diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 9303a0476..a215f4ed7 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -678,6 +678,9 @@ class TextModel(ModelBase): 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 + res = "glm4" if chkhsh == "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35": # ref: https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0 res = "minerva-7b" @@ -6696,6 +6699,139 @@ class Glm4Model(TextModel): return super().modify_tensors(data_torch, name, bid) +@ModelBase.register("Glm4MoeForCausalLM") +class Glm4MoeModel(TextModel): + 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"] + self.hparams.get("num_nextn_predict_layers", 0) + 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) + 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) + + # Special tokens + # Note: Using <|endoftext|> (151329) for eot causes endless generation + special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["[gMASK]"]) # 151331 + special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"]) # 151336 + special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"]) # 151329 + special_vocab._set_special_token("eom", tokenizer.get_added_vocab()["<|observation|>"]) # 151338 + + # Patch broken chat template + if isinstance(special_vocab.chat_template, str) and "visible_text(m.content).endswith" in special_vocab.chat_template: + special_vocab.chat_template = special_vocab.chat_template.replace( + """{{ visible_text(m.content) }}\n{{- '/nothink' if (enable_thinking is defined and not enable_thinking and not visible_text(m.content).endswith("/nothink")) else '' -}}""", + """{% set content = visible_text(m.content) %}{{ content }}\n{{- '/nothink' if (enable_thinking is defined and not enable_thinking and not content.endswith("/nothink")) else '' -}}""") + + 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 - Use only routed expert count (shared experts handled separately) + if (n_routed_experts := self.hparams.get("n_routed_experts")) is not None: + self.gguf_writer.add_expert_count(n_routed_experts) + 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) + + # NextN/MTP prediction layers + if (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None: + self.gguf_writer.add_nextn_predict_layers(num_nextn_predict_layers) + + _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" and ".layers." not in name: + return [(self.map_tensor_name("token_embd.weight"), data_torch)] + + # Handle routed experts + if name.find("mlp.experts") != -1: + 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)] + + 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) + + 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 [] + + if name.endswith("e_score_correction_bias"): + name = name.replace("e_score_correction_bias", "e_score_correction.bias") + + new_name = self.map_tensor_name(name) + + return [(new_name, data_torch)] + + 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}") + + @ModelBase.register("GlmForCausalLM", "ChatGLMModel", "ChatGLMForConditionalGeneration") class ChatGLMModel(TextModel): model_arch = gguf.MODEL_ARCH.CHATGLM diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py index 226805f1e..575e05e19 100755 --- a/convert_hf_to_gguf_update.py +++ b/convert_hf_to_gguf_update.py @@ -147,6 +147,7 @@ pre_computed_hashes = [ {"name": "chatglm-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-chat", "chkhsh": "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b"}, {"name": "chatglm-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-chat", "chkhsh": "81d72c7348a9f0ebe86f23298d37debe0a5e71149e29bd283904c02262b27516"}, {"name": "glm4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-hf", "chkhsh": "a1336059768a55c99a734006ffb02203cd450fed003e9a71886c88acf24fdbc2"}, + {"name": "glm4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/zai-org/GLM-4.5-Air", "chkhsh": "9ca2dd618e8afaf09731a7cf6e2105b373ba6a1821559f258b272fe83e6eb902"}, {"name": "minerva-7b", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0", "chkhsh": "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35"}, {"name": "hunyuan", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Hunyuan-A13B-Instruct", "chkhsh": "7e57df22b1fe23a7b1e1c7f3dc4e3f96d43a4eb0836d0c6bdc3436d7b2f1c664"}, {"name": "hunyuan-dense", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Hunyuan-4B-Instruct", "chkhsh": "bba3b3366b646dbdded5dbc42d59598b849371afc42f7beafa914afaa5b70aa6"}, diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 