fixed other arch

otherarch/gpt2_v1.cpp

@@ -624,148 +624,3 @@ bool legacy_gpt2_eval(
 
     return true;
 }
-
-// int main(int argc, char ** argv) {
-//     ggml_v1_time_init();
-//     const int64_t t_main_start_us = ggml_v1_time_us();
-
-//     gpt_params params;
-//     params.model = "models/gpt-2-117M/ggml-model.bin";
-
-//     if (utils_gpt_params_parse(argc, argv, params) == false) {
-//         return 1;
-//     }
-
-//     if (params.seed < 0) {
-//         params.seed = time(NULL);
-//     }
-
-//     printf("%s: seed = %d\n", __func__, params.seed);
-
-//     std::mt19937 rng(params.seed);
-//     if (params.prompt.empty()) {
-//         if( !isatty(STDIN_FILENO) ){
-//             std::string line;
-//             while( std::getline(std::cin, line) ){
-//                 params.prompt = params.prompt + "\n" + line;
-//             }
-//         } else {
-//             params.prompt = utils_gpt_random_prompt(rng);
-//         }
-//     }
-
-//     int64_t t_load_us = 0;
-
-//     gpt_vocab vocab;
-//     gpt2_v1_model model;
-
-//     // load the model
-//     {
-//         const int64_t t_start_us = ggml_v1_time_us();
-
-//         if (!legacy_gpt2_model_load(params.model, model, vocab, FileFormat::GPT2_1)) {
-//             fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
-//             return 1;
-//         }
-
-//         t_load_us = ggml_v1_time_us() - t_start_us;
-//     }
-
-//     int n_past = 0;
-
-//     int64_t t_sample_us  = 0;
-//     int64_t t_predict_us = 0;
-
-//     std::vector<float> logits;
-
-//     // tokenize the prompt
-//     std::vector<gpt_vocab::id> embd_inp = ::gpt_tokenize(vocab, params.prompt);
-
-//     params.n_predict = std::min(params.n_predict, model.hparams.n_ctx - (int) embd_inp.size());
-
-//     printf("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
-//     printf("\n");
-
-//     // submit the input prompt token-by-token
-//     // this reduces the memory usage during inference, at the cost of a bit of speed at the beginning
-//     std::vector<gpt_vocab::id> embd;
-
-//     // determine the required inference memory per token:
-//     size_t mem_per_token = 0;
-//     legacy_gpt2_eval(model, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token, FileFormat::GPT2_1);
-
-//     for (int i = embd.size(); i < embd_inp.size() + params.n_predict; i++) {
-//         // predict
-//         if (embd.size() > 0) {
-//             const int64_t t_start_us = ggml_v1_time_us();
-
-//             if (!legacy_gpt2_eval(model, params.n_threads, n_past, embd, logits, mem_per_token, FileFormat::GPT2_1)) {
-//                 printf("Failed to predict\n");
-//                 return 1;
-//             }
-
-//             t_predict_us += ggml_v1_time_us() - t_start_us;
-//         }
-
-//         n_past += embd.size();
-//         embd.clear();
-
-//         if (i >= embd_inp.size()) {
-//             // sample next token
-//             const int   top_k = params.top_k;
-//             const float top_p = params.top_p;
-//             const float temp  = params.temp;
-
-//             const int n_vocab = model.hparams.n_vocab;
-
-//             gpt_vocab::id id = 0;
-
-//             {
-//                 const int64_t t_start_sample_us = ggml_v1_time_us();
-
-//                 id = gpt_sample_top_k_top_p(vocab, logits.data() + (logits.size() - n_vocab), top_k, top_p, temp, rng);
-
-//                 t_sample_us += ggml_v1_time_us() - t_start_sample_us;
-//             }
-
-//             // add it to the context
-//             embd.push_back(id);
-//         } else {
-//             // if here, it means we are still processing the input prompt
-//             for (int k = i; k < embd_inp.size(); k++) {
-//                 embd.push_back(embd_inp[k]);
-//                 if (embd.size() >= params.n_batch) {
-//                     break;
-//                 }
-//             }
-//             i += embd.size() - 1;
-//         }
-
-//         // display text
-//         for (auto id : embd) {
-//             printf("%s", vocab.id_to_token[id].c_str());
-//         }
-//         fflush(stdout);
-
-//         // end of text token
-//         if (embd.back() == 50256) {
-//             break;
-//         }
-//     }
-
-//     // report timing
-//     {
-//         const int64_t t_main_end_us = ggml_v1_time_us();
-
-//         printf("\n\n");
-//         printf("%s: mem per token = %8zu bytes\n", __func__, mem_per_token);
-//         printf("%s:     load time = %8.2f ms\n", __func__, t_load_us/1000.0f);
-//         printf("%s:   sample time = %8.2f ms\n", __func__, t_sample_us/1000.0f);
-//         printf("%s:  predict time = %8.2f ms / %.2f ms per token\n", __func__, t_predict_us/1000.0f, t_predict_us/1000.0f/n_past);
-//         printf("%s:    total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);
-//     }
-
-//     ggml_v1_free(model.ctx);
-
-//     return 0;
-// }

