Repository: ggerganov/llama.cpp
File: convert_hf_to_gguf.py (Python)

Code before:
        tokens, toktypes, tokpre = self.get_vocab_base()
        self.gguf_writer.add_tokenizer_model("gpt2")
        self.gguf_writer.add_tokenizer_pre(tokpre)
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_types(toktypes)
        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
        special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["
"])
        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["
"])
        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["
"])
        special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["
"])
        special_vocab.add_to_gguf(self.gguf_writer)

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        rope_dim = self.hparams["head_dim"]
        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5)))
        rope_scaling = self.hparams.get("rope_scaling") or {}
        if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling:
            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
            self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
            self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"])


@ModelBase.register("GlmForCausalLM", "ChatGLMModel", "ChatGLMForConditionalGeneration")
class ChatGLMModel(TextModel):
    model_arch = gguf.MODEL_ARCH.CHATGLM

    def set_vocab_chatglm3(self):
        dir_model = self.dir_model
        hparams = self.hparams
        tokens: list[bytes] = []
        toktypes: list[int] = []
        scores: list[float] = []

        from transformers import AutoTokenizer
        tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
        vocab_size = hparams.get("padded_vocab_size", len(tokenizer.get_vocab()))
        assert max(tokenizer.get_vocab().values()) < vocab_size
        role_special_tokens = ["
", "
", "
", "
"]
        special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "sop", "eop"] + role_special_tokens
        for token_id in range(vocab_size):
            piece = tokenizer._convert_id_to_token(token_id)
            if token_id == 0:
                piece = "<unk>"
            elif token_id == 1:
                piece = "<bos>"
            elif token_id == 2:
                piece = "<eos>"

            text = piece.encode("utf-8")
            score = 0.0

Review comment (suggestion):
```suggestion
        if name.startswith("model.visual."):
            return []
        elif name.startswith("model.language_model."):
            name = name.replace("language_model.", "") # for Glm4v
```
With this change you can remove all the changes in `tensor_mapping.py`.

Code after:
        tokens, toktypes, tokpre = self.get_vocab_base()
        self.gguf_writer.add_tokenizer_model("gpt2")
        self.gguf_writer.add_tokenizer_pre(tokpre)
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_types(toktypes)
        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
        special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["
"])
        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["
"])
        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["
"])
        special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["
"])
        special_vocab.add_to_gguf(self.gguf_writer)

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        if (rope_dim := self.hparams.get("head_dim")) is None:
            rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5)))
        rope_scaling = self.hparams.get("rope_scaling") or {}
        if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling:
            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
            self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
            self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"])

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if name.startswith("model.visual."): # ignore visual part of Glm4v
            return []
        elif name.startswith("model.language_model."):
            name = name.replace("language_model.", "") # for Glm4v
        return super().modify_tensors(data_torch, name, bid)


@ModelBase.register("GlmForCausalLM", "ChatGLMModel", "ChatGLMForConditionalGeneration")
class ChatGLMModel(TextModel):
    model_arch = gguf.MODEL_ARCH.CHATGLM

    def set_vocab_chatglm3(self):
        dir_model = self.dir_model
        hparams = self.hparams
        tokens: list[bytes] = []
        toktypes: list[int] = []
        scores: list[float] = []

        from transformers import AutoTokenizer
        tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
        vocab_size = hparams.get("padded_vocab_size", len(tokenizer.get_vocab()))
        assert max(tokenizer.get_vocab().values()) < vocab_size
        role_special_tokens = ["
", "
", "
", "
"]
        special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "sop", "eop"] + role_special_tokens
        for token_id in range(vocab_size):
            piece = tokenizer._convert_id_to_token(token_id)
            if token_id == 0: