];
}
++i;
} else if (json[i] == '"') {
if (has_key == false) {
has_key = true;
++i;
while (json[i] == ' ') ++i;
++i; // :
while (json[i] == ' ') ++i;
if (json[i] != '\"') {
while (json[i] != ',' && json[i] != '}') {
str_val += json[i++];
}
has_key = false;
} else {
in_token = true;
continue;
}
} else {
has_key = false;
}
::replace(str_key, "\\u0120", " " ); // \u0120 -> space
::replace(str_key, "\\u010a", "\n"); // \u010a -> new line
::replace(str_key, "\\\"", "\""); // \\\" -> "
try {
result[str_key] = std::stoi(str_val);
} catch (...) {
//fprintf(stderr, "%s: ignoring key '%s' with value '%s'\n", fname.c_str(), str_key.c_str(), str_val.c_str());
}
str_key = "";
str_val = "";
in_token = false;
continue;
}
if (has_key == false) {
str_key += json[i];
} else {
str_val += json[i];
}
}
}
}
return result;
}
std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::string & text) {
std::vector<std::string> words;
// first split the text into words
{
std::string str = text;
std::string pat = R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)";
std::regex re(pat);
std::smatch m;
while (std::regex_search(str, m, re)) {
for (auto x : m) {
words.push_back(x);
}
str = m.suffix();
}
}
// find the longest tokens that form the words:
std::vector<gpt_vocab::id> tokens;
for (const auto & word : words) {
if (word.size() == 0) continue;
int i = 0;
int n = word.size();
while (i < n) {
int j = n;
while (j > i) {
auto it = vocab.token_to_id.find(word.substr(i, j-i));
if (it != vocab.token_to_id.end()) {
tokens.push_back(it->second);
i = j;
break;
}
--j;
}
if (i == n) {
break;
}
if (j == i) {
auto sub = word.substr(i, 1);
if (vocab.token_to_id.find(sub) != vocab.token_to_id.end()) {
tokens.push_back(vocab.token_to_id.at(sub));
} else {
fprintf(stderr, "%s: unknown token '%s'\n", __func__, sub.data());
}
++i;
}
}
}
return tokens;
}
bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab) {
printf("%s: loading vocab from '%s'\n", __func__, fname.c_str());
vocab.token_to_id = ::json_parse(fname);
for (const auto & kv : vocab.token_to_id) {
vocab.id_to_token[kv.second] = kv.first;
}
printf("%s: vocab size = %d\n", __func__, (int) vocab.token_to_id.size());
// print the vocabulary
//for (auto kv : vocab.token_to_id) {
// printf("'%s' -> %d\n", kv.first.data(), kv.second);
//}
return true;
}
gpt_vocab::id gpt_sample_top_k_top_p(
const gpt_vocab & vocab,
const float * logits,
int top_k,
double top_p,
double temp,
std::mt19937 & rng) {
int n_logits = vocab.id_to_token.size();
std::vector<std::pair<double, gpt_vocab::id>> logits_id;
logits_id.reserve(n_logits);
for (int i = 0; i < n_logits; i++) {
logits_id.push_back(std::make_pair(logits[i], i));
}
// find the top K tokens
std::partial_sort(
logits_id.begin(),
logits_id.begin() + top_k, logits_id.end(),
[](const std::pair<double, gpt_vocab::id> & a, const std::pair<double, gpt_vocab::id> & b) {
return a.first > b.first;
});
logits_id.resize(top_k);
// normalize
{
double sum = 0.0f;
for (int i = 0; i < (int)logits_id.size(); i++) {
sum += logits_id[i].first;
}
sum = 1.0/sum;
for (int i = 0; i < (int)logits_id.size(); i++) {
logits_id[i].first *= sum;
}
}
if (top_p < 1.0f) {
{
double cumsum = 0.0f;
for (int i = 0; i < top_k; i++) {
cumsum += logits_id[i].first;
if (cumsum >= top_p) {
logits_id.resize(i+1);
break;
}
}
}
// normalize again
{
double sum = 0.0f;
for (int i = 0; i < (int)logits_id.size(); i++) {
sum += logits_id[i].first;
}
sum = 1.0/sum;
for (int i = 0; i < (int)logits_id.size(); i++) {
logits_id[i].first *= sum;
}
}
}
//printf("\n");
//for (int i = 0; i < (int)logits_id.size(); i++) {
// printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), logits_id[i].first);
//}
//exit(0);
// sample from the obtained distribution
std::vector<double> probs;
probs.reserve(logits_id.size());
for (int i = 0; i < (int) logits_id.size(); i++) {
probs.push_back(logits_id[i].first);
}
std::discrete_distribution<> dist(probs.begin(), probs.end());
int idx = dist(rng);
return logits_id[idx].second;
