model : Minimax M2 (#16831)
* Model: Minimax M2 * Cleanup * Cleanup pt. 2 * Cleanup pt. 3 * Update convert_hf_to_gguf_update.py - merge catch blocks Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Remove vocab models and test * Remove all redundant hparam settings covered by TextModel * Move super to start, don't set block_count * Update src/llama-model.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Update gguf-py/gguf/constants.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
This commit is contained in:
Piotr Wilkin (ilintar)
GitHub
parent
e58d585604
commit
0de0a01576
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@ -1054,6 +1054,9 @@ class TextModel(ModelBase):
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if chkhsh == "53e325976a6e142379c19b09afcae354f2f496f147afa8f9e189a33fe4e3024e":
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# ref: https://huggingface.co/ibm-granite/granite-docling-258M
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res = "granite-docling"
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if chkhsh == "f4f37b6c8eb9ea29b3eac6bb8c8487c5ab7885f8d8022e67edc1c68ce8403e95":
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# ref: https://huggingface.co/MiniMaxAI/MiniMax-M2
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res = "minimax-m2"
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if res is None:
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logger.warning("\n")
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@ -7126,6 +7129,64 @@ class DeepseekV2Model(TextModel):
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raise ValueError(f"Unprocessed experts: {experts}")
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@ModelBase.register("MiniMaxM2ForCausalLM")
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class MiniMaxM2Model(TextModel):
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model_arch = gguf.MODEL_ARCH.MINIMAXM2
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_experts_cache: dict[int, dict[str, Tensor]] = {}
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.hparams["num_experts"] = self.hparams["num_local_experts"]
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def set_gguf_parameters(self):
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super().set_gguf_parameters()
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if self.hparams["scoring_func"] == "sigmoid":
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self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
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elif self.hparams["scoring_func"] == "softmax":
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self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SOFTMAX)
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else:
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raise ValueError(f"Unsupported scoring_func value: {self.hparams['scoring_func']}")
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self.gguf_writer.add_expert_feed_forward_length(self.find_hparam(["intermediate_size"]))
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self.gguf_writer.add_rope_dimension_count(self.find_hparam(["rotary_dim"]))
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
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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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# merge expert weights
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if 'experts' in name:
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n_experts = self.hparams["num_experts"]
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assert bid is not None
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expert_cache = self._experts_cache.setdefault(bid, {})
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expert_cache[name] = data_torch
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expert_weights = ["w1", "w2", "w3"]
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# not enough expert weights to merge
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if len(expert_cache) < n_experts * len(expert_weights):
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return []
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tensors: list[tuple[str, Tensor]] = []
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for w_name in expert_weights:
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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}.block_sparse_moe.experts.{xid}.{w_name}.weight"
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datas.append(expert_cache[ename])
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del expert_cache[ename]
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data_torch = torch.stack(datas, dim=0)
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merged_name = f"model.layers.{bid}.block_sparse_moe.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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del self._experts_cache[bid]
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return tensors
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return super().modify_tensors(data_torch, name, bid)
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@ModelBase.register("Dots1ForCausalLM")
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class Dots1Model(Qwen2MoeModel):
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model_arch = gguf.MODEL_ARCH.DOTS1
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@ -141,6 +141,7 @@ models = [
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{"name": "mellum", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/JetBrains/Mellum-4b-base", },
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{"name": "bailingmoe2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/inclusionAI/Ling-mini-base-2.0", },
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{"name": "granite-docling", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ibm-granite/granite-docling-258M", },
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{"name": "minimax-m2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/MiniMaxAI/MiniMax-M2", },
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]
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# some models are known to be broken upstream, so we will skip them as exceptions
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@ -435,7 +436,7 @@ for model in models:
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tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}", use_fast=False)
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else:
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tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
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except OSError as e:
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except (OSError, TypeError) as e:
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logger.error(f"Failed to load tokenizer for model {name}. Error: {e}")
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continue # Skip this model and continue with the next one in the loop
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@ -425,6 +425,7 @@ class MODEL_ARCH(IntEnum):
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GROVEMOE = auto()
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APERTUS = auto()
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COGVLM = auto()
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MINIMAXM2 = auto()
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class VISION_PROJECTOR_TYPE(IntEnum):
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@ -790,6 +791,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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MODEL_ARCH.SEED_OSS: "seed_oss",
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MODEL_ARCH.GROVEMOE: "grovemoe",
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MODEL_ARCH.APERTUS: "apertus",
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MODEL_ARCH.MINIMAXM2: "minimax-m2",
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MODEL_ARCH.COGVLM: "cogvlm",
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}
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@ -2921,6 +2923,24 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.FFN_DOWN_CHEXP,
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MODEL_TENSOR.FFN_UP_CHEXP,
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],
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MODEL_ARCH.MINIMAXM2: [
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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_Q,
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MODEL_TENSOR.ATTN_Q_NORM,
