--- /dev/null
+#include "ggml.h"
+#include "gpt-2.h"
+
+#include <cmath>
+#include <cstdio>
+#include <cstring>
+#include <fstream>
+#include <map>
+#include <string>
+#include <thread>
+#include <vector>
+#include <regex>
+#include <random>
+
+/////////////////////// GPT-2 BEGIN /////////////////////////
+
+//
+// Vocab utils
+//
+
+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;
+}
+
+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 = 5640ull*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_context {
+ 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. I love the weather this time of year.
+I wish it would rain a little bit.
+Me too.
+)";
+
+ std::mt19937 rng;
+
+ gpt_vocab vocab;
+ gpt2_model model;
+
+ int32_t n_threads = std::min(N_THREAD, (int) std::thread::hardware_concurrency());
+
+ // sampling parameters
+ int32_t top_k = 20;
+ float top_p = 0.98f;
+ float temp = 1.0f;
+};
+
+struct gpt2_context * gpt2_init(const char * path_model) {
+ gpt2_context * ctx = new gpt2_context;
+
+ ctx->rng = std::mt19937(time(NULL));
+
+ // load the model
+ {
+ const int64_t t_start_us = ggml_time_us();
+
+ if (!gpt2_model_load(path_model, ctx->model, ctx->vocab)) {
+ fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, "gpt-2.bin");
+ return nullptr;
+ }
+
+ 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));
+ }
+
+ return ctx;
+}
+
+void gpt2_free(struct gpt2_context * ctx) {
+ delete ctx;
+}
+
+const char * gpt2_get_prompt(struct gpt2_context * ctx) {
+ return ctx->prompt_base.c_str();
+}
+
+void gpt2_set_prompt(struct gpt2_context * ctx, const char * prompt) {
+ ctx->prompt_base = prompt;
+}
+
+std::vector<gpt_vocab::id> gpt2_tokenize(const gpt2_context * ctx, const char * text) {
+ return ::gpt_tokenize(ctx->vocab, text);
+}
+
+std::string gpt2_gen_text(gpt2_context * ctx, const char * text, int max_tokens) {
+ int n_past = 0;
+
+ std::vector<float> embd_w;
+
+ // tokenize the prompt
+ std::vector<gpt_vocab::id> embd_inp = ::gpt2_tokenize(ctx, text);
+
+ int n_predict = std::min(max_tokens, ctx->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() + n_predict; i++) {
+ // predict
+ if (embd.size() > 0) {
+ if (!gpt2_eval(ctx->model, ctx->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 = ctx->top_k;
+ const float top_p = ctx->top_p;
+ const float temp = ctx->temp;
+
+ const int n_vocab = ctx->model.hparams.n_vocab;
+
+ const gpt_vocab::id id = gpt_sample_top_k_top_p(ctx->vocab, embd_w.data() + (embd_w.size() - n_vocab), top_k, top_p, temp, ctx->rng);
+
+ // add it to the context
+ embd.push_back(id);
+ }
+
+ result += ctx->vocab.id_to_token[embd[0]];
+
+ // end of text token
+ if (embd.back() == 50256 ||
+ ctx->vocab.id_to_token[embd.back()] == "." ||
+ ctx->vocab.id_to_token[embd.back()] == "!" ||
+ ctx->vocab.id_to_token[embd.back()] == "?") {
+ break;
+ }
+ }
+
+ return result;
+}
--- /dev/null
+// Talk with AI
+//
+
+#include "whisper.h"
+#include "gpt-2.h"
+
+#include <SDL.h>
+#include <SDL_audio.h>
+
+#include <cassert>
+#include <cstdio>
+#include <fstream>
+#include <mutex>