}
// default hparams (GPT-2 117M)
struct gpt2_hparams {
int32_t n_vocab = 50257;
int32_t n_ctx = 1024;
int32_t n_embd = 768;
int32_t n_head = 12;
int32_t n_layer = 12;
int32_t f16 = 1;
};
struct gpt2_layer {
// normalization
struct ggml_tensor * ln_1_g;
struct ggml_tensor * ln_1_b;
struct ggml_tensor * ln_2_g;
struct ggml_tensor * ln_2_b;
// attention
struct ggml_tensor * c_attn_attn_w;
struct ggml_tensor * c_attn_attn_b;
struct ggml_tensor * c_attn_proj_w;
struct ggml_tensor * c_attn_proj_b;
// mlp
struct ggml_tensor * c_mlp_fc_w;
struct ggml_tensor * c_mlp_fc_b;
struct ggml_tensor * c_mlp_proj_w_trans; // transposed for efficiency
struct ggml_tensor * c_mlp_proj_b;
};
struct gpt2_model {
gpt2_hparams hparams;
// normalization
struct ggml_tensor * ln_f_g;
struct ggml_tensor * ln_f_b;
struct ggml_tensor * wte; // position embedding
struct ggml_tensor * wpe; // token embedding
std::vector<gpt2_layer> layers;
// key + value memory
struct ggml_tensor * memory_k;
struct ggml_tensor * memory_v;
//
struct ggml_context * ctx;
std::map<std::string, struct ggml_tensor *> tensors;
};
// load the model's weights from a file
bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab & vocab) {
printf("%s: loading model from '%s'\n", __func__, fname.c_str());
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 != 0x67676d6c) {
fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
return false;
}
}
// load hparams
{
auto & hparams = model.hparams;
fin.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
fin.read((char *) &hparams.n_ctx, sizeof(hparams.n_ctx));
fin.read((char *) &hparams.n_embd, sizeof(hparams.n_embd));
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_ctx = %d\n", __func__, hparams.n_ctx);
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: f16 = %d\n", __func__, hparams.f16);
}
// load vocab
{
int32_t n_vocab = 0;
fin.read((char *) &n_vocab, sizeof(n_vocab));
if (n_vocab != model.hparams.n_vocab) {
fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",
__func__, fname.c_str(), n_vocab, model.hparams.n_vocab);
return false;
}
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);
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
// in order to save memory and also to speed up the computation
const ggml_type wtype = model.hparams.f16 ? GGML_TYPE_F16 : 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 n_ctx = hparams.n_ctx;
const int n_vocab = hparams.n_vocab;
ctx_size += n_embd*ggml_type_size(GGML_TYPE_F32); // ln_f_g
ctx_size += n_embd*ggml_type_size(GGML_TYPE_F32); // ln_f_b
ctx_size += n_vocab*n_embd*ggml_type_size(wtype); // wte
ctx_size += n_ctx*n_embd*ggml_type_size(GGML_TYPE_F32); // wpe
ctx_size += n_layer*(n_embd*ggml_type_size(GGML_TYPE_F32)); // ln_1_g
ctx_size += n_layer*(n_embd*ggml_type_size(GGML_TYPE_F32)); // ln_1_b
ctx_size += n_layer*(n_embd*ggml_type_size(GGML_TYPE_F32)); // ln_2_g
ctx_size += n_layer*(n_embd*ggml_type_size(GGML_TYPE_F32)); // ln_2_b
ctx_size += n_layer*(3*n_embd*n_embd*ggml_type_size(wtype)); // c_attn_attn_w
ctx_size += n_layer*( 3*n_embd*ggml_type_size(GGML_TYPE_F32)); // c_attn_attn_b
ctx_size += n_layer*(n_embd*n_embd*ggml_type_size(wtype)); // c_attn_proj_w
ctx_size += n_layer*( n_embd*ggml_type_size(GGML_TYPE_F32)); // c_attn_proj_b
ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_size(wtype)); // c_mlp_fc_w
ctx_size += n_layer*( 4*n_embd*ggml_type_size(GGML_TYPE_F32)); // c_mlp_fc_b
ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_size(wtype)); // c_mlp_proj_w
ctx_size += n_layer*( n_embd*ggml_type_size(GGML_TYPE_F32)); // c_mlp_proj_b
ctx_size += n_ctx*n_layer*n_embd*ggml_type_size(GGML_TYPE_F32); // memory_k
ctx_size += n_ctx*n_layer*n_embd*ggml_type_size(GGML_TYPE_F32); // memory_v
ctx_size += (6 + 12*n_layer)*256; // object overhead
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,
};
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 n_ctx = hparams.n_ctx;
const int n_vocab = hparams.n_vocab;
model.layers.resize(n_layer);