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MODEL_TENSOR.ATTN_K,
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MODEL_TENSOR.ATTN_K_NORM,
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MODEL_TENSOR.ATTN_V,
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MODEL_TENSOR.ATTN_OUT,
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MODEL_TENSOR.FFN_NORM,
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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_EXP_PROBS_B,
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],
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MODEL_ARCH.COGVLM: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT_NORM,
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@ -381,6 +381,7 @@ class TensorNameMap:
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"model.layers.{bid}.mlp.moe_statics.e_score_correction", # ernie4.5-moe
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"model.layers.{bid}.mlp.gate.expert_bias", # bailingmoe2
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"model.layers.{bid}.feed_forward.expert_bias", # lfm2moe
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"model.layers.{bid}.block_sparse_moe.e_score_correction", # minimax-m2
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),
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# Feed-forward up
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@ -105,6 +105,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
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{ LLM_ARCH_SEED_OSS, "seed_oss" },
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{ LLM_ARCH_GROVEMOE, "grovemoe" },
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{ LLM_ARCH_APERTUS, "apertus" },
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{ LLM_ARCH_MINIMAX_M2, "minimax-m2" },
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{ LLM_ARCH_COGVLM, "cogvlm" },
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{ LLM_ARCH_UNKNOWN, "(unknown)" },
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};
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@ -2355,6 +2356,27 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
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{ LLM_TENSOR_FFN_UP_CHEXPS, "blk.%d.ffn_up_chexps" },
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},
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},
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{
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LLM_ARCH_MINIMAX_M2,
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{
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{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
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{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
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{ LLM_TENSOR_OUTPUT, "output" },
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{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
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{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
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{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
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{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
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{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
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{ LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" },
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{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
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{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
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{ LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
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{ LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
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{ LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
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{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
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{ LLM_TENSOR_FFN_EXP_PROBS_B, "blk.%d.exp_probs_b" },
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},
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},
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{
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LLM_ARCH_COGVLM,
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{
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@ -109,6 +109,7 @@ enum llm_arch {
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LLM_ARCH_SEED_OSS,
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LLM_ARCH_GROVEMOE,
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LLM_ARCH_APERTUS,
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LLM_ARCH_MINIMAX_M2,
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LLM_ARCH_COGVLM,
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LLM_ARCH_UNKNOWN,
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};
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@ -120,6 +120,7 @@ const char * llm_type_name(llm_type type) {
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case LLM_TYPE_30B_A3B: return "30B.A3B";
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case LLM_TYPE_100B_A6B: return "100B.A6B";
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case LLM_TYPE_106B_A12B: return "106B.A12B";
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case LLM_TYPE_230B_A10B: return "230B.A10B";
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case LLM_TYPE_235B_A22B: return "235B.A22B";
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case LLM_TYPE_300B_A47B: return "300B.A47B";
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case LLM_TYPE_355B_A32B: return "355B.A32B";
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@ -2155,6 +2156,17 @@ void llama_model::load_hparams(llama_model_loader & ml) {
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default: type = LLM_TYPE_UNKNOWN;
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}
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} break;
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case LLM_ARCH_MINIMAX_M2:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
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ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
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switch (hparams.n_layer) {
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case 62: type = LLM_TYPE_230B_A10B; break;
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default: type = LLM_TYPE_UNKNOWN;
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}
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} break;
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case LLM_ARCH_COGVLM:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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@ -6185,6 +6197,35 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
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layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), { n_embd_head_k }, TENSOR_NOT_REQUIRED);
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}
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} break;
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case LLM_ARCH_MINIMAX_M2:
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{
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tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
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// output
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output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
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output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
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for (int i = 0; i < n_layer; ++i) {
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auto & layer = layers[i];
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layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd_head_k * n_head }, 0);
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layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_gqa }, 0);
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layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), { n_embd, n_embd_gqa }, 0);
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layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
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layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
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layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k * n_head}, 0);
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layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_k_gqa}, 0);
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layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
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layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
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layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0);
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layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff, n_embd, n_expert}, 0);
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layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0);
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layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0);
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}
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} break;
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case LLM_ARCH_COGVLM:
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{
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tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
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@ -20024,6 +20065,130 @@ struct llm_build_apertus : public llm_graph_context {
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}
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};
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struct llm_build_minimax_m2 : public llm_graph_context {
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llm_build_minimax_m2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
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const int64_t n_embd_head = hparams.n_embd_head_v;
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GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
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// GGML_ASSERT(n_embd_head == hparams.n_rot); this is wrong in case of minimax, head_dim = 128, n_rot = 64
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ggml_tensor * cur;
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ggml_tensor * inpL;
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inpL = build_inp_embd(model.tok_embd);
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ggml_tensor * inp_pos = build_inp_pos();
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auto inp_attn = build_attn_inp_kv();
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ggml_tensor * inp_out_ids = build_inp_out_ids();
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for (int il = 0; il < n_layer; ++il) {
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ggml_tensor * inpSA = inpL;
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cur = inpL;
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// self_attention
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{
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cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
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cb(cur, "attn_norm", il);
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// compute Q and K and RoPE them
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ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
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cb(Qcur, "Qcur", il);
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ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
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cb(Kcur, "Kcur", il);
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ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
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cb(Vcur, "Vcur", il);
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Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL,
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LLM_NORM_RMS, il);
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cb(Qcur, "Qcur_normed", il);
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Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL,
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LLM_NORM_RMS, il);
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cb(Kcur, "Kcur_normed", il);
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Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
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Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
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Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
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Qcur = ggml_rope_ext(
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ctx0, Qcur, inp_pos, nullptr,
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n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow
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);
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Kcur = ggml_rope_ext(
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ctx0, Kcur, inp_pos, nullptr,
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n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow
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);
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cb(Qcur, "Qcur", il);
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cb(Kcur, "Kcur", il);
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cb(Vcur, "Vcur", il);
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cur = build_attn(inp_attn,
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model.layers[il].wo, NULL,
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Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
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}
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if (il == n_layer - 1 && inp_out_ids) {
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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);
|
||||
|
||||
// MoE branch
|
||||
cur = build_norm(ffn_inp,
|
||||
model.layers[il].ffn_norm, NULL,
|
||||
LLM_NORM_RMS, il);
|
||||
cb(cur, "ffn_norm", il);
|
||||
|
||||
cur = 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, true,
|
||||
false, 0.0,
|
||||
(llama_expert_gating_func_type) hparams.expert_gating_func,
|
||||
il);
|
||||
cb(cur, "ffn_moe_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_cogvlm : public llm_graph_context {
|
||||
llm_build_cogvlm(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v;
|
||||
|
|
@ -20654,6 +20819,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
|
|||
{
|
||||
llm = std::make_unique<llm_build_apertus>(*this, params);
|
||||
} break;
|
||||
case LLM_ARCH_MINIMAX_M2:
|
||||
{
|
||||
llm = std::make_unique<llm_build_minimax_m2>(*this, params);
|
||||
} break;
|
||||
case LLM_ARCH_COGVLM:
|
||||
{
|
||||
llm = std::make_unique<llm_build_cogvlm>(*this, params);
|
||||
|
|
@ -20875,6 +21044,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
|
|||
case LLM_ARCH_SEED_OSS:
|
||||
case LLM_ARCH_GROVEMOE:
|
||||
case LLM_ARCH_APERTUS:
|
||||
case LLM_ARCH_MINIMAX_M2:
|
||||
case LLM_ARCH_COGVLM:
|
||||
return LLAMA_ROPE_TYPE_NEOX;
|
||||
|
||||
|
|
|
|||
|
|
@ -114,6 +114,7 @@ enum llm_type {
|
|||
LLM_TYPE_30B_A3B,
|
||||
LLM_TYPE_100B_A6B,
|
||||
LLM_TYPE_106B_A12B, // GLM-4.5-Air
|
||||
LLM_TYPE_230B_A10B, // Minimax M2
|
||||
LLM_TYPE_235B_A22B,
|
||||
LLM_TYPE_300B_A47B, // Ernie MoE big
|
||||
LLM_TYPE_355B_A32B, // GLM-4.5
|
||||
|
|
|
|||
|
|
@ -401,6 +401,7 @@ struct llm_tokenizer_bpe : llm_tokenizer {
|
|||
};
|
||||
break;
|
||||
case LLAMA_VOCAB_PRE_TYPE_GPT4O:
|
||||
case LLAMA_VOCAB_PRE_TYPE_MINIMAX_M2:
|
||||
regex_exprs = {
|
||||
// original regex from tokenizer.json
|
||||
// "[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]*[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]+(?i:'s|'t|'re|'ve|'m|'ll|'d)?|[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]+[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]*(?i:'s|'t|'re|'ve|'m|'ll|'d)?|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
|
||||
|
|
@ -1992,6 +1993,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|
|||
tokenizer_pre == "grok-2") {
|
||||
pre_type = LLAMA_VOCAB_PRE_TYPE_GROK_2;
|
||||
clean_spaces = false;
|
||||
} else if (
|
||||
tokenizer_pre == "minimax-m2") {
|
||||
pre_type = LLAMA_VOCAB_PRE_TYPE_MINIMAX_M2;
|
||||
clean_spaces = false;
|
||||
} else {
|
||||
throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
|
||||
}
|
||||
|
|
|
|||
|
|
@ -49,6 +49,7 @@ enum llama_vocab_pre_type {
|
|||
LLAMA_VOCAB_PRE_TYPE_HUNYUAN_DENSE = 38,
|
||||
LLAMA_VOCAB_PRE_TYPE_GROK_2 = 39,
|
||||
LLAMA_VOCAB_PRE_TYPE_GRANITE_DOCLING = 40,
|
||||
LLAMA_VOCAB_PRE_TYPE_MINIMAX_M2 = 41,
|
||||
};
|
||||
|
||||
struct LLM_KV;
|
||||
|
|
|
|||
Loading…
Reference in New Issue