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model: support GLM 4.5 family of models (#14939)
* model: Add GLM 4.5 (#14921) Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Merge in PR suggestions Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * model: Add GLM 4.5 family of models (#14921) 1. Updated tensor_mapping.py with NextN tensor mappings - Added proper tensor mappings for all NextN/MTP tensors in /Users/samm/git/llama.cpp/gguf-py/gguf/tensor_mapping.py - Added mappings for: eh_proj, embed_tokens, enorm, hnorm, shared_head.head, shared_head.norm 2. Added num_nextn_predict_layers configuration - Added LLM_KV_NUM_NEXTN_PREDICT_LAYERS constant to llama-arch.h and llama-arch.cpp - Added num_nextn_predict_layers field to llama_hparams struct - Updated GLM4_MOE parameter loading in llama-model.cpp to read this parameter - Modified tensor loading logic to conditionally load NextN tensors based on num_nextn_predict_layers - Added GGUF writer support in gguf_writer.py with add_num_nextn_predict_layers() method - Updated conversion script to extract and write this parameter from HuggingFace config 3. Added FIM tokens for GLM4_MOE - Added GLM-4.5's FIM tokens to llama-vocab.cpp: - <|code_prefix|> for FIM_PRE - <|code_suffix|> for FIM_SUF - <|code_middle|> for FIM_MID 4. Removed manual NextN tensor handling - Removed the special-case handling in convert_hf_to_gguf.py that manually mapped NextN tensors - NextN tensors are now handled automatically through the proper tensor mapping system * glm 4.5 update tensors names * model: glm 4.5 apply suggestions from code review Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Update src/llama-model.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * model: glm 4.5 apply suggestions from code review Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * model: glm 4.5 apply suggestions from code review * Apply suggestions from code review * patch broken chat template * typings fix * add TENSOR_SKIP flag Co-authored-by: Diego Devesa <slarengh@gmail.com> * Update src/llama-model-loader.h Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> Co-authored-by: Diego Devesa <slarengh@gmail.com>
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Sam
GitHub
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15 changed files with 594 additions and 8 deletions
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@ -678,6 +678,9 @@ class TextModel(ModelBase):
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if chkhsh == "a1336059768a55c99a734006ffb02203cd450fed003e9a71886c88acf24fdbc2":
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# ref: https://huggingface.co/THUDM/glm-4-9b-hf
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res = "glm4"
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if chkhsh == "9ca2dd618e8afaf09731a7cf6e2105b373ba6a1821559f258b272fe83e6eb902":
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# ref: https://huggingface.co/zai-org/GLM-4.5-Air
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res = "glm4"
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if chkhsh == "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35":
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# ref: https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0
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res = "minerva-7b"
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@ -6696,6 +6699,139 @@ class Glm4Model(TextModel):
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return super().modify_tensors(data_torch, name, bid)
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@ModelBase.register("Glm4MoeForCausalLM")
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class Glm4MoeModel(TextModel):
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model_arch = gguf.MODEL_ARCH.GLM4_MOE
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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# GLM4_MOE has num_hidden_layers + 1 actual layers (including NextN layer)
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self.block_count = self.hparams["num_hidden_layers"] + self.hparams.get("num_nextn_predict_layers", 0)
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self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
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def set_vocab(self):
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
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special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
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tokens, toktypes, tokpre = self.get_vocab_base()
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self.gguf_writer.add_tokenizer_model("gpt2")
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self.gguf_writer.add_tokenizer_pre(tokpre)
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self.gguf_writer.add_token_list(tokens)
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self.gguf_writer.add_token_types(toktypes)
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# Special tokens
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# Note: Using <|endoftext|> (151329) for eot causes endless generation
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special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["[gMASK]"]) # 151331
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special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"]) # 151336
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special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"]) # 151329
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special_vocab._set_special_token("eom", tokenizer.get_added_vocab()["<|observation|>"]) # 151338
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# Patch broken chat template
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if isinstance(special_vocab.chat_template, str) and "visible_text(m.content).endswith" in special_vocab.chat_template:
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special_vocab.chat_template = special_vocab.chat_template.replace(
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"""{{ visible_text(m.content) }}\n{{- '/nothink' if (enable_thinking is defined and not enable_thinking and not visible_text(m.content).endswith("/nothink")) else '' -}}""",
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"""{% set content = visible_text(m.content) %}{{ content }}\n{{- '/nothink' if (enable_thinking is defined and not enable_thinking and not content.endswith("/nothink")) else '' -}}""")
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special_vocab.add_to_gguf(self.gguf_writer)
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def set_gguf_parameters(self):
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super().set_gguf_parameters()
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if (rope_dim := self.hparams.get("head_dim")) is None:
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rope_dim = (