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.wte = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
model.wpe = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ctx);
// map by name
model.tensors["model/ln_f/g"] = model.ln_f_g;
model.tensors["model/ln_f/b"] = model.ln_f_b;
model.tensors["model/wte"] = model.wte;
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, 3*n_embd, 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, 4*n_embd, n_embd);
layer.c_mlp_fc_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*n_embd);
layer.c_mlp_proj_w_trans = 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_trans;
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 n_ctx = hparams.n_ctx;
const int n_mem = n_layer*n_ctx;
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 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;
}
int32_t nelements = 1;
int32_t ne[2] = { 1, 1 };
for (int i = 0; i < n_dims; ++i) {
fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(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(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]);
return false;
}
const size_t bpe = (ftype == 0) ? sizeof(float) : sizeof(ggml_fp16_t);
if (nelements*bpe != 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("%24s - [%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);
}
printf("%s: model size = %8.2f MB\n", __func__, total_size/1024.0/1024.0);
}
fin.close();
return true;
}
// evaluate the transformer
//
// - model: the model
// - n_threads: number of threads to use
// - n_past: the context size so far
// - embd_inp: the embeddings of the tokens in the context
// - embd_w: the predicted probabilities of the next token
//
bool gpt2_eval(
const gpt2_model & model,
const int n_threads,
const int n_past,
const std::vector<gpt_vocab::id> & embd_inp,
std::vector<float> & embd_w,
size_t & mem_per_token) {
const 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.n_ctx;
const int n_head = hparams.n_head;
const int n_vocab = hparams.n_vocab;
static size_t buf_size = 512u*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 = 1.1*(mem_per_token*N); // add 10% to account for ggml object overhead
printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
// 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,
};
struct ggml_context * ctx0 = ggml_init(params);
struct ggml_cgraph gf = { .n_threads = n_threads };
struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd));
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,
ggml_get_rows(ctx0, model.wte, 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,
ggml_transpose(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(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(ctx0, KQ_scaled, n_past);
// KQ = soft_max(KQ_masked)
// [n_past + N, N, 12]
struct ggml_tensor * KQ_soft_max = ggml_soft_max(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_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);
// 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,
ggml_transpose(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,
ggml_transpose(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_trans,
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
// [ 768, 50257] - model.wte
// [ 768, N] - inpL
inpL = ggml_mul_mat(ctx0, model.wte, inpL);
// logits -> probs
inpL = ggml_soft_max(ctx0, inpL);
// run the computation
ggml_build_forward_expand(&gf, inpL);
ggml_graph_compute (ctx0, &gf);
//if (n_past%100 == 0) {
// ggml_graph_print (&gf);
// ggml_graph_dump_dot(&gf, NULL, "gpt-2.dot");
//}
//embd_w.resize(n_vocab*N);
//memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N);
// return result for just the last token
embd_w.resize(n_vocab);
memcpy(embd_w.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
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;
}
/////////////////////////////// GPT-2 END ////////////////////////////////
constexpr int N_THREAD = 8;
struct gpt2_state {
std::string prompt_base = R"(Hello, how are you?