otherarch/gpt2_v2.cpp

@@ -72,12 +72,15 @@ ModelLoadResult gpt2_v2_model_load(const std::string & fname, gpt2_v2_model & mo
         }
 
         std::string word;
+        std::vector<char> buf(128);
+
         for (int i = 0; i < n_vocab; i++) {
             uint32_t len;
             fin.read((char *) &len, sizeof(len));
 
-            word.resize(len);
+            buf.resize(len);
-            fin.read((char *) word.data(), len);
+            fin.read((char *) buf.data(), len);
+            word.assign(buf.data(), len);
 
             vocab.token_to_id[word] = i;
             vocab.id_to_token[i] = word;

otherarch/gpt2_v3.cpp

@@ -75,12 +75,15 @@ ModelLoadResult gpt2_model_load(const std::string & fname, gpt2_model & model, g
         }
 
         std::string word;
+        std::vector<char> buf(128);
+
         for (int i = 0; i < n_vocab; i++) {
             uint32_t len;
             fin.read((char *) &len, sizeof(len));
 
-            word.resize(len);
+            buf.resize(len);
-            fin.read((char *) word.data(), len);
+            fin.read((char *) buf.data(), len);
+            word.assign(buf.data(), len);
 
             vocab.token_to_id[word] = i;
             vocab.id_to_token[i] = word;

otherarch/gptj_v2.cpp

@@ -75,12 +75,15 @@ ModelLoadResult gptj_v2_model_load(const std::string & fname, gptj_v2_model & mo
         }
 
         std::string word;
+        std::vector<char> buf(128);
+
         for (int i = 0; i < n_vocab; i++) {
             uint32_t len;
             fin.read((char *) &len, sizeof(len));
 
-            word.resize(len);
+            buf.resize(len);
-            fin.read((char *) word.data(), len);
+            fin.read((char *) buf.data(), len);
+            word.assign(buf.data(), len);
 
             vocab.token_to_id[word] = i;
             vocab.id_to_token[i] = word;

otherarch/gptj_v3.cpp

@@ -75,12 +75,15 @@ ModelLoadResult gptj_model_load(const std::string & fname, gptj_model & model, g
         }
 
         std::string word;
+        std::vector<char> buf(128);
+
         for (int i = 0; i < n_vocab; i++) {
             uint32_t len;
             fin.read((char *) &len, sizeof(len));
 
-            word.resize(len);
+            buf.resize(len);
-            fin.read((char *) word.data(), len);
+            fin.read((char *) buf.data(), len);
+            word.assign(buf.data(), len);
 
             vocab.token_to_id[word] = i;
             vocab.id_to_token[i] = word;

otherarch/neox_v2.cpp

@@ -75,12 +75,15 @@ ModelLoadResult gpt_neox_v2_model_load(const std::string & fname, gpt_neox_v2_mo
         const int32_t n_vocab = model.hparams.n_vocab;
 
         std::string word;
+        std::vector<char> buf(128);
+
         for (int i = 0; i < n_vocab; i++) {
             uint32_t len;
             fin.read((char *) &len, sizeof(len));
 