5707085cb..e2d81dd98 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -105,6 +105,7 @@ class Keys: EXPERT_WEIGHTS_NORM = "{arch}.expert_weights_norm" EXPERT_GATING_FUNC = "{arch}.expert_gating_func" MOE_EVERY_N_LAYERS = "{arch}.moe_every_n_layers" + NEXTN_PREDICT_LAYERS = "{arch}.nextn_predict_layers" POOLING_TYPE = "{arch}.pooling_type" LOGIT_SCALE = "{arch}.logit_scale" DECODER_START_TOKEN_ID = "{arch}.decoder_start_token_id" @@ -357,6 +358,7 @@ class MODEL_ARCH(IntEnum): DEEPSEEK2 = auto() CHATGLM = auto() GLM4 = auto() + GLM4_MOE = auto() BITNET = auto() T5 = auto() T5ENCODER = auto() @@ -614,6 +616,13 @@ class MODEL_TENSOR(IntEnum): A_MMPROJ_FC = auto() A_MM_NORM_PRE = auto() A_MM_NORM_MID = auto() + # nextn/mtp + NEXTN_EH_PROJ = auto() + NEXTN_EMBED_TOKENS = auto() + NEXTN_ENORM = auto() + NEXTN_HNORM = auto() + NEXTN_SHARED_HEAD_HEAD = auto() + NEXTN_SHARED_HEAD_NORM = auto() MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { @@ -678,6 +687,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.DEEPSEEK2: "deepseek2", MODEL_ARCH.CHATGLM: "chatglm", MODEL_ARCH.GLM4: "glm4", + MODEL_ARCH.GLM4_MOE: "glm4moe", MODEL_ARCH.BITNET: "bitnet", MODEL_ARCH.T5: "t5", MODEL_ARCH.T5ENCODER: "t5encoder", @@ -936,6 +946,13 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = { MODEL_TENSOR.A_MMPROJ_FC: "mm.a.fc", MODEL_TENSOR.A_MM_NORM_PRE: "mm.a.norm_pre", MODEL_TENSOR.A_MM_NORM_MID: "mm.a.norm_mid", + # NextN/MTP + MODEL_TENSOR.NEXTN_EH_PROJ: "blk.{bid}.nextn.eh_proj", + MODEL_TENSOR.NEXTN_EMBED_TOKENS: "blk.{bid}.nextn.embed_tokens", + MODEL_TENSOR.NEXTN_ENORM: "blk.{bid}.nextn.enorm", + MODEL_TENSOR.NEXTN_HNORM: "blk.{bid}.nextn.hnorm", + MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD: "blk.{bid}.nextn.shared_head_head", + MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM: "blk.{bid}.nextn.shared_head_norm", } MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { @@ -2124,6 +2141,37 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.ATTN_POST_NORM, MODEL_TENSOR.FFN_POST_NORM, ], + MODEL_ARCH.GLM4_MOE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_POST_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_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + MODEL_TENSOR.FFN_EXP_PROBS_B, + # 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, diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index f4fd64ad8..89249021b 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -753,6 +753,9 @@ class GGUFWriter: def add_moe_every_n_layers(self, value: int) -> None: self.add_uint32(Keys.LLM.MOE_EVERY_N_LAYERS.format(arch=self.arch), value) + def add_nextn_predict_layers(self, count: int) -> None: + self.add_uint32(Keys.LLM.NEXTN_PREDICT_LAYERS.format(arch=self.arch), count) + def add_swin_norm(self, value: bool) -> None: self.add_bool(Keys.LLM.SWIN_NORM.format(arch=self.arch), value) diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index e6efc93fa..dd4f3d52e 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -1369,6 +1369,31 @@ class TensorNameMap: MODEL_TENSOR.A_MM_NORM_MID: ( "audio.multi_modal_projector.ln_mid", # ultravox ), + + # NextN/MTP tensors for GLM4_MOE + MODEL_TENSOR.NEXTN_EH_PROJ: ( + "model.layers.{bid}.eh_proj", + ), + + MODEL_TENSOR.NEXTN_EMBED_TOKENS: ( + "model.layers.{bid}.embed_tokens", + ), + + MODEL_TENSOR.NEXTN_ENORM: ( + "model.layers.{bid}.enorm", + ), + + MODEL_TENSOR.NEXTN_HNORM: ( + "model.layers.{bid}.hnorm", + ), + + MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD: ( + "model.layers.{bid}.shared_head.head", + ), + + MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM: ( + "model.layers.