+#include <regex>
+#include <string>
+#include <thread>
+#include <vector>
+#include <regex>
+
+// command-line parameters
+struct whisper_params {
+ int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
+ int32_t voice_ms = 10000;
+ int32_t capture_id = -1;
+ int32_t max_tokens = 32;
+ int32_t audio_ctx = 0;
+
+ float vad_thold = 0.6f;
+ float freq_thold = 100.0f;
+
+ bool speed_up = false;
+ bool translate = false;
+ bool print_special = false;
+ bool print_energy = false;
+ bool no_timestamps = true;
+
+ std::string person = "Santa";
+ std::string language = "en";
+ std::string model_wsp = "models/ggml-base.en.bin";
+ std::string model_gpt = "models/ggml-gpt-2-117M.bin";
+ std::string speak = "./examples/talk/speak.sh";
+ std::string fname_out = "";
+};
+
+void whisper_print_usage(int argc, char ** argv, const whisper_params & params);
+
+bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
+ for (int i = 1; i < argc; i++) {
+ std::string arg = argv[i];
+
+ if (arg == "-h" || arg == "--help") {
+ whisper_print_usage(argc, argv, params);
+ exit(0);
+ }
+ else if (arg == "-t" || arg == "--threads") { params.n_threads = std::stoi(argv[++i]); }
+ else if (arg == "-vms" || arg == "--voice-ms") { params.voice_ms = std::stoi(argv[++i]); }
+ else if (arg == "-c" || arg == "--capture") { params.capture_id = std::stoi(argv[++i]); }
+ else if (arg == "-mt" || arg == "--max-tokens") { params.max_tokens = std::stoi(argv[++i]); }
+ else if (arg == "-ac" || arg == "--audio-ctx") { params.audio_ctx = std::stoi(argv[++i]); }
+ else if (arg == "-vth" || arg == "--vad-thold") { params.vad_thold = std::stof(argv[++i]); }
+ else if (arg == "-fth" || arg == "--freq-thold") { params.freq_thold = std::stof(argv[++i]); }
+ else if (arg == "-su" || arg == "--speed-up") { params.speed_up = true; }
+ else if (arg == "-tr" || arg == "--translate") { params.translate = true; }
+ else if (arg == "-ps" || arg == "--print-special") { params.print_special = true; }
+ else if (arg == "-pe" || arg == "--print-energy") { params.print_energy = true; }
+ else if (arg == "-p" || arg == "--person") { params.person = argv[++i]; }
+ else if (arg == "-l" || arg == "--language") { params.language = argv[++i]; }
+ else if (arg == "-mw" || arg == "--model-whisper") { params.model_wsp = argv[++i]; }
+ else if (arg == "-mg" || arg == "--model-gpt") { params.model_gpt = argv[++i]; }
+ else if (arg == "-s" || arg == "--speak") { params.speak = argv[++i]; }
+ else if (arg == "-f" || arg == "--file") { params.fname_out = argv[++i]; }
+ else {
+ fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
+ whisper_print_usage(argc, argv, params);
+ exit(0);
+ }
+ }
+
+ return true;
+}
+
+void whisper_print_usage(int argc, char ** argv, const whisper_params & params) {
+ fprintf(stderr, "\n");
+ fprintf(stderr, "usage: %s [options]\n", argv[0]);
+ fprintf(stderr, "\n");
+ fprintf(stderr, "options:\n");
+ fprintf(stderr, " -h, --help [default] show this help message and exit\n");
+ fprintf(stderr, " -t N, --threads N [%-7d] number of threads to use during computation\n", params.n_threads);
