Update bruteforce test:

- Compare tokenizer vocab tokens.
- Bruteforce byte token generator.
- Find minimal mismatched substring.

tests/test-tokenizer-random.py

@@ -116,9 +116,25 @@ class LibLlamaModel:
             num = self.lib.llama_detokenize(self.model, self.token_ids, len(ids), self.text_buff, len(self.text_buff), remove_special, unparse_special)
         return str(cast(Buffer, self.ffi.buffer(self.text_buff, num)), encoding="utf-8", errors="replace")  # replace errors with '\uFFFD'
 
+    def get_vocab(self, detokenize=False) -> list[str]:
+        vocab: list[str] = []
+        num_tokens = self.lib.llama_n_vocab(self.model)
+        for id in range(num_tokens):
+            if detokenize:
+                text = self.detokenize([id], remove_special=False, unparse_special=True)
+            else:
+                text = self.lib.llama_token_get_text(self.model, id)
+                text = self.ffi.string(text)
+                text = str(text, encoding="utf-8", errors="replace")  # replace errors with '\uFFFD'
+            vocab.append(text)
+        return vocab
+
 
 class Tokenizer:
 
+    def get_vocab(self, detokenize=False) -> list[str]:
+        raise NotImplementedError
+
     def encode(self, text: str) -> list[int]:
         raise NotImplementedError
 
@@ -129,7 +145,7 @@ class Tokenizer:
 class TokenizerGroundtruth (Tokenizer):
 
     def __init__(self, dir_tokenizer: str):
-        self.model: PreTrainedTokenizer = AutoTokenizer.from_pretrained(dir_tokenizer)
+        self.model: PreTrainedTokenizer = AutoTokenizer.from_pretrained(dir_tokenizer, trust_remote_code=False)
         # guess BOS and EOS
         ids = self.encode("a")
         assert 1 <= len(ids) <= 3
@@ -138,15 +154,24 @@ class TokenizerGroundtruth (Tokenizer):
         self.add_bos_token = getattr(self.model, "add_bos_token", add_bos_token)
         self.add_eos_token = getattr(self.model, "add_eos_token", add_eos_token)
         # build vocab
-        tokens = list(self.model.get_vocab().values())
+        self.vocab = self.get_vocab(detokenize=True)
-        self.vocab = self.model.batch_decode(tokens, skip_special_tokens=True)
-        self.vocab = list(sorted(self.vocab))
         # tokens and lists
-        self.special_tokens = list(self.model.all_special_tokens)
+        self.special_tokens = [self.vocab[i] for i in sorted(self.model.all_special_ids)]
-        self.added_tokens   = self.model.batch_decode(self.model.added_tokens_encoder.values(), skip_special_tokens=False)
+        self.added_tokens   = [self.vocab[i] for i in sorted(self.model.added_tokens_encoder.values())]
         self.bos_token = self.model.bos_token
         self.eos_token = self.model.eos_token
 
+    def get_vocab(self, detokenize=False) -> list[str]:
+        max_token_id = max(self.model.get_vocab().values())
+        if detokenize:
+            ids = list(range(max_token_id + 1))
+            vocab = self.model.batch_decode(ids, skip_special_tokens=False)
+        else:
+            vocab = [None] * (max_token_id + 1)
+            for text, id in self.model.get_vocab().items():
+                vocab[id] = text
+        return vocab
+
     def encode(self, text: str) -> list[int]:
         return self.model.encode(text, add_special_tokens=True)
 
@@ -163,6 +188,9 @@ class TokenizerLlamaCpp (Tokenizer):
             self.libllama = LibLlama()
         self.model = LibLlamaModel(self.libllama, vocab_file, mparams=dict(vocab_only=True), cparams=dict(n_ctx=4096))
 
+    def get_vocab(self, detokenize=False) -> list[str]:
+        return self.model.get_vocab(detokenize)
+
     def encode(self, text: str) -> list[int]:
         return self.model.tokenize(text, add_special=True, parse_special=True)
 
@@ -253,6 +281,23 @@ def generator_vocab_words(tokenizer: TokenizerGroundtruth) -> Iterator[str]:
     yield from tokenizer.vocab
 