I'm fine, thanks. How are you?
Thanks, I'm fine too. What are you doing?
I'm just sitting here.
It's a lovely day, isn't it?
Yes, it is.
Did you know that I'm a robot?
I wasn't aware of that.
)";
std::mt19937 rng;
gpt_vocab vocab;
gpt2_model model;
int32_t n_threads = std::min(N_THREAD, (int) std::thread::hardware_concurrency());
int32_t n_predict = 32; // new tokens to predict
// sampling parameters
int32_t top_k = 40;
float top_p = 0.9f;
float temp = 1.0f;
};
struct gpt2_state g_gpt2;
std::vector<float> g_pcmf32;
std::vector<struct whisper_context *> g_contexts(4, nullptr);
std::mutex g_mutex;
std::thread g_worker;
std::atomic<bool> g_running(false);
bool g_force_speak = false;
std::string g_text_to_speak = "";
std::string g_status = "idle";
std::string g_status_forced = "";
std::string gpt2_gen_text(const std::string & prompt) {
int n_past = 0;
std::vector<float> embd_w;
// tokenize the prompt
std::vector<gpt_vocab::id> embd_inp = ::gpt_tokenize(g_gpt2.vocab, g_gpt2.prompt_base + prompt);
g_gpt2.n_predict = std::min(g_gpt2.n_predict, g_gpt2.model.hparams.n_ctx - (int) embd_inp.size());
std::vector<gpt_vocab::id> embd = embd_inp;
size_t mem_per_token = 3000000;
std::string result;
for (int i = embd.size(); i < embd_inp.size() + g_gpt2.n_predict; i++) {
// predict
if (embd.size() > 0) {
if (!gpt2_eval(g_gpt2.model, g_gpt2.n_threads, n_past, embd, embd_w, mem_per_token)) {
printf("gpt-2: failed to generate text\n");
return "";
}
}
n_past += embd.size();
embd.clear();
{
// sample next token
const int top_k = g_gpt2.top_k;
const float top_p = g_gpt2.top_p;
const float temp = g_gpt2.temp;
const int n_vocab = g_gpt2.model.hparams.n_vocab;
const gpt_vocab::id id = gpt_sample_top_k_top_p(g_gpt2.vocab, embd_w.data() + (embd_w.size() - n_vocab), top_k, top_p, temp, g_gpt2.rng);
// add it to the context
embd.push_back(id);
}
result += g_gpt2.vocab.id_to_token[embd[0]];
// end of text token
if (embd.back() == 50256 ||
g_gpt2.vocab.id_to_token[embd.back()] == "." ||
g_gpt2.vocab.id_to_token[embd.back()] == "!" ||
g_gpt2.vocab.id_to_token[embd.back()] == "?") {
break;
}
}
return result;
}
void talk_set_status(const std::string & status) {
std::lock_guard<std::mutex> lock(g_mutex);
g_status = status;
}
void talk_main(size_t index) {
talk_set_status("loading data ...");
struct whisper_full_params wparams = whisper_full_default_params(whisper_sampling_strategy::WHISPER_SAMPLING_GREEDY);
wparams.n_threads = std::min(N_THREAD, (int) std::thread::hardware_concurrency());
wparams.offset_ms = 0;
wparams.translate = false;
wparams.no_context = true;
wparams.single_segment = true;
wparams.print_realtime = false;
wparams.print_progress = false;
wparams.print_timestamps = true;
wparams.print_special_tokens = false;
wparams.max_tokens = 32;
wparams.audio_ctx = 768;
wparams.language = "en";
g_gpt2.rng = std::mt19937(time(NULL));
// load the model
{
const int64_t t_start_us = ggml_time_us();
if (!gpt2_model_load("gpt-2.bin", g_gpt2.model, g_gpt2.vocab)) {
fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, "gpt-2.bin");
return;
}
const int64_t t_load_us = ggml_time_us() - t_start_us;
printf("gpt-2: model loaded in %d ms\n", (int) (t_load_us/1000));
}
std::vector<float> pcmf32;
auto & ctx = g_contexts[index];
const int64_t step_samples = 2*WHISPER_SAMPLE_RATE;
const int64_t step_ms = (step_samples*1000)/WHISPER_SAMPLE_RATE;
const int64_t window_samples = 9*WHISPER_SAMPLE_RATE;
