sentencepiece bpe compatible tokenizer

Makefile

@@ -31,7 +31,7 @@ endif
 #
 
 CFLAGS   = -I.              -O3 -DNDEBUG -std=c11   -fPIC
-CXXFLAGS = -I. -I./examples -O3 -DNDEBUG -std=c++11 -fPIC
+CXXFLAGS = -I. -I./examples -O3 -DNDEBUG -std=c++17 -fPIC
 LDFLAGS  =
 
 # OS specific

README.md

@@ -11,6 +11,9 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
 - Cache input prompts for faster initialization: https://github.com/ggerganov/llama.cpp/issues/64
 - Create a `llama.cpp` logo: https://github.com/ggerganov/llama.cpp/issues/105
 
+**TEMPORARY NOTICE:**
+If you're updating to the latest master, you will need to regenerate your model files as the format has changed.
+
 ## Description
 
 The main goal is to run the model using 4-bit quantization on a MacBook

convert-pth-to-ggml.py

@@ -60,7 +60,8 @@ def write_header(fout, hparams, ftype):
 
     keys = ["vocab_size", "dim", "multiple_of", "n_heads", "n_layers"]
     values = [
-        0x67676d6c,  # magic: ggml in hex
+        0x67676d66,  # magic: ggml in hex
+        1, # file version
         *[hparams[key] for key in keys],
         hparams["dim"] // hparams["n_heads"],  # rot (obsolete)
         ftype
@@ -85,6 +86,7 @@ def write_tokens(fout, tokenizer):
             text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode("utf-8")
         fout.write(struct.pack("i", len(text)))
         fout.write(text)
+        fout.write(struct.pack("f", tokenizer.get_score(i)))
 
 def process_and_write_variables(fout, model, ftype):
 

main.cpp

@@ -3,6 +3,7 @@
 #include "utils.h"
 
 #include <cassert>
+#include <cinttypes>
 #include <cmath>
 #include <cstdio>
 #include <cstring>
@@ -105,10 +106,24 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
     {
         uint32_t magic;
         fin.read((char *) &magic, sizeof(magic));
-        if (magic != 0x67676d6c) {
+        if (magic == 0x67676d6c) {
+            fprintf(stderr, "%s: invalid model file '%s' (too old, regenerate your model files!)\n",
+                    __func__, fname.c_str());
+            return false;
+        }
+        if (magic != 0x67676d66) {
             fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
             return false;
         }
+
+        uint32_t format_version;
+        fin.read((char *) &format_version, sizeof(format_version));
+
+        if (format_version != 1) {
+            fprintf(stderr, "%s: invalid model file '%s' (unsupported format version %" PRIu32 ")\n",
+                    __func__, fname.c_str(), format_version);
+            return false;
+        }
     }
 
     int n_ff = 0;
@@ -154,8 +169,12 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
             word.resize(len);
             fin.read((char *) word.data(), len);
 
+            float score;
+            fin.read((char *) &score, sizeof(score));
+
             vocab.token_to_id[word] = i;
             vocab.id_to_token[i] = word;
+            vocab.score[i] = score;
 
             //if (i < 30000) {
             //    fprintf(stderr, "%s: vocab[%d] = '%s'\n", __func__, i, word.c_str());

quantize.cpp

@@ -3,6 +3,7 @@
 #include "utils.h"
 
 #include <cassert>
+#include <cinttypes>
 #include <cmath>
 #include <cstdio>
 #include <cstring>
@@ -63,12 +64,28 @@ bool llama_model_quantize(const std::string & fname_inp, const std::string & fna
     {
         uint32_t magic;
         finp.read((char *) &magic, sizeof(magic));
-        if (magic != 0x67676d6c) {
+        if (magic == 0x67676d6c) {
+            fprintf(stderr, "%s: invalid model file '%s' (too old, regenerate your model files!)\n",
+                    __func__, fname_inp.c_str());
+            return false;
+        }
+        if (magic != 0x67676d66) {
             fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname_inp.c_str());
             return false;
         }
 
         fout.write((char *) &magic, sizeof(magic));
+
+        uint32_t format_version;
+        finp.read((char *) &format_version, sizeof(format_version));
+
+        if (format_version != 1) {
+            fprintf(stderr, "%s: invalid model file '%s' (unsupported format version %" PRIu32 ")\n",
+                    __func__, fname_inp.c_str(), format_version);
+            return false;
+        }
+
+        fout.write((char *) &format_version, sizeof(format_version));
     }
 
     llama_hparams hparams;
@@ -122,8 +139,13 @@ bool llama_model_quantize(const std::string & fname_inp, const std::string & fna
             finp.read ((char *) word.data(), len);
             fout.write((char *) word.data(), len);
 