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self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
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)
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self.gguf_writer.add_rope_dimension_count(
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int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5))
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)
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# MoE parameters - Use only routed expert count (shared experts handled separately)
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if (n_routed_experts := self.hparams.get("n_routed_experts")) is not None:
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self.gguf_writer.add_expert_count(n_routed_experts)
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if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
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self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
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if (n_shared_experts := self.hparams.get("n_shared_experts")) is not None:
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self.gguf_writer.add_expert_shared_count(n_shared_experts)
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if (first_k_dense_replace := self.hparams.get("first_k_dense_replace")) is not None:
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self.gguf_writer.add_leading_dense_block_count(first_k_dense_replace)
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# Expert gating function (sigmoid for GLM4_MOE)
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self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
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# Routed scaling factor
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if (routed_scaling_factor := self.hparams.get("routed_scaling_factor")) is not None:
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self.gguf_writer.add_expert_weights_scale(routed_scaling_factor)
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# Normalise topk probabilities
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if (norm_topk_prob := self.hparams.get("norm_topk_prob")) is not None:
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self.gguf_writer.add_expert_weights_norm(norm_topk_prob)
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# NextN/MTP prediction layers
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if (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None:
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self.gguf_writer.add_nextn_predict_layers(num_nextn_predict_layers)
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_experts: list[dict[str, Tensor]] | None = None
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def modify_tensors(
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self, data_torch: Tensor, name: str, bid: int | None
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) -> Iterable[tuple[str, Tensor]]:
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if name.startswith("model.visual."): # ignore visual part
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return []
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elif name.startswith("model.language_model."):
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name = name.replace("language_model.", "") # for multimodal variants
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# Handle main token embedding (but not layer-specific NextN embeddings)
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if name == "model.embed_tokens.weight" and ".layers." not in name:
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return [(self.map_tensor_name("token_embd.weight"), data_torch)]
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# Handle routed experts
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if name.find("mlp.experts") != -1:
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n_experts = self.hparams["n_routed_experts"]
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assert bid is not None
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if self._experts is None:
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self._experts = [{} for _ in range(self.block_count)]
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self._experts[bid][name] = data_torch
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if len(self._experts[bid]) >= n_experts * 3:
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tensors: list[tuple[str, Tensor]] = []
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# merge the experts into a single 3d tensor
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for w_name in ["down_proj", "gate_proj", "up_proj"]:
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datas: list[Tensor] = []
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for xid in range(n_experts):
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ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
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datas.append(self._experts[bid][ename])
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del self._experts[bid][ename]
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data_torch = torch.stack(datas, dim=0)
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merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
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new_name = self.map_tensor_name(merged_name)
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tensors.append((new_name, data_torch))
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return tensors
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else:
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return []
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if name.endswith("e_score_correction_bias"):
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name = name.replace("e_score_correction_bias", "e_score_correction.bias")
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new_name = self.map_tensor_name(name)
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return [(new_name, data_torch)]
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def prepare_tensors(self):
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super().prepare_tensors()
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if self._experts is not None:
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# flatten `list[dict[str, Tensor]]` into `list[str]`
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experts = [k for d in self._experts for k in d.keys()]
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if len(experts) > 0:
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raise ValueError(f"Unprocessed experts: {experts}")
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@ModelBase.register("GlmForCausalLM", "ChatGLMModel", "ChatGLMForConditionalGeneration")
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class ChatGLMModel(TextModel):
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model_arch = gguf.MODEL_ARCH.CHATGLM
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@ -147,6 +147,7 @@ pre_computed_hashes = [
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{"name": "chatglm-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-chat", "chkhsh": "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b"},
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{"name": "chatglm-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-chat", "chkhsh": "81d72c7348a9f0ebe86f23298d37debe0a5e71149e29bd283904c02262b27516"},
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{"name": "glm4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-hf", "chkhsh": "a1336059768a55c99a734006ffb02203cd450fed003e9a71886c88acf24fdbc2"},
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{"name": "glm4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/zai-org/GLM-4.5-Air", "chkhsh": "9ca2dd618e8afaf09731a7cf6e2105b373ba6a1821559f258b272fe83e6eb902"},
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{"name": "minerva-7b", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0", "chkhsh": "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35"},
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{"name": "hunyuan", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Hunyuan-A13B-Instruct", "chkhsh": "7e57df22b1fe23a7b1e1c7f3dc4e3f96d43a4eb0836d0c6bdc3436d7b2f1c664"},
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{"name": "hunyuan-dense", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Hunyuan-4B-Instruct", "chkhsh": "bba3b3366b646dbdded5dbc42d59598b849371afc42f7beafa914afaa5b70aa6"},
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@ -105,6 +105,7 @@ class Keys:
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EXPERT_WEIGHTS_NORM = "{arch}.expert_weights_norm"
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EXPERT_GATING_FUNC = "{arch}.expert_gating_func"
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MOE_EVERY_N_LAYERS = "{arch}.moe_every_n_layers"
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NEXTN_PREDICT_LAYERS = "{arch}.nextn_predict_layers"
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POOLING_TYPE = "{arch}.pooling_type"
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LOGIT_SCALE = "{arch}.logit_scale"
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DECODER_START_TOKEN_ID = "{arch}.decoder_start_token_id"
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@ -357,6 +358,7 @@ class MODEL_ARCH(IntEnum):
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DEEPSEEK2 = auto()
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CHATGLM = auto()
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GLM4 = auto()
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GLM4_MOE = auto()
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BITNET = auto()
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T5 = auto()
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T5ENCODER = auto()
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@ -614,6 +616,13 @@ class MODEL_TENSOR(IntEnum):
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A_MMPROJ_FC = auto()
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A_MM_NORM_PRE = auto()
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A_MM_NORM_MID = auto()
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# nextn/mtp
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NEXTN_EH_PROJ = auto()
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NEXTN_EMBED_TOKENS = auto()
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NEXTN_ENORM = auto()
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NEXTN_HNORM = auto()
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NEXTN_SHARED_HEAD_HEAD = auto()
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NEXTN_SHARED_HEAD_NORM = auto()
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MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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@ -678,6 +687,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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MODEL_ARCH.DEEPSEEK2: "deepseek2",
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MODEL_ARCH.CHATGLM: "chatglm",
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MODEL_ARCH.GLM4: "glm4",
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MODEL_ARCH.GLM4_MOE: "glm4moe",
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MODEL_ARCH.BITNET: "bitnet",
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MODEL_ARCH.T5: "t5",
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MODEL_ARCH.T5ENCODER: "t5encoder",
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@ -936,6 +946,13 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
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MODEL_TENSOR.A_MMPROJ_FC: "mm.a.fc",
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MODEL_TENSOR.A_MM_NORM_PRE: "mm.a.norm_pre",
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MODEL_TENSOR.A_MM_NORM_MID: "mm.a.norm_mid",
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# NextN/MTP
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MODEL_TENSOR.NEXTN_EH_PROJ: "blk.{bid}.nextn.eh_proj",
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MODEL_TENSOR.NEXTN_EMBED_TOKENS: "blk.{bid}.nextn.embed_tokens",
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MODEL_TENSOR.NEXTN_ENORM: "blk.{bid}.nextn.enorm",
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MODEL_TENSOR.NEXTN_HNORM: "blk.{bid}.nextn.hnorm",
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MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD: "blk.{bid}.nextn.shared_head_head",
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MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM: "blk.{bid}.nextn.shared_head_norm",
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}
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MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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@ -2124,6 +2141,37 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.ATTN_POST_NORM,
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MODEL_TENSOR.FFN_POST_NORM,
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],
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MODEL_ARCH.GLM4_MOE: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT_NORM,
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MODEL_TENSOR.OUTPUT,
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MODEL_TENSOR.ATTN_NORM,
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MODEL_TENSOR.ATTN_POST_NORM,
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MODEL_TENSOR.ATTN_Q,
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MODEL_TENSOR.ATTN_K,
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MODEL_TENSOR.ATTN_V,
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MODEL_TENSOR.ATTN_OUT,