 
+def generator_byte_tokens() -> Iterator[str]:
+    """Brute force check common byte encoding"""
+    for a, b in ["<>", "[]", "()", ("\\", "")]:
+        yield from [f"{a}{i}{b}" for i in range(256)]
+        yield from [f"{a}{i:x}{b}" for i in range(256)]
+        yield from [f"{a}{i:X}{b}" for i in range(256)]
+        yield from [f"{a}x{i:x}{b}" for i in range(256)]
+        yield from [f"{a}x{i:X}{b}" for i in range(256)]
+        yield from [f"{a}x{i:02x}{b}" for i in range(256)]
+        yield from [f"{a}x{i:02X}{b}" for i in range(256)]
+        yield from [f"{a}0x{i:x}{b}" for i in range(256)]
+        yield from [f"{a}0x{i:X}{b}" for i in range(256)]
+        yield from [f"{a}0x{i:02x}{b}" for i in range(256)]
+        yield from [f"{a}0x{i:02X}{b}" for i in range(256)]
+        yield from [f"{a}{chr(i)}{b}" for i in range(256)]
+
+
 def generator_ascii_lr_strip() -> Iterator[str]:
     WHITESPACES = ["", " ", "  "]
     CHARACTERS = list(chr(i) for i in range(1, 0x80)) + [""]
@@ -275,10 +320,11 @@ def generator_apostrophe() -> Iterator[str]:
                     yield char1 + lstrip + "'" + rstrip + char2
                     yield char1 + char2 + lstrip + "'" + rstrip + "z"
                     yield "a" + lstrip + "'" + rstrip + char1 + char2
+                    yield "a" + lstrip + "'" + char1 + char2 + rstrip + "z"
 
 
 def generator_added_lr_strip(tokenizer: TokenizerGroundtruth) -> Iterator[str]:
-    WHITESPACES = ["", " ", "  ", "\n", "\r\n", "\n\n", "\t", "\t\t"]
+    WHITESPACES = ["", " ", "  ", "\n", "\r\n", "\n\n", "\t", "\t\t", "        "]
     all_tokens = list(sorted(set(tokenizer.special_tokens + tokenizer.added_tokens)))
     for token in all_tokens:
         for lstrip in WHITESPACES:
@@ -436,6 +482,7 @@ def compare_tokenizers(tokenizer1: TokenizerGroundtruth, tokenizer2: TokenizerLl
     t_start = time.perf_counter()
     encode_errors = 0
     decode_errors = 0
+    total_tests = 0
     MAX_ERRORS = 10
 
     logger.info("%s: %s" % (generator.__qualname__, "ini"))
@@ -455,21 +502,44 @@ def compare_tokenizers(tokenizer1: TokenizerGroundtruth, tokenizer2: TokenizerLl
         t_encode2 += t2 - t1
         t_decode1 += t3 - t2
         t_decode2 += t4 - t3
-        if encode_errors < MAX_ERRORS and ids1 != ids2:
+        # compare
-            i = find_first_mismatch(ids1, ids2)
+        encode_ok = ids1 == ids2
-            ids1 = list(ids1)[max(0, i - 2) : i + 5 + 1]
+        decode_ok = check_detokenizer(text, text1, text2)
-            ids2 = list(ids2)[max(0, i - 2) : i + 5 + 1]
+        encode_errors += not encode_ok
+        decode_errors += not decode_ok
+        total_tests += 1
+        if (encode_errors < MAX_ERRORS and not encode_ok) or (decode_errors < MAX_ERRORS and not decode_ok):
+            def _compare(text: str):
+                ids1  = tokenizer1.encode(text)
+                ids2  = tokenizer2.encode(text)
+                text1 = tokenizer1.decode(ids1)
+                text2 = tokenizer2.decode(ids1)
+                encode_ok = ids1 == ids2
+                decode_ok = check_detokenizer(text, text1, text2)
+                ok = encode_ok and decode_ok
+                return ok, ids1, ids2, text1, text2
+            a, b = 0, len(text)
+            for step in [64, 32, 16, 8, 4, 2, 1]:
+                while a < b:
+                    t = max(a, b - step)
+                    if _compare(text[a : t])[0]:
+                        break
+                    b = t
+            for step in [64, 32, 16, 8, 4, 2, 1]:
+                while a < b:
+                    t = min(a + step, b)
+                    if _compare(text[t : b])[0]:
+                        break
+                    a = t
+            ok, ids1, ids2, text1, text2 = _compare(text[a : b])
+            assert a <= b and not ok
+            logger.error(" Text:" + repr(text[a : b]))
+            logger.error("  " + " ".join(repr(x) + ":" + hex(ord(x)) for x in text[a : b]))
             logger.error(" Expected: " + str(ids1))
             logger.error("   Result: " + str(ids2))
-            encode_errors += 1
+            logger.error(" Expected: " + " ".join(repr(x) + ":" + hex(ord(x)) for x in text1))
+            logger.error("   Result: " + " ".join(repr(x) + ":" + hex(ord(x)) for x in text2))
             logger.error(f" {encode_errors=}")
-        if decode_errors < MAX_ERRORS and not check_detokenizer(text, text1, text2):
-            i = find_first_mismatch(text1, text2)
-            text1 = list(text1[max(0, i - 2) : i + 5 + 1])
-            text2 = list(text2[max(0, i - 2) : i + 5 + 1])
-            logger.error(" Expected: " + " ".join(hex(ord(x)) for x in text1))
-            logger.error("   Result: " + " ".join(hex(ord(x)) for x in text2))
-            decode_errors += 1
             logger.error(f" {decode_errors=}")
         if encode_errors >= MAX_ERRORS and decode_errors >= MAX_ERRORS:
             logger.error(f" EXIT: {encode_errors=} {decode_errors=}")
@@ -480,6 +550,43 @@ def compare_tokenizers(tokenizer1: TokenizerGroundtruth, tokenizer2: TokenizerLl
     logger.info(f"{generator.__qualname__}: end,  {t_encode1=:.3f} {t_encode2=:.3f}  {t_decode1=:.3f} {t_decode2=:.3f}  {t_total=:.3f}")
 