auto t_last = std::chrono::high_resolution_clock::now();
talk_set_status("listening ...");
while (g_running) {
const auto t_now = std::chrono::high_resolution_clock::now();
if (std::chrono::duration_cast<std::chrono::milliseconds>(t_now - t_last).count() < step_ms) {
{
std::lock_guard<std::mutex> lock(g_mutex);
g_pcmf32.clear();
}
std::this_thread::sleep_for(std::chrono::milliseconds(10));
continue;
}
talk_set_status("listening ...");
{
std::unique_lock<std::mutex> lock(g_mutex);
if (g_pcmf32.size() < step_samples) {
lock.unlock();
std::this_thread::sleep_for(std::chrono::milliseconds(10));
continue;
}
pcmf32 = std::vector<float>(g_pcmf32.end() - std::min((int64_t) g_pcmf32.size(), window_samples), g_pcmf32.end());
}
// if energy in during last second is above threshold, then skip
{
float energy_all = 0.0f;
float energy_1s = 0.0f;
for (size_t i = 0; i < pcmf32.size(); i++) {
energy_all += fabsf(pcmf32[i]);
if (i >= pcmf32.size() - WHISPER_SAMPLE_RATE) {
energy_1s += fabsf(pcmf32[i]);
}
}
energy_all /= pcmf32.size();
energy_1s /= WHISPER_SAMPLE_RATE;
if (energy_1s > 0.1f*energy_all && !g_force_speak) {
std::this_thread::sleep_for(std::chrono::milliseconds(10));
continue;
}
}
talk_set_status("processing ...");
g_force_speak = false;
t_last = t_now;
{
const auto t_start = std::chrono::high_resolution_clock::now();
int ret = whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size());
if (ret != 0) {
printf("whisper_full() failed: %d\n", ret);
break;
}
const auto t_end = std::chrono::high_resolution_clock::now();
printf("whisper_full() returned %d in %f seconds\n", ret, std::chrono::duration<double>(t_end - t_start).count());
}
{
std::string text_heard;
const int n_segments = whisper_full_n_segments(ctx);
for (int i = n_segments - 1; i < n_segments; ++i) {
const char * text = whisper_full_get_segment_text(ctx, i);
const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
printf ("[%s --> %s] %s\n", to_timestamp(t0).c_str(), to_timestamp(t1).c_str(), text);
text_heard += text;
}
// remove text between brackets using regex
{
std::regex re("\\[.*?\\]");
text_heard = std::regex_replace(text_heard, re, "");
}
// remove text between brackets using regex
{
std::regex re("\\(.*?\\)");
text_heard = std::regex_replace(text_heard, re, "");
}
// remove all characters, except for letters, numbers, punctuation and ':', '\'', '-', ' '
text_heard = std::regex_replace(text_heard, std::regex("[^a-zA-Z0-9\\.,\\?!\\s\\:\\'\\-]"), "");
// take first line
text_heard = text_heard.substr(0, text_heard.find_first_of("\n"));
// remove leading and trailing whitespace
text_heard = std::regex_replace(text_heard, std::regex("^\\s+"), "");
text_heard = std::regex_replace(text_heard, std::regex("\\s+$"), "");
talk_set_status("'" + text_heard + "' - thinking how to respond ...");
const std::vector<gpt_vocab::id> tokens = ::gpt_tokenize(g_gpt2.vocab, text_heard);
printf("whisper: number of tokens: %d, '%s'\n", (int) tokens.size(), text_heard.c_str());
std::string text_to_speak;
if (tokens.size() > 0) {
text_to_speak = gpt2_gen_text(text_heard + "\n");
text_to_speak = std::regex_replace(text_to_speak, std::regex("[^a-zA-Z0-9\\.,\\?!\\s\\:\\'\\-]"), "");
text_to_speak = text_to_speak.substr(0, text_to_speak.find_first_of("\n"));
std::lock_guard<std::mutex> lock(g_mutex);
// remove first 2 lines of base prompt
{
const size_t pos = g_gpt2.prompt_base.find_first_of("\n");
if (pos != std::string::npos) {
