Glm 4.5

convert_hf_to_gguf.py

@@ -618,6 +618,12 @@
         if chkhsh == "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b":
             # ref: https://huggingface.co/THUDM/glm-4-9b-chat
             res = "chatglm-bpe"
+        if chkhsh == "a1336059768a55c99a734006ffb02203cd450fed003e9a71886c88acf24fdbc2":
+            # ref: https://huggingface.co/THUDM/glm-4-9b-hf
+            res = "glm4"
+        if chkhsh == "9ca2dd618e8afaf09731a7cf6e2105b373ba6a1821559f258b272fe83e6eb902":
+            # ref: https://huggingface.co/zai-org/GLM-4.5-Air, https://huggingface.co/zai-org/GLM-4.5
+            res = "gpt-2"
         if chkhsh == "7fc505bd3104ca1083b150b17d088b59534ede9bde81f0dd2090967d7fe52cee":
             # ref: https://huggingface.co/LumiOpen/Viking-7B
             res = "viking"
@@ -1869,20 +1875,20 @@
         tensors: list[tuple[str, Tensor]] = []
 
         if name.endswith("q_proj.weight"):
             tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid), weight_torch))
             tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid, suffix=".scale"), scale_torch))
         elif name.endswith("k_proj.weight"):
             tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid), weight_torch))
             tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid, suffix=".scale"), scale_torch))
         elif name.endswith("v_proj.weight"):
             tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid), weight_torch))
             tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid, suffix=".scale"), scale_torch))
         elif name.endswith("o_proj.weight"):
             tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_OUT, bid), weight_torch))
             tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_OUT, bid, suffix=".scale"), scale_torch))
         elif name.endswith("up_proj.weight"):
             tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, bid), weight_torch))
             tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, bid, suffix=".scale"), scale_torch))
         elif name.endswith("down_proj.weight"):
             tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.FFN_DOWN, bid), weight_torch))
             tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.FFN_DOWN, bid, suffix=".scale"), scale_torch))
@@ -3948,6 +3954,214 @@
             return [(self.map_tensor_name(name), data_torch)]
         return super().modify_tensors(data_torch, name, bid)
 