{bid}.shared_head.norm", + ), } # architecture-specific block mappings diff --git a/models/templates/README.md b/models/templates/README.md index 35b6386dd..2e8eaa595 100644 --- a/models/templates/README.md +++ b/models/templates/README.md @@ -21,4 +21,5 @@ These templates can be updated with the following commands: ./scripts/get_chat_template.py Qwen/Qwen2.5-7B-Instruct > models/templates/Qwen-Qwen2.5-7B-Instruct.jinja ./scripts/get_chat_template.py Qwen/QwQ-32B > models/templates/Qwen-QwQ-32B.jinja ./scripts/get_chat_template.py Qwen/Qwen3-0.6B > models/templates/Qwen-Qwen3-0.6B.jinja -``` \ No newline at end of file +./scripts/get_chat_template.py zai-org/GLM-4.5 > models/templates/zai-org-GLM-4.5.jinja +``` diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp index ba7bf9598..8d669bddc 100644 --- a/src/llama-arch.cpp +++ b/src/llama-arch.cpp @@ -62,6 +62,7 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_DEEPSEEK2, "deepseek2" }, { LLM_ARCH_CHATGLM, "chatglm" }, { LLM_ARCH_GLM4, "glm4" }, + { LLM_ARCH_GLM4_MOE, "glm4moe" }, { LLM_ARCH_BITNET, "bitnet" }, { LLM_ARCH_T5, "t5" }, { LLM_ARCH_T5ENCODER, "t5encoder" }, @@ -127,6 +128,7 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_EXPERT_WEIGHTS_NORM, "%s.expert_weights_norm" }, { LLM_KV_EXPERT_GATING_FUNC, "%s.expert_gating_func" }, { LLM_KV_MOE_EVERY_N_LAYERS, "%s.moe_every_n_layers" }, + { LLM_KV_NEXTN_PREDICT_LAYERS, "%s.nextn_predict_layers" }, { LLM_KV_POOLING_TYPE, "%s.pooling_type" }, { LLM_KV_LOGIT_SCALE, "%s.logit_scale" }, { LLM_KV_DECODER_START_TOKEN_ID, "%s.decoder_start_token_id" }, @@ -1391,6 +1393,40 @@ static const std::map> LLM_TENSOR_N { LLM_TENSOR_FFN_POST_NORM, "blk.%d.post_ffw_norm" }, }, }, + { + LLM_ARCH_GLM4_MOE, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_POST_NORM, "blk.%d.post_attention_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" }, + { LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" }, + { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + { LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" }, + { LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" }, + { LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" }, + { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" }, + { LLM_TENSOR_FFN_GATE_SHEXP, "blk.%d.ffn_gate_shexp" }, + { LLM_TENSOR_FFN_DOWN_SHEXP, "blk.%d.ffn_down_shexp" }, + { LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" }, + { LLM_TENSOR_FFN_EXP_PROBS_B, "blk.%d.exp_probs_b" }, + // NextN/MTP tensors - preserved but unused (in final layer, dynamic layer number) + { LLM_TENSOR_NEXTN_EH_PROJ, "blk.%d.nextn.eh_proj" }, + { LLM_TENSOR_NEXTN_EMBED_TOKENS, "blk.%d.nextn.embed_tokens" }, + { LLM_TENSOR_NEXTN_ENORM, "blk.%d.nextn.enorm" }, + { LLM_TENSOR_NEXTN_HNORM, "blk.%d.nextn.hnorm" }, + { LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "blk.%d.nextn.shared_head_head" }, + { LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "blk.%d.nextn.shared_head_norm" }, + }, + }, { LLM_ARCH_BITNET, { @@ -2181,6 +2217,14 @@ static const std::map LLM_TENSOR_INFOS = { {LLM_TENSOR_SHORTCONV_CONV, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_SSM_CONV}}, {LLM_TENSOR_SHORTCONV_INPROJ, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, {LLM_TENSOR_SHORTCONV_OUTPROJ, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + // NextN/MTP tensors are currently ignored (reserved for future MTP support) + // These tensors only exist in the last layer(s) and are treated as output tensors + {LLM_TENSOR_NEXTN_EH_PROJ, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_NEXTN_EMBED_TOKENS, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_GET_ROWS}}, + {LLM_TENSOR_NEXTN_ENORM, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_GET_ROWS}}, + {LLM_TENSOR_NEXTN_HNORM, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}}, + {LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}}, }; LLM_KV::LLM_KV(llm_arch arch, const char * suffix) : arch(arch), suffix(suffix) {} diff --git a/src/llama-arch.h b/src/llama-arch.h index 9b8bd65b2..456eb8d8c 100644 --- a/src/llama-arch.h +++ b/src/llama-arch.h @@ -66,6 +66,7 @@ enum llm_arch { LLM_ARCH_DEEPSEEK2, LLM_ARCH_CHATGLM, LLM_ARCH_GLM4, + LLM_ARCH_GLM4_MOE, LLM_ARCH_BITNET, LLM_ARCH_T5, LLM_ARCH_T5ENCODER, @@ -131,6 +132,7 @@ enum llm_kv { LLM_KV_EXPERT_WEIGHTS_NORM, LLM_KV_EXPERT_GATING_FUNC, LLM_KV_MOE_EVERY_N_LAYERS, + LLM_KV_NEXTN_PREDICT_LAYERS, LLM_KV_POOLING_TYPE, LLM_KV_LOGIT_SCALE, LLM_KV_DECODER_START_TOKEN_ID, @@ -409,6 +411,12 @@ enum llm_tensor { LLM_TENSOR_SHORTCONV_CONV, LLM_TENSOR_SHORTCONV_INPROJ, LLM_TENSOR_SHORTCONV_OUTPROJ, + LLM_TENSOR_NEXTN_EH_PROJ, + LLM_TENSOR_NEXTN_EMBED_TOKENS, + LLM_TENSOR_NEXTN_ENORM, + LLM_TENSOR_NEXTN_HNORM, + LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, + LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, }; enum llm_tensor_layer { diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp index 491a26b63..9c15e8324 100644 --- a/src/llama-graph.cpp +++ b/src/llama-graph.cpp @@ -749,8 +749,8 @@ ggml_tensor * llm_graph_context::build_ffn( if (down) { cur = build_lora_mm(down, cur); - if (arch == LLM_ARCH_GLM4) { - // GLM4 seems to have numerical issues with half-precision accumulators + if (arch == LLM_ARCH_GLM4 || arch == LLM_ARCH_GLM4_MOE) { + // GLM4 and GLM4_MOE seem to have numerical issues with half-precision accumulators ggml_mul_mat_set_prec(cur, GGML_PREC_F32); } } @@ -1391,8 +1391,8 @@ ggml_tensor * llm_graph_context::build_attn( if (wo) { cur = build_lora_mm(wo, cur); - if (arch == LLM_ARCH_GLM4) { - // GLM4 seems to have numerical issues with half-precision accumulators + if (arch == LLM_ARCH_GLM4 || arch == LLM_ARCH_GLM4_MOE) { + // GLM4 and GLM4_MOE seem to have numerical issues with half-precision accumulators ggml_mul_mat_set_prec(cur, GGML_PREC_F32); } } diff --git a/src/llama-hparams.h b/src/llama-hparams.h index 8b7e2a113..d60035726 100644 --- a/src/llama-hparams.h +++ b/src/llama-hparams.h @@ -73,6 +73,7 @@ struct llama_hparams { bool expert_weights_norm = false; uint32_t expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_NONE; uint32_t moe_every_n_layers = 0; + uint32_t nextn_predict_layers = 0; float f_norm_eps; float f_norm_rms_eps; diff --git a/src/llama-kv-cache-unified.cpp b/src/llama-kv-cache-unified.cpp index e1614d1b8..e539142e6 100644 --- a/src/llama-kv-cache-unified.cpp +++ b/src/llama-kv-cache-unified.cpp @@ -39,6 +39,10 @@ llama_kv_cache_unified::llama_kv_cache_unified( if (model.arch == LLM_ARCH_GEMMA3N) { n_layer_cache = 20; } + if (model.arch == LLM_ARCH_GLM4_MOE) { + // GLM-4.5: Only process up to last layer, skip final NextN layer + n_layer_cache = hparams.n_layer - hparams.nextn_predict_layers; + } // create a context for each buffer type std::map ctx_map; diff --git a/src/llama-model-loader.h b/src/llama-model-loader.h index 0f52b011b..c9189f6cb 100644 --- a/src/llama-model-loader.h +++ b/src/llama-model-loader.h @@ -58,8 +58,9 @@ struct llama_model_loader { } }; - static const int TENSOR_NOT_REQUIRED = 1; - static const int TENSOR_DUPLICATED = 2; + static const int TENSOR_NOT_REQUIRED = 1 << 0; + static const int TENSOR_DUPLICATED = 1 << 1; + static const int TENSOR_SKIP = 1 << 2; int n_kv = 0; int n_tensors = 0; diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 60a615c15..44f89003b 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -109,8 +109,10 @@ const char * llm_type_name(llm_type type) { case LLM_TYPE_A13B: return "A13B"; case LLM_TYPE_21B_A3B: return "21B.A3B"; case LLM_TYPE_30B_A3B: return "30B.A3B"; + case LLM_TYPE_106B_A12B: return "106B.A12B"; case LLM_TYPE_235B_A22B: return "235B.A22B"; case LLM_TYPE_300B_A47B: return "300B.A47B"; + case LLM_TYPE_355B_A32B: return "355B.A32B"; case LLM_TYPE_E2B: return "E2B"; case LLM_TYPE_E4B: return "E4B"; default: return "?B"; @@ -1434,6 +1436,34 @@ void llama_model::load_hparams(llama_model_loader & ml) { default: type = LLM_TYPE_UNKNOWN; } } break; + case LLM_ARCH_GLM4_MOE: + { + ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + + // MoE parameters + ml.get_key(LLM_KV_EXPERT_COUNT, hparams.n_expert); + ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used); + ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared); + ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false); + ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale); + ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false); + + // Expert gating function (GLM-4.5 uses sigmoid) + ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false); + if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) { + hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID; + } + + // NextN/MTP parameters + ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.nextn_predict_layers, false); + + switch (hparams.n_layer) { + case 47: type = LLM_TYPE_106B_A12B; break; // GLM-4.5-Air (46 layers + 1 NextN layer) + case 93: type = LLM_TYPE_355B_A32B; break; // GLM-4.5 (92 layers + 1 NextN layer) + default: type = LLM_TYPE_UNKNOWN; + } + } break; case