Compare vocabs

tests/test-tokenizer-random.py

@@ -112,9 +112,25 @@ 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(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
 
@@ -125,7 +141,7 @@ class Tokenizer:
 class TokenizerGroundtruth (Tokenizer):
 
     def __init__(self, dir_tokenizer: str):
-        self.model = AutoTokenizer.from_pretrained(dir_tokenizer)
+        self.model = AutoTokenizer.from_pretrained(dir_tokenizer, trust_remote_code=False)
         # guess BOS and EOS
         ids = self.encode("a")
         assert 1 <= len(ids) <= 3
@@ -134,15 +150,24 @@ 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.added_tokens   = list(self.model.added_tokens_encoder)
         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)
 
@@ -159,6 +184,9 @@ 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)
 
@@ -491,6 +519,34 @@ def compare_tokenizers(tokenizer1: TokenizerGroundtruth, tokenizer2: TokenizerLl
     logger.info(f"{generator.__name__}: 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 ""
+                text2 = vocab2[i] if i < len(vocab2) else ""
+                is_unused = text1.startswith("[UNUSED_TOKEN_")  # AutoTokenizer adds more unused tokens than SentencePiece ?
+                if text1 != text2 and is_unused and 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):
     parser = argparse.ArgumentParser()
     parser.add_argument("vocab_file", help="path to vocab 'gguf' file")
@@ -504,13 +560,16 @@ def main(argv: list[str] = 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_ascii_lr_strip())
+    compare_tokenizers(tokenizer1, tokenizer2, generator_custom_text())
-    compare_tokenizers(tokenizer1, tokenizer2, generator_apostrophe())
+    compare_tokenizers(tokenizer1, tokenizer2, generator_custom_text_edge_cases())
-    compare_tokenizers(tokenizer1, tokenizer2, generator_unicodes())
+    # compare_tokenizers(tokenizer1, tokenizer2, generator_representative(tokenizer1))
-    compare_tokenizers(tokenizer1, tokenizer2, generator_vocab_words(tokenizer1))
+    # compare_tokenizers(tokenizer1, tokenizer2, generator_ascii_lr_strip())
-    compare_tokenizers(tokenizer1, tokenizer2, generator_added_lr_strip(tokenizer1))
+    # 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_chars(10_000))
     # compare_tokenizers(tokenizer1, tokenizer2, generator_random_unicodes(10_000))