+@Model.register("Glm4MoeForCausalLM")
+class Glm4MoeModel(Model):
+    model_arch = gguf.MODEL_ARCH.GLM4_MOE
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        # GLM4_MOE has num_hidden_layers + 1 actual layers (including NextN layer)
+        self.block_count = self.hparams["num_hidden_layers"] + 1
+        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+
+    def set_vocab(self):
+        from transformers import AutoTokenizer
+
+        tokenizer = AutoTokenizer.from_pretrained(
+            self.dir_model, trust_remote_code=True
+        )
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
+        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)
+
+        # Set special tokens
+        special_vocab._set_special_token(
+            "eos", tokenizer.get_added_vocab()["<|endoftext|>"]
+        )
+        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"])
+        special_vocab._set_special_token(
+            "unk", tokenizer.get_added_vocab()["<|endoftext|>"]
+        )
+        special_vocab._set_special_token(
+            "bos", tokenizer.get_added_vocab()["<|endoftext|>"]
+        )
+
+        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))
+        )
+
+        # MoE parameters
+        if (n_experts := self.hparams.get("n_routed_experts")) is not None:
+            self.gguf_writer.add_expert_count(n_experts)
+        # Note: expert_used_count is already set by parent class using num_experts_per_tok
+        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
+            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
+        if (n_shared_experts := self.hparams.get("n_shared_experts")) is not None:
+            self.gguf_writer.add_expert_shared_count(n_shared_experts)
+        if (first_k_dense_replace := self.hparams.get("first_k_dense_replace")) is not None:
+            self.gguf_writer.add_leading_dense_block_count(first_k_dense_replace)
+
+        # Expert gating function (sigmoid for GLM4_MOE)
+        self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
+
+        # Routed scaling factor
+        if (routed_scaling_factor := self.hparams.get("routed_scaling_factor")) is not None:
+            self.gguf_writer.add_expert_weights_scale(routed_scaling_factor)
+
+        # Normalise topk probabilities
+        if (norm_topk_prob := self.hparams.get("norm_topk_prob")) is not None:
+            self.gguf_writer.add_expert_weights_norm(norm_topk_prob)
+
+    _experts: list[dict[str, Tensor]] | None = None
+    _shared_experts: list[dict[str, Tensor]] | None = None
+
+    def modify_tensors(
+        self, data_torch: Tensor, name: str, bid: int | None
+    ) -> Iterable[tuple[str, Tensor]]:
+        if name.startswith("model.visual."):  # ignore visual part
+            return []
+        elif name.startswith("model.language_model."):
+            name = name.replace("language_model.", "")  # for multimodal variants
+
+        # Handle main token embedding (but not layer-specific NextN embeddings)
+        if name == "model.embed_tokens.weight":
+            return [(self.map_tensor_name("token_embd.weight"), data_torch)]
+
+        # Handle routed experts
+        if name.find("mlp.experts") != -1 and "shared_experts" not in name:
+            n_experts = self.hparams["n_routed_experts"]
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            # Extend experts array if needed (for models where actual layers > num_hidden_layers)
+            while len(self._experts) <= bid:
+                self._experts.append({})
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                tensors: list[tuple[str, Tensor]] = []
+
+                # merge the experts into a single 3d tensor
+                for w_name in ["down_proj", "gate_proj", "up_proj"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+                    # Generate GGUF tensor names for merged experts
+                    if w_name == "down_proj":
+                        new_name = f"blk.{bid}.ffn_down_exps.weight"
+                    elif w_name == "gate_proj":
+                        new_name = f"blk.{bid}.ffn_gate_exps.weight"
+                    elif w_name == "up_proj":
+                        new_name = f"blk.{bid}.ffn_up_exps.weight"
+                    else:
+                        merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+                        new_name = self.map_tensor_name(merged_name)
+                    tensors.append((new_name, data_torch))
+                return tensors
+            else:
+                return []
+
+        # Handle expert gating input (routing gate)
+        if ".mlp.gate.e_score_correction_bias" in name:
+            new_name = name.replace("model.layers.", "blk.").replace(
+                ".mlp.gate.e_score_correction_bias", ".ffn_gate_inp.bias" # *NOTE* this is ".exp_probs_b" in mainline PR
+            )
+            return [(new_name, data_torch)]
+        elif ".mlp.gate.weight" in name:
+            new_name = name.replace("model.layers.", "blk.").replace(
+                ".mlp.gate.weight", ".ffn_gate_inp.weight"
+            )
+            return [(new_name, data_torch)]
+
+        # Handle shared expert tensors
+        if ".mlp.shared_experts." in name:
+            new_name = name.replace("model.layers.", "blk.").replace(".mlp.shared_experts.", ".ffn_")
+            if "gate_proj" in new_name:
+                new_name = new_name.replace("gate_proj", "gate_shexp")
+            elif "down_proj" in new_name:
+                new_name = new_name.replace("down_proj", "down_shexp")
+            elif "up_proj" in new_name:
+                new_name = new_name.replace("up_proj", "up_shexp")
+            return [(new_name, data_torch)]
+
+        # Handle regular dense FFN layers (for hybrid dense/MoE architecture)
+        if ".mlp." in name and "experts" not in name and "_shexp" not in name:
+            if "gate_proj" in name:
+                new_name = name.replace("model.layers.", "blk.").replace(
+                    ".mlp.gate_proj.weight", ".ffn_gate.weight"
+                )
+            elif "up_proj" in name:
+                new_name = name.replace("model.layers.", "blk.").replace(
+                    ".mlp.up_proj.weight", ".ffn_up.weight"
+                )
+            elif "down_proj" in name:
+                new_name = name.replace("model.layers.", "blk.").replace(
+                    ".mlp.down_proj.weight", ".ffn_down.weight"
+                )
+            else:
+                new_name = name
+            return [(self.map_tensor_name(new_name), data_torch)]
+
+        # Handle special NextN tensors - preserve for future MTP support - See https://github.com/ggml-org/llama.cpp/pull/13236
+        if (
+            ".embed_tokens." in name
+            or ".shared_head." in name
+            or ".eh_proj." in name
+            or ".enorm." in name
+            or ".hnorm." in name
+        ):
+            new_name = name.replace("model.layers.", "blk.").replace("model.", "").replace(".weight", "")
+            # logger.debug(f"Skipping MTP tensor: {new_name}")
+            return [(new_name, data_torch)]
+
+        # GLM tensor mapping - handle directly without map_tensor_name
+        if ".input_layernorm." in name:
+            new_name = name.replace("model.layers.", "blk.").replace(".input_layernorm.", ".attn_norm.")
+            return [(new_name, data_torch)]
+        elif ".post_attention_layernorm." in name:
+            new_name = name.replace("model.layers.", "blk.").replace(".post_attention_layernorm.", ".ffn_norm.")
+            return [(new_name, data_torch)]
+        elif ".self_attn." in name:
+            # Map GLM self_attn to standard attention naming
+            new_name = name.replace("model.layers.", "blk.").replace(".self_attn.", ".attn_")
+            if "q_proj" in new_name:
+                new_name = new_name.replace("q_proj", "q")
+            elif "k_proj" in new_name:
+                new_name = new_name.replace("k_proj", "k")
+            elif "v_proj" in new_name:
+                new_name = new_name.replace("v_proj", "v")
+            elif "o_proj" in new_name:
+                new_name = new_name.replace("o_proj", "output")
+            return [(new_name, data_torch)]
+
+        return super().modify_tensors(data_torch, name, bid)
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
 