 
+def compare_vocabs(tokenizer1: TokenizerGroundtruth, tokenizer2: TokenizerLlamaCpp):
+
+    MAX_PRINT_ERRORS = 10
+
+    logger.info("compare_vocabs: ini")
+
+    t_start = time.perf_counter()
+
+    for detokenize in (False, True):
+        vocab1 = tokenizer1.get_vocab(detokenize)
+        vocab2 = tokenizer2.get_vocab(detokenize)
+        if vocab1 != vocab2:
+            num_errors = 0
+            for i in range(max(len(vocab1), len(vocab2))):
+                text1 = vocab1[i] if i < len(vocab1) else None
+                text2 = vocab2[i] if i < len(vocab2) else None
+                if text1 != text2:
+                    # is "[UNUSED_TOKEN_" and "[PAD" valid for all models ?  #TODO: use toktypes
+                    if text1 is not None:
+                        text1 = text1.replace("[UNUSED_TOKEN_", "[PAD")
+                    if text2 is not None:
+                        text2 = text2.replace("[UNUSED_TOKEN_", "[PAD")
+                    if text1 is None and (text2 or "").startswith('[PAD'):
+                        text2 = None
+                    if text2 is None and (text1 or "").startswith('[PAD'):
+                        text1 = None
+                if text1 != text2:
+                    num_errors += 1
+                    if num_errors < MAX_PRINT_ERRORS:
+                        logger.error(f" {detokenize=} id={i} expected={repr(text1)} result={repr(text2)}")
+            if num_errors:
+                logger.error(f" {num_errors=}")
+
+    t_total = time.perf_counter() - t_start
+    logger.info(f"compare_vocabs: end,  {t_total=:.3f}")
+
+
 def main(argv: list[str] | None = None):
     parser = argparse.ArgumentParser()
     parser.add_argument("vocab_file", type=str, help="path to vocab 'gguf' file")
@@ -493,18 +600,21 @@ def main(argv: list[str] | None = None):
     tokenizer1 = TokenizerGroundtruth(args.dir_tokenizer)
     tokenizer2 = TokenizerLlamaCpp(args.vocab_file)
 
-    # compare_tokenizers(tokenizer1, tokenizer2, generator_custom_text())
+    compare_vocabs(tokenizer1, tokenizer2)
-    # compare_tokenizers(tokenizer1, tokenizer2, generator_custom_text_edge_cases())
+
+    compare_tokenizers(tokenizer1, tokenizer2, generator_custom_text())
+    compare_tokenizers(tokenizer1, tokenizer2, generator_custom_text_edge_cases())
+    compare_tokenizers(tokenizer1, tokenizer2, generator_byte_tokens())
     compare_tokenizers(tokenizer1, tokenizer2, generator_ascii_lr_strip())
     compare_tokenizers(tokenizer1, tokenizer2, generator_apostrophe())
     compare_tokenizers(tokenizer1, tokenizer2, generator_unicodes())
     compare_tokenizers(tokenizer1, tokenizer2, generator_vocab_words(tokenizer1))
     compare_tokenizers(tokenizer1, tokenizer2, generator_added_lr_strip(tokenizer1))
-    # compare_tokenizers(tokenizer1, tokenizer2, generator_random_added_tokens(tokenizer1, 10_000))
+    compare_tokenizers(tokenizer1, tokenizer2, generator_random_added_tokens(tokenizer1, 10_000))
-    # compare_tokenizers(tokenizer1, tokenizer2, generator_random_chars(10_000))
+    compare_tokenizers(tokenizer1, tokenizer2, generator_random_chars(10_000))
-    # compare_tokenizers(tokenizer1, tokenizer2, generator_random_unicodes(10_000))
+    compare_tokenizers(tokenizer1, tokenizer2, generator_random_unicodes(10_000))
-    # compare_tokenizers(tokenizer1, tokenizer2, generator_random_vocab_chars(tokenizer1, 10_000))
+    compare_tokenizers(tokenizer1, tokenizer2, generator_random_vocab_chars(tokenizer1, 10_000))
-    # compare_tokenizers(tokenizer1, tokenizer2, generator_random_vocab_words(tokenizer1, 5_000))
+    compare_tokenizers(tokenizer1, tokenizer2, generator_random_vocab_words(tokenizer1, 5_000))
 
     tokenizer2.model.free()