+ fprintf(stderr, " -vms N, --voice-ms N [%-7d] voice duration in milliseconds\n", params.voice_ms);
+ fprintf(stderr, " -c ID, --capture ID [%-7d] capture device ID\n", params.capture_id);
+ fprintf(stderr, " -mt N, --max-tokens N [%-7d] maximum number of tokens per audio chunk\n", params.max_tokens);
+ fprintf(stderr, " -ac N, --audio-ctx N [%-7d] audio context size (0 - all)\n", params.audio_ctx);
+ fprintf(stderr, " -vth N, --vad-thold N [%-7.2f] voice activity detection threshold\n", params.vad_thold);
+ fprintf(stderr, " -fth N, --freq-thold N [%-7.2f] high-pass frequency cutoff\n", params.freq_thold);
+ fprintf(stderr, " -su, --speed-up [%-7s] speed up audio by x2 (reduced accuracy)\n", params.speed_up ? "true" : "false");
+ fprintf(stderr, " -tr, --translate [%-7s] translate from source language to english\n", params.translate ? "true" : "false");
+ fprintf(stderr, " -ps, --print-special [%-7s] print special tokens\n", params.print_special ? "true" : "false");
+ fprintf(stderr, " -pe, --print-energy [%-7s] print sound energy (for debugging)\n", params.print_energy ? "true" : "false");
+ fprintf(stderr, " -p NAME, --person NAME [%-7s] person name (for prompt selection)\n", params.person.c_str());
+ fprintf(stderr, " -l LANG, --language LANG [%-7s] spoken language\n", params.language.c_str());
+ fprintf(stderr, " -mw FILE, --model-whisper [%-7s] whisper model file\n", params.model_wsp.c_str());
+ fprintf(stderr, " -mg FILE, --model-gpt [%-7s] gpt model file\n", params.model_gpt.c_str());
+ fprintf(stderr, " -s FILE, --speak TEXT [%-7s] command for TTS\n", params.speak.c_str());
+ fprintf(stderr, " -f FNAME, --file FNAME [%-7s] text output file name\n", params.fname_out.c_str());
+ fprintf(stderr, "\n");
+}
+
+//
+// SDL Audio capture
+//
+
+class audio_async {
+public:
+ audio_async(int len_ms);
+ ~audio_async();
+
+ bool init(int capture_id, int sample_rate);
+
+ // start capturing audio via the provided SDL callback
+ // keep last len_ms seconds of audio in a circular buffer
+ bool resume();
+ bool pause();
+ bool clear();
+
+ // callback to be called by SDL
+ void callback(uint8_t * stream, int len);
+
+ // get audio data from the circular buffer
+ void get(int ms, std::vector<float> & audio);
+
+private:
+ SDL_AudioDeviceID m_dev_id_in = 0;
+
+ int m_len_ms = 0;
+ int m_sample_rate = 0;
+
+ bool m_running = false;
+ std::mutex m_mutex;
+
+ std::vector<float> m_audio;
+ std::vector<float> m_audio_new;
+ size_t m_audio_pos = 0;
+ size_t m_audio_len = 0;
+};
+
+audio_async::audio_async(int len_ms) {
+ m_len_ms = len_ms;
+}
+
+audio_async::~audio_async() {
+ if (m_dev_id_in) {
+ SDL_CloseAudioDevice(m_dev_id_in);
+ }
+}
+
+bool audio_async::init(int capture_id, int sample_rate) {
+ SDL_LogSetPriority(SDL_LOG_CATEGORY_APPLICATION, SDL_LOG_PRIORITY_INFO);
+
+ if (SDL_Init(SDL_INIT_AUDIO) < 0) {
+ SDL_LogError(SDL_LOG_CATEGORY_APPLICATION, "Couldn't initialize SDL: %s\n", SDL_GetError());
+ return false;
+ }
+
+ SDL_SetHintWithPriority(SDL_HINT_AUDIO_RESAMPLING_MODE, "medium", SDL_HINT_OVERRIDE);