 @Model.register("ChatGLMModel", "ChatGLMForConditionalGeneration")
 class ChatGLMModel(Model):

gguf-py/gguf/constants.py

@@ -220,6 +220,7 @@ class MODEL_ARCH(IntEnum):
     OPENELM      = auto()
     ARCTIC       = auto()
     DEEPSEEK2    = auto()
+    GLM4_MOE     = auto()
     CHATGLM      = auto()
     BITNET       = auto()
     BITNET_25    = auto()
@@ -262,6 +263,9 @@ class MODEL_TENSOR(IntEnum):
     FFN_GATE_EXP         = auto()
     FFN_DOWN_EXP         = auto()
     FFN_UP_EXP           = auto()
+    FFN_GATE_EXPS        = auto()  # merged experts
+    FFN_DOWN_EXPS        = auto()  # merged experts
+    FFN_UP_EXPS          = auto()  # merged experts
     FFN_GATE_SHEXP       = auto()
     FFN_DOWN_SHEXP       = auto()
     FFN_UP_SHEXP         = auto()
@@ -314,6 +318,12 @@ class MODEL_TENSOR(IntEnum):
     ENC_FFN_DOWN         = auto()
     ENC_FFN_UP           = auto()
     ENC_OUTPUT_NORM      = auto()
+    NEXTN_EH_PROJ        = auto()   # nextn tensors (glm4moe)
+    NEXTN_EMBED_TOKENS   = auto()   # nextn tensors (glm4moe)
+    NEXTN_ENORM          = auto()   # nextn tensors (glm4moe)
+    NEXTN_HNORM          = auto()   # nextn tensors (glm4moe)
+    NEXTN_SHARED_HEAD_HEAD = auto() # nextn tensors (glm4moe)
+    NEXTN_SHARED_HEAD_NORM = auto() # nextn tensors (glm4moe)
 