-            word.resize(len);
+            buf.resize(len);
-            fin.read((char *) word.data(), len);
+            fin.read((char *) buf.data(), len);
+            word.assign(buf.data(), len);
 
             vocab.token_to_id[word] = i;
             vocab.id_to_token[i] = word;

otherarch/neox_v3.cpp

@@ -75,18 +75,22 @@ ModelLoadResult gpt_neox_model_load(const std::string & fname, gpt_neox_model &
         const int32_t n_vocab = model.hparams.n_vocab;
 
         std::string word;
+        std::vector<char> buf(128);
+
         for (int i = 0; i < n_vocab; i++) {
             uint32_t len;
             fin.read((char *) &len, sizeof(len));
 
-            word.resize(len);
+            buf.resize(len);
-            fin.read((char *) word.data(), len);
+            fin.read((char *) buf.data(), len);
+            word.assign(buf.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 or quantized
     // in order to save memory and also to speed up the computation
     ggml_type wtype = ggml_ftype_to_ggml_type((ggml_ftype) (model.hparams.ftype));

otherarch/utils.cpp

@@ -3,78 +3,7 @@
 #include <fstream>
 #include <regex>
 
-bool utils_gpt_params_parse(int argc, char ** argv, gpt_params & params) {
-    for (int i = 1; i < argc; i++) {
-        std::string arg = argv[i];
 
-        if (arg == "-s" || arg == "--seed") {
-            params.seed = std::stoi(argv[++i]);
-        } else if (arg == "-t" || arg == "--threads") {
-            params.n_threads = std::stoi(argv[++i]);
-        } else if (arg == "-p" || arg == "--prompt") {
-            params.prompt = argv[++i];
-        } else if (arg == "-n" || arg == "--n_predict") {
-            params.n_predict = std::stoi(argv[++i]);
-        } else if (arg == "--top_k") {
-            params.top_k = std::stoi(argv[++i]);
-        } else if (arg == "--top_p") {
-            params.top_p = std::stof(argv[++i]);
-        } else if (arg == "--temp") {
-            params.temp = std::stof(argv[++i]);
-        } else if (arg == "-b" || arg == "--batch_size") {
-            params.n_batch = std::stoi(argv[++i]);
-        } else if (arg == "-m" || arg == "--model") {
-            params.model = argv[++i];
-        } else if (arg == "-h" || arg == "--help") {
-            utils_gpt_print_usage(argc, argv, params);
-            exit(0);
-        } else {
-            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
-            utils_gpt_print_usage(argc, argv, params);
-            exit(0);
-        }
-    }
-
-    return true;
-}
-
-void utils_gpt_print_usage(int argc, char ** argv, const gpt_params & params) {
-    fprintf(stderr, "usage: %s [options]\n", argv[0]);
-    fprintf(stderr, "\n");
-    fprintf(stderr, "options:\n");
-    fprintf(stderr, "  -h, --help            show this help message and exit\n");
-    fprintf(stderr, "  -s SEED, --seed SEED  RNG seed (default: -1)\n");
-    fprintf(stderr, "  -t N, --threads N     number of threads to use during computation (default: %d)\n", params.n_threads);
-    fprintf(stderr, "  -p PROMPT, --prompt PROMPT\n");
-    fprintf(stderr, "                        prompt to start generation with (default: random)\n");
-    fprintf(stderr, "  -n N, --n_predict N   number of tokens to predict (default: %d)\n", params.n_predict);
-    fprintf(stderr, "  --top_k N             top-k sampling (default: %d)\n", params.top_k);
-    fprintf(stderr, "  --top_p N             top-p sampling (default: %.1f)\n", params.top_p);
-    fprintf(stderr, "  --temp N              temperature (default: %.1f)\n", params.temp);
-    fprintf(stderr, "  -b N, --batch_size N  batch size for prompt processing (default: %d)\n", params.n_batch);
-    fprintf(stderr, "  -m FNAME, --model FNAME\n");
-    fprintf(stderr, "                        model path (default: %s)\n", params.model.c_str());
-    fprintf(stderr, "\n");
-}
-
-std::string utils_gpt_random_prompt(std::mt19937 & rng) {
-    const int r = rng() % 10;
-    switch (r) {
-        case 0: return "So";
-        case 1: return "Once upon a time";
-        case 2: return "When";
-        case 3: return "The";
-        case 4: return "After";
-        case 5: return "If";
-        case 6: return "import";
-        case 7: return "He";
-        case 8: return "She";
-        case 9: return "They";
-        default: return "To";
-    }
-
-    return "The";
-}
 
 void utreplace(std::string & str, const std::string & needle, const std::string & replacement) {
     size_t pos = 0;
@@ -175,6 +104,31 @@ std::map<std::string, int32_t> json_parse(const std::string & fname) {
     return result;
 }
 