+
+ {
+ int nDevices = SDL_GetNumAudioDevices(SDL_TRUE);
+ fprintf(stderr, "%s: found %d capture devices:\n", __func__, nDevices);
+ for (int i = 0; i < nDevices; i++) {
+ fprintf(stderr, "%s: - Capture device #%d: '%s'\n", __func__, i, SDL_GetAudioDeviceName(i, SDL_TRUE));
+ }
+ }
+
+ SDL_AudioSpec capture_spec_requested;
+ SDL_AudioSpec capture_spec_obtained;
+
+ SDL_zero(capture_spec_requested);
+ SDL_zero(capture_spec_obtained);
+
+ capture_spec_requested.freq = sample_rate;
+ capture_spec_requested.format = AUDIO_F32;
+ capture_spec_requested.channels = 1;
+ capture_spec_requested.samples = 1024;
+ capture_spec_requested.callback = [](void * userdata, uint8_t * stream, int len) {
+ audio_async * audio = (audio_async *) userdata;
+ audio->callback(stream, len);
+ };
+ capture_spec_requested.userdata = this;
+
+ if (capture_id >= 0) {
+ fprintf(stderr, "%s: attempt to open capture device %d : '%s' ...\n", __func__, capture_id, SDL_GetAudioDeviceName(capture_id, SDL_TRUE));
+ m_dev_id_in = SDL_OpenAudioDevice(SDL_GetAudioDeviceName(capture_id, SDL_TRUE), SDL_TRUE, &capture_spec_requested, &capture_spec_obtained, 0);
+ } else {
+ fprintf(stderr, "%s: attempt to open default capture device ...\n", __func__);
+ m_dev_id_in = SDL_OpenAudioDevice(nullptr, SDL_TRUE, &capture_spec_requested, &capture_spec_obtained, 0);
+ }
+
+ if (!m_dev_id_in) {
+ fprintf(stderr, "%s: couldn't open an audio device for capture: %s!\n", __func__, SDL_GetError());
+ m_dev_id_in = 0;
+
+ return false;
+ } else {
+ fprintf(stderr, "%s: obtained spec for input device (SDL Id = %d):\n", __func__, m_dev_id_in);
+ fprintf(stderr, "%s: - sample rate: %d\n", __func__, capture_spec_obtained.freq);
+ fprintf(stderr, "%s: - format: %d (required: %d)\n", __func__, capture_spec_obtained.format,
+ capture_spec_requested.format);
+ fprintf(stderr, "%s: - channels: %d (required: %d)\n", __func__, capture_spec_obtained.channels,
+ capture_spec_requested.channels);
+ fprintf(stderr, "%s: - samples per frame: %d\n", __func__, capture_spec_obtained.samples);
+ fprintf(stderr, "\n");
+ }
+
+ m_sample_rate = capture_spec_obtained.freq;
+
+ m_audio.resize((m_sample_rate*m_len_ms)/1000);
+
+ return true;
+}
+
+bool audio_async::resume() {
+ if (!m_dev_id_in) {
+ fprintf(stderr, "%s: no audio device to resume!\n", __func__);
+ return false;
+ }
+
+ if (m_running) {
+ fprintf(stderr, "%s: already running!\n", __func__);
+ return false;
+ }
+
+ SDL_PauseAudioDevice(m_dev_id_in, 0);
+
+ m_running = true;
+
+ return true;
+}
+
+bool audio_async::pause() {
+ if (!m_dev_id_in) {
+ fprintf(stderr, "%s: no audio device to pause!\n", __func__);
+ return false;
+ }
+
+ if (!m_running) {
+ fprintf(stderr, "%s: already paused!\n", __func__);
+ return false;
+ }
+
+ SDL_PauseAudioDevice(m_dev_id_in, 1);
+
+ m_running = false;
+
+ return true;
+}
+
+bool audio_async::clear() {
+ if (!m_dev_id_in) {
+ fprintf(stderr, "%s: no audio device to clear!\n", __func__);
+ return false;
+ }
+
+ if (!m_running) {
+ fprintf(stderr, "%s: not running!\n", __func__);
