model: convert-hf-to-gguf.py remove tiktoken

convert-hf-to-gguf.py

@@ -393,66 +393,6 @@ class Model(ABC):
         special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
         special_vocab.add_to_gguf(self.gguf_writer)
 
-    def _set_vocab_tiktoken(self):
-        # https://github.com/openai/tiktoken
-        dir_model = self.dir_model
-        hparams = self.hparams
-        tokens: list[str] = []
-        toktypes: list[int] = []
-
-        from transformers import AutoTokenizer
-        tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
-        vocab_size = hparams["vocab_size"]
-        assert max(tokenizer.get_vocab().values()) < vocab_size
-        vocab = {}
-        merges = []
-
-        # FIXME REVIEW should we extract this from QwenModel to base Model class ?
-        mergeable_ranks = tokenizer.encoding._mergeable_ranks
-        for token, rank in mergeable_ranks.items():
-            vocab[QwenModel.token_bytes_to_string(token)] = rank
-            if len(token) == 1:
-                continue
-            merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
-            assert len(merged) == 2
-            merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged)))
-
-        # for this kind of tokenizer, added_vocab is not a subset of vocab, so they need to be combined
-        added_vocab = tokenizer.get_added_vocab()
-        reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in ({**vocab, **added_vocab}).items()}
-
-        for i in range(vocab_size):
-            if i not in reverse_vocab:
-                tokens.append(f"[PAD{i}]")
-                toktypes.append(gguf.TokenType.USER_DEFINED)
-            elif reverse_vocab[i] in added_vocab:
-                tokens.append(reverse_vocab[i])
-                if tokenizer.added_tokens_decoder[i].special:
-                    toktypes.append(gguf.TokenType.CONTROL)
-                else:
-                    toktypes.append(gguf.TokenType.USER_DEFINED)
-            else:
-                tokens.append(reverse_vocab[i])
-                toktypes.append(gguf.TokenType.NORMAL)
-
-        self.gguf_writer.add_tokenizer_model("gpt2")
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_types(toktypes)
-
-        special_vocab = gguf.SpecialVocab(dir_model, load_merges=False)
-        special_vocab.chat_template = tokenizer.default_chat_template
-        special_vocab.merges = merges
-        tk_endoftext = tokenizer.encoding._special_tokens["<|endoftext|>"]
-
-        # only add special tokens when they were not already loaded from config.json
-        if len(special_vocab.special_token_ids) == 0:
-            special_vocab._set_special_token("bos", tk_endoftext)
-            special_vocab._set_special_token("eos", tk_endoftext)
-        # this one is usually not in config.json anyway
-        special_vocab._set_special_token("unk", tk_endoftext)
-
-        special_vocab.add_to_gguf(self.gguf_writer)
-
 
 @Model.register("GPTNeoXForCausalLM")
 class GPTNeoXModel(Model):
@@ -1582,10 +1522,6 @@ class DbrxModel(Model):
 
             self.gguf_writer.add_tensor(new_name, data)
 
-    def set_vocab(self):
-        self._set_vocab_tiktoken()
-
-
 @Model.register("MiniCPMForCausalLM")
 class MiniCPMModel(Model):
     model_arch = gguf.MODEL_ARCH.MINICPM

requirements/requirements-convert-hf-to-gguf.txt

@@ -1,4 +1,3 @@
 -r ./requirements-convert.txt
 torch~=2.1.1
 einops~=0.7.0
-tiktoken~=0.6.0