g_gpt2.prompt_base = g_gpt2.prompt_base.substr(pos + 1);
}
}
{
const size_t pos = g_gpt2.prompt_base.find_first_of("\n");
if (pos != std::string::npos) {
g_gpt2.prompt_base = g_gpt2.prompt_base.substr(pos + 1);
}
}
g_gpt2.prompt_base += text_heard + "\n" + text_to_speak + "\n";
} else {
text_to_speak = gpt2_gen_text("");
text_to_speak = std::regex_replace(text_to_speak, std::regex("[^a-zA-Z0-9\\.,\\?!\\s\\:\\'\\-]"), "");
text_to_speak = text_to_speak.substr(0, text_to_speak.find_first_of("\n"));
std::lock_guard<std::mutex> lock(g_mutex);
const size_t pos = g_gpt2.prompt_base.find_first_of("\n");
if (pos != std::string::npos) {
g_gpt2.prompt_base = g_gpt2.prompt_base.substr(pos + 1);
}
g_gpt2.prompt_base += text_to_speak + "\n";
}
printf("gpt-2: %s\n", text_to_speak.c_str());
//printf("========================\n");
//printf("gpt-2: prompt_base:\n'%s'\n", g_gpt2.prompt_base.c_str());
//printf("========================\n");
{
std::lock_guard<std::mutex> lock(g_mutex);
t_last = std::chrono::high_resolution_clock::now();
g_text_to_speak = text_to_speak;
g_pcmf32.clear();
}
talk_set_status("speaking ...");
}
}
if (index < g_contexts.size()) {
whisper_free(g_contexts[index]);
g_contexts[index] = nullptr;
}
}
EMSCRIPTEN_BINDINGS(talk) {
emscripten::function("init", emscripten::optional_override([](const std::string & path_model) {
for (size_t i = 0; i < g_contexts.size(); ++i) {
if (g_contexts[i] == nullptr) {
g_contexts[i] = whisper_init(path_model.c_str());
if (g_contexts[i] != nullptr) {
g_running = true;
if (g_worker.joinable()) {
g_worker.join();
}
g_worker = std::thread([i]() {
talk_main(i);
});
return i + 1;
} else {
return (size_t) 0;
}
}
}
return (size_t) 0;
}));
emscripten::function("free", emscripten::optional_override([](size_t index) {
if (g_running) {
g_running = false;
}
}));
emscripten::function("set_audio", emscripten::optional_override([](size_t index, const emscripten::val & audio) {
--index;
if (index >= g_contexts.size()) {
return -1;
}
if (g_contexts[index] == nullptr) {
return -2;
}
{
std::lock_guard<std::mutex> lock(g_mutex);
const int n = audio["length"].as<int>();
emscripten::val heap = emscripten::val::module_property("HEAPU8");
emscripten::val memory = heap["buffer"];
g_pcmf32.resize(n);
emscripten::val memoryView = audio["constructor"].new_(memory, reinterpret_cast<uintptr_t>(g_pcmf32.data()), n);
memoryView.call<void>("set", audio);
}
return 0;
}));
emscripten::function("force_speak", emscripten::optional_override([](size_t index) {
{
std::lock_guard<std::mutex> lock(g_mutex);
g_force_speak = true;
}
}));
emscripten::function("get_text_context", emscripten::optional_override([]() {
std::string text_context;
{
std::lock_guard<std::mutex> lock(g_mutex);
text_context = g_gpt2.prompt_base;
}
return text_context;
}));
emscripten::function("get_text_to_speak", emscripten::optional_override([]() {
std::string text_to_speak;
{
std::lock_guard<std::mutex> lock(g_mutex);
text_to_speak = std::move(g_text_to_speak);
}
return text_to_speak;
}));
emscripten::function("get_status", emscripten::optional_override([]() {
std::string status;
{
std::lock_guard<std::mutex> lock(g_mutex);
status = g_status_forced.empty() ? g_status : g_status_forced;
}
return status;
}));
emscripten::function("set_status", emscripten::optional_override([](const std::string & status) {
{
std::lock_guard<std::mutex> lock(g_mutex);
g_status_forced = status;
}
}));
}