+ return false;
+ }
+
+ {
+ std::lock_guard<std::mutex> lock(m_mutex);
+
+ m_audio_pos = 0;
+ m_audio_len = 0;
+ }
+
+ return true;
+}
+
+// callback to be called by SDL
+void audio_async::callback(uint8_t * stream, int len) {
+ if (!m_running) {
+ return;
+ }
+
+ const size_t n_samples = len / sizeof(float);
+
+ m_audio_new.resize(n_samples);
+ memcpy(m_audio_new.data(), stream, n_samples * sizeof(float));
+
+ //fprintf(stderr, "%s: %zu samples, pos %zu, len %zu\n", __func__, n_samples, m_audio_pos, m_audio_len);
+
+ {
+ std::lock_guard<std::mutex> lock(m_mutex);
+
+ if (m_audio_pos + n_samples > m_audio.size()) {
+ const size_t n0 = m_audio.size() - m_audio_pos;
+
+ memcpy(&m_audio[m_audio_pos], stream, n0 * sizeof(float));
+ memcpy(&m_audio[0], &stream[n0], (n_samples - n0) * sizeof(float));
+
+ m_audio_pos = (m_audio_pos + n_samples) % m_audio.size();
+ m_audio_len = m_audio.size();
+ } else {
+ memcpy(&m_audio[m_audio_pos], stream, n_samples * sizeof(float));
+
+ m_audio_pos = (m_audio_pos + n_samples) % m_audio.size();
+ m_audio_len = std::min(m_audio_len + n_samples, m_audio.size());
+ }
+ }
+}
+
+void audio_async::get(int ms, std::vector<float> & result) {
+ if (!m_dev_id_in) {
+ fprintf(stderr, "%s: no audio device to get audio from!\n", __func__);
+ return;
+ }
+
+ if (!m_running) {
+ fprintf(stderr, "%s: not running!\n", __func__);
+ return;
+ }
+
+ result.clear();
+
+ {
+ std::lock_guard<std::mutex> lock(m_mutex);
+
+ if (ms <= 0) {
+ ms = m_len_ms;
+ }
+
+ size_t n_samples = (m_sample_rate * ms) / 1000;
+ if (n_samples > m_audio_len) {
+ n_samples = m_audio_len;
+ }
+
+ result.resize(n_samples);
+
+ int s0 = m_audio_pos - n_samples;
+ if (s0 < 0) {
+ s0 += m_audio.size();
+ }
+
+ if (s0 + n_samples > m_audio.size()) {
+ const size_t n0 = m_audio.size() - s0;
+
+ memcpy(result.data(), &m_audio[s0], n0 * sizeof(float));
+ memcpy(&result[n0], &m_audio[0], (n_samples - n0) * sizeof(float));
+ } else {
+ memcpy(result.data(), &m_audio[s0], n_samples * sizeof(float));
+ }
+ }
+}
+
+///////////////////////////
+
+std::string trim(const std::string & s) {
+ std::regex e("^\\s+|\\s+$");
+ return std::regex_replace(s, e, "");
+}
+
+std::string replace(const std::string & s, const std::string & from, const std::string & to) {
+ std::string result = s;
+ size_t pos = 0;
+ while ((pos = result.find(from, pos)) != std::string::npos) {
+ result.replace(pos, from.length(), to);
+ pos += to.length();
+ }
+ return result;
+}
+
+void high_pass_filter(std::vector<float> & data, float cutoff, float sample_rate) {
+ const float rc = 1.0f / (2.0f * M_PI * cutoff);
+ const float dt = 1.0f / sample_rate;
+ const float alpha = dt / (rc + dt);
+
+ float y = data[0];
+
+ for (size_t i = 1; i < data.size(); i++) {
+ y = alpha * (y + data[i] - data[i - 1]);
+ data[i] = y;
+ }
+}
+
+bool vad_simple(std::vector<float> & pcmf32, int sample_rate, int last_ms, float vad_thold, float freq_thold, bool verbose) {
+ const int n_samples = pcmf32.size();
+ const int n_samples_last = (sample_rate * last_ms) / 1000;
+
+ if (n_samples_last >= n_samples) {
+ // not enough samples - assume no speech
+ return false;
+ }
+
+ if (freq_thold > 0.0f) {
+ high_pass_filter(pcmf32, freq_thold, sample_rate);
+ }
+
+ float energy_all = 0.0f;
+ float energy_last = 0.0f;
+
+ for (size_t i = 0; i < n_samples; i++) {
+ energy_all += fabsf(pcmf32[i]);
+
+ if (i >= n_samples - n_samples_last) {
+ energy_last += fabsf(pcmf32[i]);
+ }
+ }
+
+ energy_all /= n_samples;
+ energy_last /= n_samples_last;
+
+ if (verbose) {
+ fprintf(stderr, "%s: energy_all: %f, energy_last: %f, vad_thold: %f, freq_thold: %f\n", __func__, energy_all, energy_last, vad_thold, freq_thold);
+ }
+
+ if (energy_last > vad_thold*energy_all) {
+ return false;
+ }
+
+ return true;
+}
+
+std::string transcribe(whisper_context * ctx, const whisper_params & params, const std::vector<float> & pcmf32, float & prob, int64_t & t_ms) {
+ const auto t_start = std::chrono::high_resolution_clock::now();
+
+ prob = 0.0f;
+ t_ms = 0;
+
+ whisper_full_params wparams = whisper_full_default_params(WHISPER_SAMPLING_GREEDY);
+
+ wparams.print_progress = false;
+ wparams.print_special = params.print_special;
+ wparams.print_realtime = false;
+ wparams.print_timestamps = !params.no_timestamps;
+ wparams.translate = params.translate;
+ wparams.no_context = true;
+ wparams.single_segment = true;
+ wparams.max_tokens = params.max_tokens;
+ wparams.language = params.language.c_str();
+ wparams.n_threads = params.n_threads;
+
+ wparams.audio_ctx = params.audio_ctx;
+ wparams.speed_up = params.speed_up;
+
+ if (whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size()) != 0) {
+ return "";
+ }
+
+ int prob_n = 0;
+ std::string result;
+
+ const int n_segments = whisper_full_n_segments(ctx);
+ for (int i = 0; i < n_segments; ++i) {
+ const char * text = whisper_full_get_segment_text(ctx, i);
+
+ result += text;
+
+ const int n_tokens = whisper_full_n_tokens(ctx, i);
+ for (int j = 0; j < n_tokens; ++j) {
+ const auto token = whisper_full_get_token_data(ctx, i, j);
+
+ prob += token.p;
+ ++prob_n;
+ }
+ }
+
+ if (prob_n > 0) {
+ prob /= prob_n;
+ }
+
+ const auto t_end = std::chrono::high_resolution_clock::now();
+ t_ms = std::chrono::duration_cast<std::chrono::milliseconds>(t_end - t_start).count();
+
+ return result;
+}
+
+// compute similarity between two strings using Levenshtein distance
+float similarity(const std::string & s0, const std::string & s1) {
+ const size_t len0 = s0.size() + 1;
+ const size_t len1 = s1.size() + 1;
+
+ std::vector<int> col(len1, 0);
+ std::vector<int> prevCol(len1, 0);
+
+ for (size_t i = 0; i < len1; i++) {
+ prevCol[i] = i;
+ }
+
+ for (size_t i = 0; i < len0; i++) {
+ col[0] = i;
+ for (size_t j = 1; j < len1; j++) {
+ col[j] = std::min(std::min(1 + col[j - 1], 1 + prevCol[j]), prevCol[j - 1] + (s0[i - 1] == s1[j - 1] ? 0 : 1));
+ }
+ col.swap(prevCol);
+ }
+
+ const float dist = prevCol[len1 - 1];
+
+ return 1.0f - (dist / std::max(s0.size(), s1.size()));
+}
+
+// generated with ChatGPT
+std::map<std::string, std::string> k_prompts = {
+ { "Santa",
+R"(Kid: Hi Santa! Are you real?
+Santa: Of course I am, my dear! Ho ho ho!
+Kid: Can you please bring me a new toy for Christmas?
+Santa: I'll see what I can do, but you have to make sure to be a good boy or girl and listen to your parents.
+Kid: I will, Santa! Thank you!
+Santa: You're welcome, little one. Merry Christmas! Ho ho ho!
+Kid: Can you tell me how you deliver all the presents to all the kids in the world in one night?
+Santa: It's a secret, but I have a lot of help from my elves and my magical sleigh. And I have a special route that I follow to make sure I visit every child.
+Kid: Wow, that's amazing! Can I please have a ride in your sleigh sometime?
+Santa: I'm sorry, but only good boys and girls get to ride in my sleigh.
+)" },
+ { "Kid",
+R"(Kid: Hi Santa! Are you real?
+Santa: Of course I am, my dear! Ho ho ho!
+Kid: Can you please bring me a new toy for Christmas?
+Santa: I'll see what I can do, but you have to make sure to be a good boy or girl and listen to your parents.
+Kid: I will, Santa! Thank you!
+Kid: Can you tell me how you deliver all the presents to all the kids in the world in one night?
+Santa: It's a secret, but I have a lot of help from my elves and my magical sleigh. And I have a special route that I follow to make sure I visit every child.
+Kid: Wow, that's amazing! Can I please have a ride in your sleigh sometime?
+)" },
+};
+
+int main(int argc, char ** argv) {
+ whisper_params params;
+
+ if (whisper_params_parse(argc, argv, params) == false) {
+ return 1;
+ }
+
+ if (whisper_lang_id(params.language.c_str()) == -1) {
+ fprintf(stderr, "error: unknown language '%s'\n", params.language.c_str());
+ whisper_print_usage(argc, argv, params);
+ exit(0);
+ }
+
+ // whisper init
+
+ struct whisper_context * ctx_wsp = whisper_init(params.model_wsp.c_str());
+
+ // gpt init
+
+ struct gpt2_context * ctx_gpt = gpt2_init(params.model_gpt.c_str());
+
+ // print some info about the processing
+ {
+ fprintf(stderr, "\n");
+ if (!whisper_is_multilingual(ctx_wsp)) {
+ if (params.language != "en" || params.translate) {
+ params.language = "en";
+ params.translate = false;
+ fprintf(stderr, "%s: WARNING: model is not multilingual, ignoring language and translation options\n", __func__);
+ }
+ }
+ fprintf(stderr, "%s: processing, %d threads, lang = %s, task = %s, timestamps = %d ...\n",
+ __func__,
+ params.n_threads,
+ params.language.c_str(),
+ params.translate ? "translate" : "transcribe",
+ params.no_timestamps ? 0 : 1);
+
+ fprintf(stderr, "\n");
+ }
+
+
+ // init audio
+
+ audio_async audio(30*1000);
+ if (!audio.init(params.capture_id, WHISPER_SAMPLE_RATE)) {
+ fprintf(stderr, "%s: audio.init() failed!\n", __func__);
+ return 1;
+ }
+
+ audio.resume();
+
+ int n_iter = 0;
+
+ bool is_running = true;
+ bool force_speak = params.person == "Kid";
+
+ float prob0 = 0.0f;
+ float prob = 0.0f;
+
+ std::vector<float> pcmf32_cur;
+ std::vector<float> pcmf32_prompt;
+
+ if (k_prompts.find(params.person) == k_prompts.end()) {
+ fprintf(stderr, "%s: unknown person '%s'\n", __func__, params.person.c_str());