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MODEL_TENSOR.ATTN_Q_NORM,
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MODEL_TENSOR.ATTN_K_NORM,
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MODEL_TENSOR.FFN_GATE,
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_UP,
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MODEL_TENSOR.FFN_GATE_INP,
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MODEL_TENSOR.FFN_GATE_EXP,
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MODEL_TENSOR.FFN_DOWN_EXP,
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MODEL_TENSOR.FFN_UP_EXP,
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MODEL_TENSOR.FFN_GATE_SHEXP,
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MODEL_TENSOR.FFN_DOWN_SHEXP,
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MODEL_TENSOR.FFN_UP_SHEXP,
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MODEL_TENSOR.FFN_EXP_PROBS_B,
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# NextN/MTP tensors - preserved but unused
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MODEL_TENSOR.NEXTN_EH_PROJ,
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MODEL_TENSOR.NEXTN_EMBED_TOKENS,
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MODEL_TENSOR.NEXTN_ENORM,
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MODEL_TENSOR.NEXTN_HNORM,
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MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD,
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MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM,
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],
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MODEL_ARCH.BITNET: [
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MODEL_TENSOR.ATTN_Q,
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MODEL_TENSOR.ATTN_K,
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@ -753,6 +753,9 @@ class GGUFWriter:
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def add_moe_every_n_layers(self, value: int) -> None:
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self.add_uint32(Keys.LLM.MOE_EVERY_N_LAYERS.format(arch=self.arch), value)
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def add_nextn_predict_layers(self, count: int) -> None:
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self.add_uint32(Keys.LLM.NEXTN_PREDICT_LAYERS.format(arch=self.arch), count)
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def add_swin_norm(self, value: bool) -> None:
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self.add_bool(Keys.LLM.SWIN_NORM.format(arch=self.arch), value)
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@ -1369,6 +1369,31 @@ class TensorNameMap:
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MODEL_TENSOR.A_MM_NORM_MID: (
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"audio.multi_modal_projector.ln_mid", # ultravox
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),
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# NextN/MTP tensors for GLM4_MOE
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MODEL_TENSOR.NEXTN_EH_PROJ: (
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"model.layers.{bid}.eh_proj",
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),
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MODEL_TENSOR.NEXTN_EMBED_TOKENS: (
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"model.layers.{bid}.embed_tokens",
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),
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MODEL_TENSOR.NEXTN_ENORM: (
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"model.layers.{bid}.enorm",
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),
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MODEL_TENSOR.NEXTN_HNORM: (
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"model.layers.{bid}.hnorm",
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),
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MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD: (
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"model.layers.{bid}.shared_head.head",
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),
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MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM: (
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"model.layers.{bid}.shared_head.norm",
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),
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}
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# architecture-specific block mappings
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@ -21,4 +21,5 @@ These templates can be updated with the following commands:
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./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
|
||||
./scripts/get_chat_template.py zai-org/GLM-4.5 > models/templates/zai-org-GLM-4.5.jinja
|
||||
```
|
||||
|
|
@ -62,6 +62,7 @@ static const std::map<llm_arch, const char *> 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, const char *> 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_arch, std::map<llm_tensor, const char *>> 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, llm_tensor_info> 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) {}
|
||||
|
|
|
|||
|
|
@ -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 {
|
||||
|
|
|
|||
|
|
@ -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);
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -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;
|
||||
|
|
|
|||
|
|
@ -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<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
|
||||
|
|
|
|||
|
|
@ -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;
|
||||
|
|
|
|||
|
|
@ -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<uint32_t>(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<uint32_t>(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<uint32_t>(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<uint32_t>(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<llm_build_glm4>(*this, params);
|
||||
} break;
|
||||
case LLM_ARCH_GLM4_MOE:
|
||||
{
|
||||
llm = std::make_unique<llm_build_glm4_moe>(*this, params);
|
||||
} break;
|
||||
case LLM_ARCH_BITNET:
|
||||
{
|
||||
llm = std::make_unique<llm_build_bitnet>(*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:
|
||||
|
|
|
|||
|
|
@ -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 {
|
||||
|
|
|
|||
|
|
@ -2191,6 +2191,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|
|||
|| t.first == "<｜fim▁begin｜>" // DeepSeek
|
||||
|| t.first == "<PRE>"
|
||||
|| t.first == "▁<PRE>" // 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 == "<SUF>"
|
||||
|| t.first == "▁<SUF>" // 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 == "<MID>"
|
||||
|| t.first == "▁<MID>" // 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) {
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue