mirror of
https://github.com/ggerganov/llama.cpp.git
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clean-up
This commit is contained in:
Georgi Gerganov
parent
ef5ed47d82
commit
211cdf7326
@ -107,26 +107,29 @@ static void irfft(int n, const double * inp_cplx, double * out_real) {
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}
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}
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static void fold(
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const std::vector<double> & data,
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int64_t output_size,
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int64_t win_length,
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int64_t hop_length,
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int64_t pad,
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std::vector<double>& output
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) {
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int64_t output_height = output_size;
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int64_t kernel_w = win_length;
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int64_t stride_w = hop_length;
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int64_t width = output_size;
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//
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// y = torch.nn.functional.fold(
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// data, output_size=(1, output_size), kernel_size=(1, self.win_length), stride=(1, self.hop_length),
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// )[:, 0, 0, pad:-pad]
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//
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// data.shape = torch.Size([1, 1280, 261])
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// output_size = 84480
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// win_length = 1280
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// hop_length = 320
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// pad = 480
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//
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static void fold(const std::vector<double> & data, int64_t n_out, int64_t n_win, int64_t n_hop, int64_t n_pad, std::vector<double> & output) {
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int64_t output_height = n_out;
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int64_t kernel_w = n_win;
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int64_t stride_w = n_hop;
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int64_t width = n_out;
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output.resize(width, 0.0f);
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int64_t col_idx = 0;
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for (int64_t w_col = 0; w_col < width; ++w_col) {
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int64_t start = w_col * stride_w - pad;
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int64_t end = start + kernel_w;
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int64_t start = w_col * stride_w - n_pad;
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int64_t end = start + kernel_w;
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for (int64_t w_im = start; w_im < end; ++w_im) {
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if (w_im >= 0 && w_im < output_height) {
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@ -136,124 +139,55 @@ static void fold(
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}
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}
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output.resize(output_size - 2 * pad);
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output.resize(n_out - 2 * n_pad);
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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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struct wav_header {
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char riff[4] = {'R', 'I', 'F', 'F'};
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uint32_t chunk_size;
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char wave[4] = {'W', 'A', 'V', 'E'};
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char fmt[4] = {'f', 'm', 't', ' '};
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uint32_t fmt_chunk_size = 16;
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uint16_t audio_format = 1; // PCM
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uint16_t num_channels = 1; // Mono
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uint32_t sample_rate;
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uint32_t byte_rate;
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uint16_t block_align;
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uint16_t bits_per_sample = 16;
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char data[4] = {'d', 'a', 't', 'a'};
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uint32_t data_size;
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};
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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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static void save_wav16(const std::string & fname, const std::vector<double> & data, int sample_rate) {
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std::ofstream file(fname, std::ios::binary);
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if (!file) {
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LOG_ERR("%s: Failed to open file '%s' for writing", __func__, fname.c_str());
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return;
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}
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common_init();
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wav_header header;
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header.sample_rate = sample_rate;
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header.byte_rate = header.sample_rate * header.num_channels * (header.bits_per_sample / 8);
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header.block_align = header.num_channels * (header.bits_per_sample / 8);
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header.data_size = data.size() * (header.bits_per_sample / 8);
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header.chunk_size = 36 + header.data_size;
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// init LLM
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file.write(reinterpret_cast<const char*>(&header), sizeof(header));
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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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for (const auto & sample : data) {
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int16_t pcm_sample = static_cast<int16_t>(std::clamp(sample * 32767.0, -32768.0, 32767.0));
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file.write(reinterpret_cast<const char*>(&pcm_sample), sizeof(pcm_sample));
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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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file.close();
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}
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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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static std::vector<double> embd_to_audio(
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const float * embd,
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const std::vector<llama_token> & codes,
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const int n_embd,
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const int n_thread) {
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const int n = codes.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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@ -301,7 +235,6 @@ int main(int argc, char ** argv) {
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std::vector<double> res (n*n_fft);
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std::vector<double> hann2(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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@ -318,56 +251,151 @@ int main(int argc, char ** argv) {
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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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std::vector<double> audio;
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std::vector<double> env;
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fold(res, n_out, n_win, n_hop, n_pad, audio);
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fold(hann2, n_out, n_win, n_hop, n_pad, env);
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fold(hann2, n_out, n_win, n_hop, n_pad, env); // TODO: can be done once
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for (size_t i = 0; i < audio.size(); ++i) {
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audio[i] /= env[i];
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}
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//LOG("audio (%d):\n", audio.size());
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//for (int i = 0; i < 1000; ++i) {
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// LOG("%d: %8.5f\n", i, audio[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 < 1000; ++i) {
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// sum += audio[i];
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//}
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//LOG("sum: %f\n", sum);
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return audio;
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}
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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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int main(int argc, char ** argv) {
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common_params params;
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fprintf(stderr, "\n");
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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> codes = {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,
|
||||
153415, 151990, 153083, 152884, 151670, 198, 151668, 198, 151645};
|
||||
|
||||
{
|
||||
const std::string inp_txt = common_detokenize(ctx_ttc, codes, true);
|
||||
LOG_INF("prompt: '%s'\n", inp_txt.c_str());
|
||||
LOG_INF("%s: prompt size: %d\n", __func__, (int) codes.size());
|
||||
}
|
||||
|
||||
// remove all non-audio tokens (i.e. < 151672 || > 155772)
|
||||
codes.erase(std::remove_if(codes.begin(), codes.end(), [](llama_token t) { return t < 151672 || t > 155772; }), codes.end());
|
||||
|
||||
{
|
||||
const std::string inp_txt = common_detokenize(ctx_ttc, codes, true);
|
||||
LOG_INF("prompt audio: '%s'\n", inp_txt.c_str());
|
||||
LOG_INF("%s: prompt audio size: %d\n", __func__, (int) codes.size());
|
||||
}
|
||||
|
||||
for (auto & token : codes) {
|
||||
token -= 151672;
|
||||
}
|
||||
|
||||
const auto t_voc_start = ggml_time_us();
|
||||
|
||||
llama_batch batch = llama_batch_init(codes.size(), 0, 1);
|
||||
|
||||
// evaluate the initial prompt
|
||||
for (size_t i = 0; i < codes.size(); ++i) {
|
||||
common_batch_add(batch, codes[i], i, { 0 }, true); // TODO: all logits?