+ return 1;
+ }
+
+ gpt2_set_prompt(ctx_gpt, k_prompts.at(params.person).c_str());
+
+ const std::string person_other = params.person == "Santa" ? "Kid" : "Santa";
+ const int voice_id = params.person == "Santa" ? 5 : 2;
+
+ fprintf(stderr, "gpt-2: prompt_base:\n");
+ fprintf(stderr, "========================\n\n");
+ fprintf(stderr, "%s\n", gpt2_get_prompt(ctx_gpt));
+ fprintf(stderr, "========================\n\n");
+
+ // main loop
+ while (is_running) {
+ // handle Ctrl + C
+ {
+ SDL_Event event;
+ while (SDL_PollEvent(&event)) {
+ switch (event.type) {
+ case SDL_QUIT:
+ {
+ is_running = false;
+ } break;
+ default:
+ break;
+ }
+ }
+
+ if (!is_running) {
+ break;
+ }
+ }
+
+ // delay
+ std::this_thread::sleep_for(std::chrono::milliseconds(100));
+
+ int64_t t_ms = 0;
+
+ {
+ audio.get(2000, pcmf32_cur);
+
+ if (vad_simple(pcmf32_cur, WHISPER_SAMPLE_RATE, 1250, params.vad_thold, params.freq_thold, params.print_energy) || force_speak) {
+ fprintf(stdout, "%s: Speech detected! Processing ...\n", __func__);
+
+ audio.get(params.voice_ms, pcmf32_cur);
+
+ std::string text_heard = "Hey little one, what do you want for Christmas?";
+ if (!force_speak) {
+ text_heard = ::trim(::transcribe(ctx_wsp, params, pcmf32_cur, prob0, t_ms));
+ }
+
+ force_speak = false;
+
+ // 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+$"), "");
+
+ const std::vector<gpt_vocab::id> tokens = gpt2_tokenize(ctx_gpt, text_heard.c_str());
+
+ if (text_heard.empty() || tokens.empty()) {
+ fprintf(stdout, "%s: Heard nothing, skipping ...\n", __func__);
+ audio.clear();
+
+ continue;
+ }
+
+ fprintf(stdout, "%s: Heard '%s%s%s', (t = %d ms)\n", __func__, "\033[1m", text_heard.c_str(), "\033[0m", (int) t_ms);
+
+ std::string prompt_base = gpt2_get_prompt(ctx_gpt);
+
+ std::string text_to_speak;
+
+ {
+ text_heard = person_other + ": " + text_heard;
+
+ text_to_speak = gpt2_gen_text(ctx_gpt, (prompt_base + text_heard + "\n").c_str(), params.max_tokens);
+ 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"));
+
+ // remove first 2 lines of base prompt
+ if (n_iter > 4) {
+ {
+ const size_t pos = prompt_base.find_first_of("\n");
+ if (pos != std::string::npos) {
+ prompt_base = prompt_base.substr(pos + 1);
+ }
+ }
+ {
+ const size_t pos = prompt_base.find_first_of("\n");
+ if (pos != std::string::npos) {
+ prompt_base = prompt_base.substr(pos + 1);
+ }
+ }
+ }
+
+ prompt_base += text_heard + "\n" + text_to_speak + "\n";
+ }
+
+ printf("%s\n", text_to_speak.c_str());
+
+ //printf("========================\n");
+ //printf("gpt-2: prompt_base:\n'%s'\n", prompt_base.c_str());
+ //printf("========================\n");
+
+ gpt2_set_prompt(ctx_gpt, prompt_base.c_str());
+
+ text_to_speak = ::replace(text_to_speak, params.person + ": ", "");
+ system((params.speak + " " + std::to_string(voice_id) + " \"" + text_to_speak + "\"").c_str());
+
+ audio.clear();
+
+ ++n_iter;
+ }
+ }
+ }
+
+ audio.pause();
+
+ whisper_print_timings(ctx_wsp);
+ whisper_free(ctx_wsp);
+
+ return 0;
+}
overview / pkg / ggml / sources / whisper.cpp / commitdiff