+
+void gpt_vocab::add_special_token(const std::string & token) {
+    special_tokens.push_back(token);
+}
+
+static void append_utf8(char32_t ch, std::string & out) {
+    if (ch <= 0x7F) {
+        out.push_back(static_cast<unsigned char>(ch));
+    } else if (ch <= 0x7FF) {
+        out.push_back(static_cast<unsigned char>(0xC0 | ((ch >> 6) & 0x1F)));
+        out.push_back(static_cast<unsigned char>(0x80 | (ch & 0x3F)));
+    } else if (ch <= 0xFFFF) {
+        out.push_back(static_cast<unsigned char>(0xE0 | ((ch >> 12) & 0x0F)));
+        out.push_back(static_cast<unsigned char>(0x80 | ((ch >> 6) & 0x3F)));
+        out.push_back(static_cast<unsigned char>(0x80 | (ch & 0x3F)));
+    } else if (ch <= 0x10FFFF) {
+        out.push_back(static_cast<unsigned char>(0xF0 | ((ch >> 18) & 0x07)));
+        out.push_back(static_cast<unsigned char>(0x80 | ((ch >> 12) & 0x3F)));
+        out.push_back(static_cast<unsigned char>(0x80 | ((ch >> 6) & 0x3F)));
+        out.push_back(static_cast<unsigned char>(0x80 | (ch & 0x3F)));
+    } else {
+        printf("Invalid Unicode code point\n");
+    }
+}
+
 std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::string & text) {
     std::vector<std::string> words;
 
@@ -208,7 +162,8 @@ std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::stri
                 if (it != vocab.token_to_id.end()) {
                     tokens.push_back(it->second);
                     i = j;
-                    break;
+                    j = n;
+                    continue;
                 }
                 --j;
             }
@@ -230,202 +185,6 @@ std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::stri
     return tokens;
 }
 
-bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab) {
-    printf("%s: loading vocab from '%s'\n", __func__, fname.c_str());
-
-    vocab.token_to_id = ::json_parse(fname);
-
-    for (const auto & kv : vocab.token_to_id) {
-        vocab.id_to_token[kv.second] = kv.first;
-    }
-
-    printf("%s: vocab size = %d\n", __func__, (int) vocab.token_to_id.size());
-
-    // print the vocabulary
-    //for (auto kv : vocab.token_to_id) {
-    //    printf("'%s' -> %d\n", kv.first.data(), kv.second);
-    //}
-
-    return true;
-}
-
-void gptj_sample_top_k(std::vector<std::pair<double, gpt_vocab::id>> & logits_id, int top_k) {
-    // 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);
-}
-
-gpt_vocab::id gptj_sample_top_p_top_k(
-        const gpt_vocab & vocab,
-        const float * logits,
-        std::vector<gpt_vocab::id> & last_n_tokens,
-        double repeat_penalty,
-        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);
-
-    {
-        const double scale = 1.0/temp;
-        for (int i = 0; i < n_logits; ++i) {
-            // repetition penalty from CTRL paper (https://arxiv.org/abs/1909.05858)
-            // credit https://github.com/facebookresearch/llama/compare/main...shawwn:llama:main
-            if (std::find(last_n_tokens.begin(), last_n_tokens.end(), i) != last_n_tokens.end()) {
-                // if score < 0 then repetition penalty has to multiplied to reduce the previous token probability
-                if (logits[i] < 0.0) {
-                    logits_id.push_back(std::make_pair(logits[i]*scale*repeat_penalty, i));
-                } else {
-                    logits_id.push_back(std::make_pair(logits[i]*scale/repeat_penalty, i));
-                }                
-            } else {
-                logits_id.push_back(std::make_pair(logits[i]*scale, i));
-            }
-        }
-    }
-
-    gptj_sample_top_k(logits_id, top_k > 0 ? std::min(top_k, n_logits) : n_logits);
-
-    double maxl = -INFINITY;
-    for (const auto & kv : logits_id) {
-        maxl = std::max(maxl, kv.first);
-    }
-
-    // compute probs for the top K tokens
-    std::vector<double> probs;
-    probs.reserve(logits_id.size());
-
-    double sum = 0.0;
-    for (const auto & kv : logits_id) {
-        double p = exp(kv.first - maxl);
-        probs.push_back(p);
-        sum += p;
-    }
-
-    // normalize the probs
-    for (auto & p : probs) {
-        p /= sum;
-    }
-
-    if (top_p < 1.0f) {
-        double cumsum = 0.0f;
-        for (int i = 0; i < (int) probs.size(); i++) {
-            cumsum += probs[i];
-            if (cumsum >= top_p) {
-                probs.resize(i + 1);
-                logits_id.resize(i + 1);
-                break;
-            }
-        }
-
-        cumsum = 1.0/cumsum;
-        for (int i = 0; i < (int) probs.size(); i++) {
-            probs[i] *= cumsum;
-        }
-    }
-
-    //printf("\n");
-    //for (int i = 0; i < (int) 10; i++) {
-    //    printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), probs[i]);
-    //}
-    //printf("\n\n");
-    //exit(0);
-
-    std::discrete_distribution<> dist(probs.begin(), probs.end());
-    int idx = dist(rng);
-
-    return logits_id[idx].second;
-}
-
-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);
-
-    {
-        const double scale = 1.0/temp;
-        for (int i = 0; i < n_logits; ++i) {
-            logits_id.push_back(std::make_pair(logits[i]*scale, 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);
-
-    double maxl = -INFINITY;
-    for (const auto & kv : logits_id) {
-        maxl = std::max(maxl, kv.first);
-    }
-
-    // compute probs for the top K tokens
-    std::vector<double> probs;
-    probs.reserve(logits_id.size());
-
-    double sum = 0.0;
-    for (const auto & kv : logits_id) {
-        double p = exp(kv.first - maxl);
-        probs.push_back(p);
-        sum += p;
-    }
-
-    // normalize the probs
-    for (auto & p : probs) {
-        p /= sum;
-    }
-
-    if (top_p < 1.0f) {
-        double cumsum = 0.0f;
-        for (int i = 0; i < top_k; i++) {
-            cumsum += probs[i];
-            if (cumsum >= top_p) {
-                top_k = i + 1;
-                probs.resize(top_k);
-                logits_id.resize(top_k);
-                break;
-            }
-        }
-
-        cumsum = 1.0/cumsum;
-        for (int i = 0; i < (int) probs.size(); i++) {
-            probs[i] *= cumsum;
-        }
-    }
-
-    //printf("\n");
-    //for (int i = 0; i < (int) probs.size(); i++) {
-    //    printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), probs[i]);
-    //}
-    //exit(0);
-
-    std::discrete_distribution<> dist(probs.begin(), probs.end());
-    int idx = dist(rng);
-
-    return logits_id[idx].second;
-}
-
 bool should_transpose_layer(std::string name)
 {
        

otherarch/utils.h

@@ -24,6 +24,9 @@ struct gpt_vocab {
 
     std::map<token, id> token_to_id;
     std::map<id, token> id_to_token;
+    std::vector<std::string> special_tokens;
+
+    void add_special_token(const std::string & token);
 };
 
 void utreplace(std::string & str, const std::string & needle, const std::string & replacement);
@@ -43,37 +46,6 @@ std::map<std::string, int32_t> json_parse(const std::string & fname);
 //
 std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::string & text);
 
-// load the tokens from encoder.json
-bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab);
 
-// sample next token given probabilities for each embedding
-//
-//   - consider only the top K tokens
-//   - from them, consider only the top tokens with cumulative probability > P
-//
-// TODO: not sure if this implementation is correct
-// TODO: temperature is not implemented
-//
-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);
-
-gpt_vocab::id gptj_sample_top_p_top_k(
-        const gpt_vocab & vocab,
-        const float * logits,
-        std::vector<gpt_vocab::id> & last_n_tokens,
-        double repeat_penalty,
-        int top_k,
-        double top_p,
-        double temp,
-        std::mt19937 & rng);
-
-bool utils_gpt_params_parse(int argc, char ** argv, gpt_params & params);
-void utils_gpt_print_usage(int argc, char ** argv, const gpt_params & params);
-std::string utils_gpt_random_prompt(std::mt19937 & rng);
 
 bool should_transpose_layer(std::string name);