 
 MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
@@ -358,6 +368,7 @@ class MODEL_TENSOR(IntEnum):
     MODEL_ARCH.ARCTIC:         "arctic",
     MODEL_ARCH.DEEPSEEK2:      "deepseek2",
     MODEL_ARCH.CHATGLM:        "chatglm",
+    MODEL_ARCH.GLM4_MOE:         "glm4moe",
     MODEL_ARCH.BITNET:         "bitnet",
     MODEL_ARCH.BITNET_25:      "bitnet-25",
     MODEL_ARCH.T5:             "t5",
@@ -404,6 +415,9 @@ class MODEL_TENSOR(IntEnum):
     MODEL_TENSOR.FFN_GATE_EXP:         "blk.{bid}.ffn_gate_exps",
     MODEL_TENSOR.FFN_DOWN_EXP:         "blk.{bid}.ffn_down_exps",
     MODEL_TENSOR.FFN_UP_EXP:           "blk.{bid}.ffn_up_exps",
+    MODEL_TENSOR.FFN_GATE_EXPS:        "blk.{bid}.ffn_gate_exps",  # merged experts
+    MODEL_TENSOR.FFN_DOWN_EXPS:        "blk.{bid}.ffn_down_exps",  # merged experts
+    MODEL_TENSOR.FFN_UP_EXPS:          "blk.{bid}.ffn_up_exps",    # merged experts
     MODEL_TENSOR.FFN_EXP_PROBS_B:      "blk.{bid}.exp_probs_b",
     MODEL_TENSOR.LAYER_OUT_NORM:       "blk.{bid}.layer_output_norm",
     MODEL_TENSOR.SSM_IN:               "blk.{bid}.ssm_in",
@@ -451,6 +465,13 @@ class MODEL_TENSOR(IntEnum):
     MODEL_TENSOR.ENC_FFN_DOWN:         "enc.blk.{bid}.ffn_down",
     MODEL_TENSOR.ENC_FFN_UP:           "enc.blk.{bid}.ffn_up",
     MODEL_TENSOR.ENC_OUTPUT_NORM:      "enc.output_norm",
+    # NextN/MTP tensors (GLM4_MOE)
+    MODEL_TENSOR.NEXTN_EH_PROJ:             "blk.{bid}.eh_proj",
+    MODEL_TENSOR.NEXTN_EMBED_TOKENS:        "blk.{bid}.embed_tokens",
+    MODEL_TENSOR.NEXTN_ENORM:               "blk.{bid}.enorm",
+    MODEL_TENSOR.NEXTN_HNORM:               "blk.{bid}.hnorm",
+    MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD:    "blk.{bid}.shared_head.head",
+    MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM:    "blk.{bid}.shared_head.norm",
 }
 
 MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
@@ -1070,6 +1091,36 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
     ],
+    MODEL_ARCH.GLM4_MOE: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_Q_NORM,
+        MODEL_TENSOR.ATTN_K_NORM,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,          # dense layers
+        MODEL_TENSOR.FFN_DOWN,          # dense layers
+        MODEL_TENSOR.FFN_UP,            # dense layers
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_GATE_EXPS,
+        MODEL_TENSOR.FFN_DOWN_EXPS,
+        MODEL_TENSOR.FFN_UP_EXPS,
+        MODEL_TENSOR.FFN_GATE_SHEXP,
+        MODEL_TENSOR.FFN_DOWN_SHEXP,
+        MODEL_TENSOR.FFN_UP_SHEXP,
+        # NextN/MTP tensors - preserved but unused
+        MODEL_TENSOR.NEXTN_EH_PROJ,
+        MODEL_TENSOR.NEXTN_EMBED_TOKENS,
+        MODEL_TENSOR.NEXTN_ENORM,
+        MODEL_TENSOR.NEXTN_HNORM,
+        MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD,
+        MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM,
+    ],
     MODEL_ARCH.BITNET: [
         MODEL_TENSOR.ATTN_Q,
         MODEL_TENSOR.ATTN_K,