+            float score;
+            finp.read ((char *) &score, sizeof(score));
+            fout.write((char *) &score, sizeof(score));
+
             vocab.token_to_id[word] = i;
             vocab.id_to_token[i] = word;
+            vocab.score[i] = score;
         }
     }
 

utils.cpp

@@ -6,6 +6,7 @@
 #include <regex>
 #include <iostream>
 #include <iterator>
+#include <queue>
 #include <string>
 #include <math.h>
 
@@ -294,58 +295,146 @@ std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::stri
     return tokens;
 }
 
-// TODO: Calculate this constant from the vocabulary
-#define MAX_TOKEN_LEN 18
-// SentencePiece implementation after https://guillaume-be.github.io/2020-05-30/sentence_piece
-std::vector<gpt_vocab::id> llama_tokenize(const gpt_vocab & vocab, const std::string & text, bool bos) {
-    std::vector<gpt_vocab::id> res;
-    std::vector<int> score;
-    std::vector<gpt_vocab::id> prev;
-    int len = text.length();
-
-    score.resize(len + 1);
-    prev.resize(len + 1);
-
-    // Forward pass
-    for (int i = 0; i < len; i++) {
-        int max_len = std::min(len - i, MAX_TOKEN_LEN);
-        for (int sub_len = 1; sub_len <= max_len; sub_len++) {
-            auto sub = text.substr(i, sub_len);
-            auto token = vocab.token_to_id.find(sub);
-            if (token != vocab.token_to_id.end()) {
-                int token_score = sub.length() * sub.length();
-                int local_score = score[i] + token_score;
-                int next = i + sub_len;
-                if (score[next] < local_score) {
-                    score[next] = local_score;
-                    prev[next] = (*token).second;
+static size_t utf8_len(char src) {
+    const size_t lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 };
+    uint8_t highbits = static_cast<uint8_t>(src) >> 4;
+    return lookup[highbits];
+}
+
+struct llama_sp_symbol {
+    using index = int;
+    index prev;
+    index next;
+    std::string_view text;
+};
+
+struct llama_sp_bigram {
+    struct comparator {
+        bool operator()(llama_sp_bigram & l, llama_sp_bigram & r) {
+            return (l.score < r.score) || (l.score == r.score && l.left > r.left);
+        }
+    };
+    using queue_storage = std::vector<llama_sp_bigram>;
+    using queue = std::priority_queue<llama_sp_bigram, queue_storage, comparator>;
+    llama_sp_symbol::index left;
+    llama_sp_symbol::index right;
+    float score;
+    size_t size;
+};
+
+struct llama_tokenizer {
+    llama_tokenizer(const gpt_vocab & vocab): vocab_(vocab) {}
+
+    void tokenize(std::string_view text, std::vector<gpt_vocab::id> & output) {
+        // split string into utf8 chars
+        int index = 0;
+        while (!text.empty()) {
+            llama_sp_symbol sym;
+            size_t char_len = std::min(text.size(), utf8_len(text.data()[0]));
+            sym.text = std::string_view(text.data(), char_len);
+            sym.prev = index - 1;
+            text.remove_prefix(char_len);
+            sym.next = text.empty() ? -1 : index + 1;
+            index++;
+            symbols_.emplace_back(std::move(sym));
+        }
+
+        // seed the work queue with all possible 2-character tokens.
+        for (size_t i = 1; i < symbols_.size(); ++i) {
+            try_add_bigram(i - 1, i);
+        }
+
+        // keep substituting the highest frequency pairs for as long as we can.
+        while (!work_queue_.empty()) {
+            auto bigram = work_queue_.top();
+            work_queue_.pop();
+
+            auto & left_sym = symbols_[bigram.left];
+            auto & right_sym = symbols_[bigram.right];
+
+            // if one of the symbols already got merged, skip it.
+            if (left_sym.text.empty() || right_sym.text.empty() ||
+                left_sym.text.size() + right_sym.text.size() != bigram.size) {
+                continue;
+            }
+
+            // merge the right sym into the left one
+            left_sym.text = std::string_view(left_sym.text.data(), left_sym.text.size() + right_sym.text.size());
+            right_sym.text = std::string_view("");
+
+            // remove the right sym from the chain
+            left_sym.next = right_sym.next;
+            if (right_sym.next >= 0) {
+                symbols_[right_sym.next].prev = bigram.left;
+            }
+
+            // find more substitutions
+            try_add_bigram(left_sym.prev, bigram.left);
+            try_add_bigram(bigram.left, left_sym.next);
+        }
+
+        for (int i = 0; i != -1; i = symbols_[i].next) {
+            auto& symbol = symbols_[i];
+            auto token = vocab_.token_to_id.find(std::string(symbol.text));
+
+            if (token == vocab_.token_to_id.end()) {
+                // output any symbols that did not form tokens as bytes.
+                for (int j = 0; j < symbol.text.size(); ++j) {
+                    gpt_vocab::id token_id = static_cast<uint8_t>(symbol.text[j]) + 3;
+                    output.push_back(token_id);
                 }
+            } else {
+                output.push_back((*token).second);
             }
         }
     }
 
-    // Backward pass
-    int i = len;
-    while (i > 0) {
-        gpt_vocab::id token_id = prev[i];
-        if (token_id == 0) {
-	    // TODO: Return error or something more meaningful
-            printf("failed to tokenize string!\n");
-	    break;
+private:
+    void try_add_bigram(int left, int right) {
+        if (left == -1 || right == -1) {
+            return;
+        }
+
+        std::string_view text(symbols_[left].text.data(), symbols_[left].text.size() + symbols_[right].text.size());
+        auto token = vocab_.token_to_id.find(std::string(text));
+
+        if (token == vocab_.token_to_id.end()) {
+            return;
         }
-        res.push_back(token_id);
-        auto token = (*vocab.id_to_token.find(token_id)).second;
-        i -= token.length();
+
+        auto score = vocab_.score.find((*token).second);
+
+        if (score == vocab_.score.end()) {
+            return;
+        }
+
+        llama_sp_bigram bigram;
+        bigram.left = left;
+        bigram.right = right;
+        bigram.score = (*score).second;
+        bigram.size = text.size();
+        work_queue_.push(bigram);
     }
 
-    if (bos) {
-        res.push_back(1); // TODO: replace with vocab.bos
+    const gpt_vocab & vocab_;
+    std::vector<llama_sp_symbol> symbols_;
+    llama_sp_bigram::queue work_queue_;
+};
+
+std::vector<gpt_vocab::id> llama_tokenize(const gpt_vocab & vocab, std::string_view text, bool bos) {
+    llama_tokenizer tokenizer(vocab);
+    std::vector<gpt_vocab::id> output;
+
+    if (text.size() == 0) {
+        return output;
     }
 
-    // Pieces are in reverse order so correct that
-    std::reverse(res.begin(), res.end());
+    if (bos) {
+        output.push_back(1);
+    }
 
-    return res;
+    tokenizer.tokenize(text, output);
+    return output;
 }
 
 bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab) {

utils.h

@@ -58,6 +58,7 @@ struct gpt_vocab {
 
     std::map<token, id> token_to_id;
     std::map<id, token> id_to_token;
+    std::map<id, float> score;
 };
 
 void replace(std::string & str, const std::string & needle, const std::string & replacement);
@@ -79,7 +80,7 @@ std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::stri
 
 // TODO: this is probably wrong, but I cannot figure out how this tokenizer works ..
 // ref: https://github.com/google/sentencepiece
-std::vector<gpt_vocab::id> llama_tokenize(const gpt_vocab & vocab, const std::string & text, bool bos);
+std::vector<gpt_vocab::id> llama_tokenize(const gpt_vocab & vocab, std::string_view text, bool bos);
 
 // load the tokens from encoder.json
 bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab);