mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-29 04:44:34 +00:00
323 lines
10 KiB
C++
323 lines
10 KiB
C++
#include "arg.h"
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#include "common.h"
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#include "sampling.h"
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#include "log.h"
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#include "llama.h"
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#include <algorithm>
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#include <cstdio>
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#include <string>
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#include <vector>
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#include <fstream>
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#include <thread>
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//
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// Terminal utils
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//
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#define SQR(X) ((X) * (X))
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#define UNCUBE(x) x < 48 ? 0 : x < 115 ? 1 : (x - 35) / 40
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/**
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* Quantizes 24-bit RGB to xterm256 code range [16,256).
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*/
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static int rgb2xterm256(int r, int g, int b) {
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unsigned char cube[] = {0, 0137, 0207, 0257, 0327, 0377};
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int av, ir, ig, ib, il, qr, qg, qb, ql;
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av = r * .299 + g * .587 + b * .114 + .5;
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ql = (il = av > 238 ? 23 : (av - 3) / 10) * 10 + 8;
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qr = cube[(ir = UNCUBE(r))];
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qg = cube[(ig = UNCUBE(g))];
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qb = cube[(ib = UNCUBE(b))];
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if (SQR(qr - r) + SQR(qg - g) + SQR(qb - b) <=
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SQR(ql - r) + SQR(ql - g) + SQR(ql - b))
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return ir * 36 + ig * 6 + ib + 020;
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return il + 0350;
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}
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static std::string set_xterm256_foreground(int r, int g, int b) {
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int x = rgb2xterm256(r, g, b);
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std::ostringstream oss;
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oss << "\033[38;5;" << x << "m";
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return oss.str();
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}
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const std::vector<std::string> k_colors = {
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set_xterm256_foreground(220, 5, 12),
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set_xterm256_foreground(232, 96, 28),
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set_xterm256_foreground(241, 147, 45),
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set_xterm256_foreground(246, 193, 65),
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set_xterm256_foreground(247, 240, 86),
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set_xterm256_foreground(144, 201, 135),
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set_xterm256_foreground( 78, 178, 101),
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};
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static void print_usage(int, char ** argv) {
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LOG("\nexample usage:\n");
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LOG("\n %s -m model.gguf -p \"Hello!\"\n", argv[0]);
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LOG("\n");
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}
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void fill_hann_window(int length, bool periodic, float * output) {
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int offset = -1;
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if (periodic) {
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offset = 0;
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}
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for (int i = 0; i < length; i++) {
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output[i] = 0.5 * (1.0 - cosf((2.0 * M_PI * i) / (length + offset)));
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}
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}
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// very poor-man fft
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void twiddle(float * real, float * imag, int k, int N) {
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float angle = 2 * M_PI * k / N;
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*real = cos(angle);
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*imag = sin(angle);
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}
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void irfft(int n, float * inp_cplx, float * out_real) {
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int N = n / 2 + 1;
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std::vector<float> real_input(N);
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std::vector<float> imag_input(N);
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for (int i = 0; i < N; ++i) {
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real_input[i] = inp_cplx[2 * i];
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imag_input[i] = inp_cplx[2 * i + 1];
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}
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std::vector<float> real_output(n);
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std::vector<float> imag_output(n);
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for (int k = 0; k < n; ++k) {
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real_output[k] = 0.0f;
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imag_output[k] = 0.0f;
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for (int m = 0; m < N; ++m) {
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float twiddle_real;
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float twiddle_imag;
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twiddle(&twiddle_real, &twiddle_imag, k * m, n);
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real_output[k] += real_input[m] * twiddle_real - imag_input[m] * twiddle_imag;
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imag_output[k] += real_input[m] * twiddle_imag + imag_input[m] * twiddle_real;
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}
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}
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for (int i = 0; i < n; ++i) {
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out_real[i] = real_output[i] / N;
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}
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}
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int main(int argc, char ** argv) {
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common_params params;
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params.prompt = "";
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params.n_predict = 1024;
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params.n_batch = 8192;
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params.n_ctx = 8192;
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if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_TTS, print_usage)) {
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return 1;
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}
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common_init();
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// init LLM
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llama_backend_init();
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llama_numa_init(params.numa);
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llama_model * model_ttc = NULL; // text-to-codes
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llama_model * model_cts = NULL; // codes-to-speech
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llama_context * ctx_ttc = NULL;
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llama_context * ctx_cts = NULL;
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common_init_result llama_init_ttc = common_init_from_params(params);
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model_ttc = llama_init_ttc.model;
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ctx_ttc = llama_init_ttc.context;
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params.model = params.vocoder.model;
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params.embedding = true;
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common_init_result llama_init_cts = common_init_from_params(params);
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model_cts = llama_init_cts.model;
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ctx_cts = llama_init_cts.context;
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const auto t_main_start = ggml_time_us();
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std::vector<llama_token> prompt_inp = {198, 88225, 155856, 151669, 152205,
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153064, 152537, 153421, 153209, 152524, 151689, 152993, 152438, 152695,
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153091, 152945, 152829, 152534, 152934, 153020, 151997, 152263, 153010,
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153146, 152399, 153208, 152496, 151793, 152848, 152263, 152571, 153286,
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152227, 153300, 152934, 152263, 153208, 152263, 152965, 152430, 152296,
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153146, 152920, 152376, 152556, 153363, 151775, 152044, 152972, 152690,
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153379, 152368, 152233, 153422, 152490, 151996, 152022, 151694, 152061,
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153238, 152539, 153356, 152640, 153021, 153123, 151962, 153094, 151670,
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198, 20339, 13189, 155824, 151669, 152070, 152007, 152910, 151683,
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152000, 152373, 152760, 152046, 151735, 152334, 152394, 153073, 152908,
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151856, 151953, 153247, 153293, 151903, 153480, 153168, 152478, 153359,
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153429, 151905, 151678, 152567, 152411, 152165, 152556, 153075, 153424,
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151993, 152999, 153078, 152151, 152088, 153389, 152484, 151874, 151670,
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198, 285, 155784, 151669, 152226, 152126, 152638, 153215, 151729,
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152959, 153479, 153059, 151838, 151670, 198, 1782, 155783, 151669,
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153288, 153055, 153314, 152497, 152962, 152741, 152076, 153253, 151670,
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198, 471, 16488, 155825, 151669, 152060, 152916, 151893, 153469, 152501,
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152080, 152743, 151932, 153161, 152096, 152761, 152698, 153401, 153242,
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153336, 152441, 152838, 153467, 152706, 153496, 153310, 152422, 153360,
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153115, 152763, 151998, 152373, 153450, 152554, 151968, 153323, 152055,
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152468, 153111, 153358, 152813, 152010, 151770, 152823, 152960, 151670,
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198, 22627, 155823, 151669, 152814, 152366, 153484, 152931, 153441,
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152164, 152877, 152915, 153463, 151692, 152911, 152747, 152776, 151831,
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153449, 151882, 152975, 152031, 152513, 153150, 152448, 152667, 153133,
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153189, 152619, 153466, 152054, 152106, 153119, 152277, 152439, 153109,
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152997, 152141, 153154, 153256, 153311, 151922, 151670, 198, 1055,
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155781, 151669, 152633, 151850, 153060, 153270, 152560, 153348, 152729,
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151670, 198, 25312, 155803, 151669, 152521, 153403, 152561, 153337,
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153383, 152199, 153493, 153326, 151830, 152254, 152248, 152349, 152153,
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153007, 151823, 153037, 152575, 152457, 152406, 152592, 153116, 153365,
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153456, 151670, 198, 88225, 155817, 151669, 153271, 151925, 152218,
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152418, 152253, 153140, 151903, 153151, 152626, 152338, 152647, 153464,
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152785, 152768, 151711, 152037, 152033, 151804, 152216, 151701, 151855,
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152348, 152995, 152955, 152905, 152342, 152340, 153391, 153453, 152418,
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153415, 151990, 153083, 152884, 151670, 198, 151668, 198, 151645};
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{
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const std::string inp_txt = common_detokenize(ctx_ttc, prompt_inp, true);
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LOG_INF("prompt: '%s'\n", inp_txt.c_str());
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LOG_INF("%s: prompt size: %d\n", __func__, (int) prompt_inp.size());
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}
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// remove all non-audio tokens (i.e. < 151672 || > 155772)
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prompt_inp.erase(std::remove_if(prompt_inp.begin(), prompt_inp.end(), [](llama_token t) { return t < 151672 || t > 155772; }), prompt_inp.end());
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{
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const std::string inp_txt = common_detokenize(ctx_ttc, prompt_inp, true);
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LOG_INF("prompt audio: '%s'\n", inp_txt.c_str());
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LOG_INF("%s: prompt audio size: %d\n", __func__, (int) prompt_inp.size());
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}
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for (auto & token : prompt_inp) {
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token -= 151672;
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}
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llama_batch batch = llama_batch_init(prompt_inp.size(), 0, 1);
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// evaluate the initial prompt
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for (size_t i = 0; i < prompt_inp.size(); ++i) {
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common_batch_add(batch, prompt_inp[i], i, { 0 }, true); // TODO: all logits?
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}
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GGML_ASSERT(batch.n_tokens == (int) prompt_inp.size());
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if (llama_decode(ctx_cts, batch) != 0) {
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LOG_ERR("%s: llama_decode() failed\n", __func__);
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return 1;
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}
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llama_synchronize(ctx_cts);
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LOG_INF("%s: time for prompt: %.3f ms\n", __func__, (ggml_time_us() - t_main_start) / 1000.0f);
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const int n_embd = llama_n_embd(model_cts);
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const float * embd = llama_get_embeddings(ctx_cts);
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const int n = prompt_inp.size();
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const int n_fft = 1280;
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const int n_hop = 320;
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const int n_win = 1280;
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const int n_pad = (n_win - n_hop)/2;
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std::vector<float> hann(n_fft);
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fill_hann_window(hann.size(), true, hann.data());
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int n_spec = n_embd*n;
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std::vector<float> E (n_spec);
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std::vector<float> S (n_spec);
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std::vector<float> ST(n_spec);
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for (int l = 0; l < n; ++l) {
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for (int k = 0; k < n_embd; ++k) {
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E[k*n + l] = embd[l*n_embd + k];
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}
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}
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for (int k = 0; k < n_embd/2; ++k) {
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for (int l = 0; l < n; ++l) {
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float mag = E[(k )*n + l];
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float phi = E[(k + n_embd/2)*n + l];
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mag = exp(mag);
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if (mag > 1e2) {
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mag = 1e2;
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}
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S[2*(k*n + l) + 0] = mag*cosf(phi);
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S[2*(k*n + l) + 1] = mag*sinf(phi);
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}
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}
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for (int l = 0; l < n; ++l) {
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for (int k = 0; k < n_embd/2; ++k) {
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ST[l*n_embd + 2*k + 0] = S[2*(k*n + l) + 0];
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ST[l*n_embd + 2*k + 1] = S[2*(k*n + l) + 1];
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}
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}
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std::vector<float> res(n*n_fft);
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const int n_thread = std::thread::hardware_concurrency();
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std::vector<std::thread> workers(n_thread);
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for (int i = 0; i < n_thread; ++i) {
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workers[i] = std::thread([&, i]() {
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for (int l = i; l < n; l += n_thread) {
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irfft(n_fft, ST.data() + l*n_embd, res.data() + l*n_fft);
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}
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});
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}
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for (int i = 0; i < n_thread; ++i) {
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workers[i].join();
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}
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LOG("result (%d):\n", res.size());
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for (int i = 0; i < n_fft; ++i) {
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LOG("%d - %8.5f\n", i, res[5*n_fft + i]);
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}
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LOG("\n");
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double sum = 0.0;
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for (int i = 0; i < n_fft; ++i) {
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sum += res[5*n_fft + i];
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}
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LOG("sum: %f\n", sum);
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{
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LOG("result:\n");
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for (int i = 0; i < 10; ++i) {
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LOG("%8.3f ", S[i]);
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}
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LOG("\n");
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for (int i = n_spec - 10; i < n_spec; ++i) {
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LOG("%8.3f ", S[i]);
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}
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LOG("\n");
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double sum = 0.0;
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for (int i = 0; i < n_spec; ++i) {
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sum += S[i];
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}
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LOG("sum: %f\n", sum);
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}
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fprintf(stderr, "\n");
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llama_free(ctx_ttc);
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llama_free_model(model_ttc);
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llama_free(ctx_cts);
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llama_free_model(model_cts);
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llama_backend_free();
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return 0;
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}
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