|
||||
}
|
||||
GGML_ASSERT(batch.n_tokens == (int) codes.size());
|
||||
|
||||
if (llama_decode(ctx_cts, batch) != 0) {
|
||||
LOG_ERR("%s: llama_decode() failed\n", __func__);
|
||||
return 1;
|
||||
}
|
||||
|
||||
llama_synchronize(ctx_cts);
|
||||
|
||||
LOG_INF("%s: time for vocoder: %.3f ms\n", __func__, (ggml_time_us() - t_voc_start) / 1000.0f);
|
||||
|
||||
const auto t_spec_start = ggml_time_us();
|
||||
|
||||
const int n_embd = llama_n_embd(model_cts);
|
||||
const float * embd = llama_get_embeddings(ctx_cts);
|
||||
|
||||
// spectral operations
|
||||
// TODO: not optimized at all
|
||||
auto audio = embd_to_audio(embd, codes, n_embd, params.cpuparams.n_threads);
|
||||
|
||||
const std::string fname = "output.wav";
|
||||
|
||||
const int n_sr = 24000; // sampling rate
|
||||
|
||||
LOG_INF("%s: time for spectral ops: %.3f ms\n", __func__, (ggml_time_us() - t_spec_start) / 1000.0f);
|
||||
LOG_INF("%s: total time: %.3f ms\n", __func__, (ggml_time_us() - t_main_start) / 1000.0f);
|
||||
|
||||
save_wav16(fname, audio, n_sr);
|
||||
|
||||
LOG_INF("%s: audio written to file '%s'\n", __func__, fname.c_str());
|
||||
|
||||
llama_free(ctx_ttc);
|
||||
llama_free_model(model_ttc);
|
||||
|
||||
@ -3846,10 +3846,6 @@ struct ggml_tensor * ggml_conv_1d(
|
||||
int d0) {
|
||||
struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, 0, p0, 0, d0, 0, false, GGML_TYPE_F16); // [N, OL, IC * K]
|
||||
|
||||
printf("a: %lld %lld %lld %lld\n", a->ne[0], a->ne[1], a->ne[2], a->ne[3]);
|
||||
printf("b: %lld %lld %lld %lld\n", b->ne[0], b->ne[1], b->ne[2], b->ne[3]);
|
||||
printf("im2col: %lld %lld %lld %lld\n", im2col->ne[0], im2col->ne[1], im2col->ne[2], im2col->ne[3]);
|
||||
|
||||
struct ggml_tensor * result =
|
||||
ggml_mul_mat(ctx,
|
||||
ggml_reshape_2d(ctx, im2col, im2col->ne[0], (im2col->ne[2] * im2col->ne[1])), // [N, OL, IC * K] => [N*OL, IC * K]
|
||||
|
||||
@ -17234,9 +17234,6 @@ struct llm_build_context {
|
||||
|
||||
cur = ggml_cont(ctx0, ggml_transpose(ctx0, inpL));
|
||||
|
||||
printf("cur: %d %d %d\n", cur->ne[0], cur->ne[1], cur->ne[2]);
|
||||
printf("conv1d: %d %d %d\n", model.conv_1d->ne[0], model.conv_1d->ne[1], model.conv_1d->ne[2]);
|
||||
|
||||
cur = ggml_conv_1d_ph(ctx0, model.conv_1d, cur, 1, 1);
|
||||
cur = ggml_add(ctx0, cur, ggml_reshape_2d(ctx0, model.conv_1d_b, 1, model.conv_1d_b->ne[0]));
|
||||
|
||||
@ -17445,8 +17442,6 @@ struct llm_build_context {
|
||||
cur = ggml_add(ctx0, cur, model.output_b);
|
||||
cb(cur, "result_embd", -1);
|
||||
|
||||
printf("cur: %d %d %d\n", cur->ne[0], cur->ne[1], cur->ne[2]);
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
|
||||
return gf;
|
||||
|
||||
Loading…
Reference in New Issue
Block a user