LLM_ARCH_BITNET: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); @@ -1949,6 +1979,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) { const auto TENSOR_DUPLICATED = llama_model_loader::TENSOR_DUPLICATED; const auto TENSOR_NOT_REQUIRED = llama_model_loader::TENSOR_NOT_REQUIRED; + const auto TENSOR_SKIP = llama_model_loader::TENSOR_SKIP; // create tensors for the weights { @@ -2004,7 +2035,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) { } // skip unused tensors - if (info.op == GGML_OP_NONE) { + if (info.op == GGML_OP_NONE || flags & TENSOR_SKIP) { const size_t nbytes = ggml_nbytes(t_meta); LLAMA_LOG_WARN("model has unused tensor %s (size = %zu bytes) -- ignoring\n", tn.str().c_str(), nbytes); @@ -4427,6 +4458,105 @@ bool llama_model::load_tensors(llama_model_loader & ml) { layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0); } } break; + case LLM_ARCH_GLM4_MOE: + { + const int64_t n_expert = hparams.n_expert; + const int64_t n_expert_used = hparams.n_expert_used; + const int64_t n_expert_shared = hparams.n_expert_shared; + + GGML_ASSERT(hparams.n_expert > 0 && "n_expert must be > 0 for GLM4_MOE MoE layers"); + GGML_ASSERT(hparams.n_expert_used > 0 && "n_expert_used must be > 0 for GLM4_MOE MoE layers"); + + tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0); + + // output + output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0); + output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED); + // if output is NULL, init from the input tok embed + if (output == NULL) { + output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED); + } + + // Load ALL tensors including NextN layer to satisfy total tensor count + // but only PROCESS up to last layer (skipping final NextN layer) in forward pass + for (int i = 0; i < n_layer; ++i) { + int flags = 0; + if (hparams.nextn_predict_layers > 0 && static_cast(i) >= n_layer - hparams.nextn_predict_layers) { + // skip all tensors in the NextN layers + flags |= TENSOR_SKIP; + } + + auto & layer = layers[i]; + + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, flags); + + // GLM-style attention with bias terms + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd_head_k * n_head }, flags); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_k_gqa }, flags); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), { n_embd, n_embd_v_gqa }, flags); + layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), { n_embd_head_k * n_head }, flags); + layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), { n_embd_k_gqa }, flags); + layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), { n_embd_v_gqa }, flags); + + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, flags); + + // K/Q norm tensors (optional for GLM-4.5 355B variant) + layer.attn_q_norm = create_tensor( + tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, TENSOR_NOT_REQUIRED | flags); + layer.attn_k_norm = create_tensor( + tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, TENSOR_NOT_REQUIRED | flags); + + layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), { n_embd }, flags); + + // Check if this layer uses MoE or dense FFN based on n_layer_dense_lead + // GLM 4.5 uses hybrid architecture: layer 0 is dense, layers 1+ are MoE + const bool use_moe = (static_cast(i) >= hparams.n_layer_dense_lead); + + if (use_moe) { + // MoE layers + layer.ffn_gate_inp = + create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert }, flags); + layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), { n_expert }, flags); + + // MoE branch + const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used; + + layer.ffn_gate_exps = create_tensor( + tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, flags); + layer.ffn_down_exps = create_tensor( + tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, flags); + layer.ffn_up_exps = create_tensor( + tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, flags); + + // Shared expert + if (n_expert_shared > 0) { + const int64_t n_ff_shexp = n_ff_exp * n_expert_shared; + layer.ffn_gate_shexp = create_tensor( + tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp }, flags); + layer.ffn_down_shexp = create_tensor( + tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_shexp, n_embd }, flags); + layer.ffn_up_shexp = create_tensor( + tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, n_ff_shexp }, flags); + } + } else { + // Dense layers (first k layers) - GLM uses separate gate/up projections + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), { n_embd, n_ff }, flags); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, flags); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, n_ff }, flags); + } + + // NextN/MTP tensors (preserved but unused) - conditionally load for last nextn_predict_layers + if (hparams.nextn_predict_layers > 0 && static_cast(i) >= n_layer - hparams.nextn_predict_layers) { + layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, flags); + layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, flags); + layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags); + layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, flags); + layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, flags); + layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, flags); + } + } + } + break; case LLM_ARCH_NEMOTRON: { tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); @@ -13564,6 +13694,169 @@ struct llm_build_glm4 : public llm_graph_context { } }; +struct llm_build_glm4_moe : public llm_graph_context { + llm_build_glm4_moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { + const int64_t n_embd_head = hparams.n_embd_head_v; + + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + + ggml_tensor * cur; + ggml_tensor * inpL; + + inpL = build_inp_embd(model.tok_embd); + + // inp_pos - contains the positions + ggml_tensor * inp_pos = build_inp_pos(); + + auto * inp_attn = build_attn_inp_kv_unified(); + + ggml_tensor * inp_out_ids = build_inp_out_ids(); + + // Only process up to last layer (skip final NextN layer) + // Final layer tensors are loaded but not processed in forward pass + const int n_transformer_layers = n_layer - hparams.nextn_predict_layers; + for (int il = 0; il < n_transformer_layers; ++il) { + ggml_tensor * inpSA = inpL; + + // Pre-attention norm + cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il); + cb(cur, "attn_norm", il); + + // self-attention + { + ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur); + if (model.layers[il].bq) { + Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq); + } + cb(Qcur, "Qcur", il); + + ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur); + if (model.layers[il].bk) { + Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk); + } + cb(Kcur, "Kcur", il); + + ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur); + if (model.layers[il].bv) { + Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); + } + cb(Vcur, "Vcur", il); + + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); + Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens); + + // Apply Q/K norm if available (GLM-4.5 355B variant) + if (model.layers[il].attn_q_norm) { + Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il); + cb(Qcur, "Qcur_normed", il); + } + if (model.layers[il].attn_k_norm) { + Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il); + cb(Kcur, "Kcur_normed", il); + } + + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + cur = build_attn(inp_attn, + model.layers[il].wo, NULL, + Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); + } + + if (il == n_transformer_layers - 1 && inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + + ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // Post-attention norm + cur = build_norm(ffn_inp, model.layers[il].attn_post_norm, NULL, LLM_NORM_RMS, il); + cb(cur, "post_attn_norm", il); + + // Check if this is a dense layer (n_layer_dense_lead=1, so layer 0 is dense) + if (static_cast(il) < hparams.n_layer_dense_lead) { + // Dense FFN layer + cur = build_ffn(cur, + model.layers[il].ffn_up, NULL, NULL, + model.layers[il].ffn_gate, NULL, NULL, + model.layers[il].ffn_down, NULL, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(cur, "ffn_out", il); + } else { + // MoE layer with shared experts + const int64_t n_expert = hparams.n_expert; + const int64_t n_expert_used = hparams.n_expert_used; + + // Process routed experts using existing MoE infrastructure + ggml_tensor * routed_out = build_moe_ffn(cur, + model.layers[il].ffn_gate_inp, + model.layers[il].ffn_up_exps, + model.layers[il].ffn_gate_exps, + model.layers[il].ffn_down_exps, + model.layers[il].ffn_exp_probs_b, + n_expert, n_expert_used, + LLM_FFN_SILU, hparams.expert_weights_norm, + true, hparams.expert_weights_scale, + (llama_expert_gating_func_type) hparams.expert_gating_func, + il); + cb(routed_out, "ffn_moe_out", il); + + // Process shared expert on original input + ggml_tensor * shared_out = build_ffn(cur, + model.layers[il].ffn_up_shexp, NULL, NULL, + model.layers[il].ffn_gate_shexp, NULL, NULL, + model.layers[il].ffn_down_shexp, NULL, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(shared_out, "ffn_shexp_out", il); + + // Final output: routed_output + shared_output + cur = ggml_add(ctx0, routed_out, shared_out); + cb(cur, "ffn_out", il); + } + + cur = ggml_add(ctx0, cur, ffn_inp); + + cur = build_cvec(cur, il); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1); + + cb(cur, "result_norm", -1); + res->t_embd = cur; + + // lm_head + cur = build_lora_mm(model.output, cur); + + cb(cur, "result_output", -1); + res->t_logits = cur; + + ggml_build_forward_expand(gf, cur); + } +}; + struct llm_build_nemotron : public llm_graph_context { llm_build_nemotron(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; @@ -17877,6 +18170,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const { { llm = std::make_unique(*this, params); } break; + case LLM_ARCH_GLM4_MOE: + { + llm = std::make_unique(*this, params); + } break; case LLM_ARCH_BITNET: { llm = std::make_unique(*this, params); @@ -18208,6 +18505,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) { case LLM_ARCH_HUNYUAN_DENSE: case LLM_ARCH_LFM2: case LLM_ARCH_SMALLTHINKER: + case LLM_ARCH_GLM4_MOE: return LLAMA_ROPE_TYPE_NEOX; case LLM_ARCH_QWEN2VL: diff --git a/src/llama-model.h b/src/llama-model.h index 094e23808..bdb81cecd 100644 --- a/src/llama-model.h +++ b/src/llama-model.h @@ -101,8 +101,10 @@ enum llm_type { LLM_TYPE_A13B, LLM_TYPE_21B_A3B, // Ernie MoE small LLM_TYPE_30B_A3B, + LLM_TYPE_106B_A12B, // GLM-4.5-Air LLM_TYPE_235B_A22B, LLM_TYPE_300B_A47B, // Ernie MoE big + LLM_TYPE_355B_A32B, // GLM-4.5 LLM_TYPE_E2B, LLM_TYPE_E4B, }; @@ -166,6 +168,15 @@ struct llama_layer_shortconv { struct ggml_tensor * out_proj = nullptr; }; +struct llama_layer_nextn { + struct ggml_tensor * eh_proj = nullptr; + struct ggml_tensor * embed_tokens = nullptr; + struct ggml_tensor * enorm = nullptr; + struct ggml_tensor * hnorm = nullptr; + struct ggml_tensor * shared_head_head = nullptr; + struct ggml_tensor * shared_head_norm = nullptr; +}; + struct llama_layer { // normalization struct ggml_tensor * attn_norm = nullptr; @@ -354,6 +365,8 @@ struct llama_layer { struct llama_layer_convnext convnext; struct llama_layer_shortconv shortconv; + + struct llama_layer_nextn nextn; }; struct llama_model { diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp index 959c86a14..3f43fc556 100644 --- a/src/llama-vocab.cpp +++ b/src/llama-vocab.cpp @@ -2191,6 +2191,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { || t.first == "<｜fim▁begin｜>" // DeepSeek || t.first == "
"
                         || t.first == "▁
"          // CodeLlama
+                        || t.first == "<|code_prefix|>" // GLM-4.5
                         ) {
                     special_fim_pre_id = t.second;
                     if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
@@ -2210,6 +2211,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                         || t.first == "<｜fim▁hole｜>" // DeepSeek
                         || t.first == ""
                         || t.first == "▁"         // CodeLlama
+                        || t.first == "<|code_suffix|>" // GLM-4.5
                         ) {
                     special_fim_suf_id = t.second;
                     if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
@@ -2229,6 +2231,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                         || t.first == "<｜fim▁end｜>"  // DeepSeek
                         || t.first == ""
                         || t.first == "▁"         // CodeLlama
+                        || t.first == "<|code_middle|>" // GLM-4.5
                         ) {
                     special_fim_mid_id = t.second;
                     if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {