from __future__ import annotations
from typing import Iterable, Sequence, TYPE_CHECKING
import torch
if TYPE_CHECKING:
from torch import Tensor
from .base import ModelBase, TextModel, MmprojModel, gguf, logger
@ModelBase.register("MiniMaxText01ForCausalLM")
@ModelBase.register("MiniMaxM1ForCausalLM")
@ModelBase.example("MiniMaxAI/MiniMax-Text-01", "MiniMaxAI/MiniMax-M1-40k")
class MiniMaxText01Model(TextModel):
model_arch = gguf.MODEL_ARCH.MINIMAX01
def _get_suppress_tokens(self) -> Sequence[int] | None:
import json
from transformers import AutoTokenizer
from .base import LazyTorchTensor
# check added tokens embeddings in embeddings tensor for zero-valued embeddings
# they get in the way of the token sampling process and must be suppressed
tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
tokenizer_vocab_size = tokenizer.vocab_size
with open(self.dir_model / "model.safetensors.index.json", "r", encoding="utf-8") as f:
weight_map = json.load(f)["weight_map"]
embeddings_tensor_name = "model.embed_tokens.weight"
embeddings_shard_name = weight_map[embeddings_tensor_name]
with gguf.utility.SafetensorsLocal(self.dir_model / embeddings_shard_name) as model_shard:
embeddings_data = model_shard[embeddings_tensor_name]
embeddings_weights_dtype = LazyTorchTensor._dtype_str_map[embeddings_data.dtype]
embeddings_weights = torch.from_numpy(embeddings_data.mmap_bytes()).view(embeddings_weights_dtype).reshape(embeddings_data.shape)
embeddings_vocab_size = embeddings_weights.shape[0]
embeddings_added_tokens = embeddings_weights[tokenizer_vocab_size:embeddings_vocab_size]
embeddings_zero_rows = torch.all(embeddings_added_tokens == 0, dim=1)
tokens_zero_embeddings_ids = (torch.nonzero(embeddings_zero_rows, as_tuple=False).flatten() + tokenizer_vocab_size).tolist()
return tokens_zero_embeddings_ids
def set_vocab(self) -> None:
from pathlib import Path
self._set_vocab_gpt2()
for tmpl_file in [
self.dir_model / "chat_template.jinja",
Path(__file__).parent.parent / "models" / "templates" / "MiniMax-M1.jinja"
]:
if tmpl_file.is_file():
self.gguf_writer.add_chat_template(tmpl_file.read_text(encoding="utf-8"))
logger.info(f"Chat template overridden with {tmpl_file}.")
break
def set_gguf_parameters(self):
super().set_gguf_parameters()
suppress_tokens = self._get_suppress_tokens()
if suppress_tokens:
logger.info(f"Suppressing tokens with zero embeddings {suppress_tokens}")
self.gguf_writer.add_suppress_tokens(suppress_tokens)
layernorm_full_attention_alpha = self.hparams["layernorm_full_attention_alpha"]
layernorm_full_attention_beta = self.hparams["layernorm_full_attention_beta"]
layernorm_linear_attention_alpha = self.hparams["layernorm_linear_attention_alpha"]
layernorm_linear_attention_beta = self.hparams["layernorm_linear_attention_beta"]
layernorm_mlp_alpha = self.hparams["layernorm_mlp_alpha"]
layernorm_mlp_beta = self.hparams["layernorm_mlp_beta"]
assert layernorm_full_attention_alpha == layernorm_linear_attention_alpha == layernorm_mlp_alpha
assert layernorm_full_attention_beta == layernorm_linear_attention_beta == layernorm_mlp_beta == 1.0
# we do not store the layernorm betas as they are all 1.0
# layernorm alphas are stored as single residual_scale hparam
self.gguf_writer.add_residual_scale(layernorm_full_attention_alpha)
self.gguf_writer.add_rope_dimension_count(self.hparams["rotary_dim"])
_experts: list[dict[str, Tensor]] | None = None
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# process the experts separately
if name.find("block_sparse_moe.experts") != -1:
n_experts = self.hparams["num_local_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:
# merge the experts into a single 3d tensor
for wid in ["w1", "w2", "w3"]:
datas: list[Tensor] = []
for xid in range(n_experts):
ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid}.weight"
datas.append(self._experts[bid][ename])
del self._experts[bid][ename]
data_torch = torch.stack(datas, dim=0)
merged_name = f"layers.{bid}.feed_forward.experts.{wid}.weight"
new_name = self.map_tensor_name(merged_name)
yield from super().modify_tensors(data_torch, new_name, bid)
return
else:
return
yield from super().modify_tensors(data_torch, name, bid)
@ModelBase.register("MiniMaxM2ForCausalLM")
@ModelBase.example("MiniMaxAI/MiniMax-M2")
class MiniMaxM2Model(TextModel):
model_arch = gguf.MODEL_ARCH.MINIMAXM2
_experts_cache: dict[int, dict[str, Tensor]] = {}
def set_gguf_parameters(self):
super().set_gguf_parameters()
self.gguf_writer.add_expert_feed_forward_length(self.find_hparam(["intermediate_size"]))
self.gguf_writer.add_rope_dimension_count(self.find_hparam(["rotary_dim"]))
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
# merge expert weights
if "block_sparse_moe.experts." in name:
n_experts = self.find_hparam(["num_local_experts", "num_experts"])
assert bid is not None
expert_cache = self._experts_cache.setdefault(bid, {})
expert_cache[name] = data_torch
expert_weights = ["w1", "w2", "w3"]
# not enough expert weights to merge
if len(expert_cache) < n_experts * len(expert_weights):
return
for w_name in expert_weights:
datas: list[Tensor] = []
for xid in range(n_experts):
ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{w_name}.weight"
datas.append(expert_cache[ename])
del expert_cache[ename]
data_torch = torch.stack(datas, dim=0)
merged_name = f"model.layers.{bid}.block_sparse_moe.experts.{w_name}.weight"
new_name = self.map_tensor_name(merged_name)
yield from super().modify_tensors(data_torch, new_name, bid)
del self._experts_cache[bid]
return
yield from super().modify_tensors(data_torch, name, bid)
@ModelBase.register("MiniMaxM3SparseForCausalLM", "MiniMaxM3SparseForConditionalGeneration")
@ModelBase.example("MiniMaxAI/MiniMax-M3")
class MiniMaxM3Model(MiniMaxM2Model):
model_arch = gguf.MODEL_ARCH.MINIMAXM3
def tensor_force_quant(self, name, new_name, bid, n_dims):
if ".indexer." in new_name:
return gguf.GGMLQuantizationType.F32
return super().tensor_force_quant(name, new_name, bid, n_dims)
def set_gguf_parameters(self):
super().set_gguf_parameters()
self.gguf_writer.add_expert_shared_count(self.find_hparam(["n_shared_experts"]))
self.gguf_writer.add_expert_weights_scale(self.find_hparam(["routed_scaling_factor"]))
self.gguf_writer.add_expert_weights_norm(True)
sac = self.find_hparam(["sparse_attention_config"])
self.gguf_writer.add_indexer_head_count(sac["sparse_num_index_heads"])
self.gguf_writer.add_indexer_key_length(sac["sparse_index_dim"])
self.gguf_writer.add_indexer_top_k(sac["sparse_topk_blocks"])
self.gguf_writer.add_indexer_block_size(sac["sparse_block_size"])
self.gguf_writer.add_indexer_local_blocks(sac["sparse_local_block"])
moe_layer_freq = self.find_hparam(["moe_layer_freq"])
n_dense = 0
for v in moe_layer_freq:
if v == 0:
n_dense += 1
else:
break
self.gguf_writer.add_leading_dense_block_count(n_dense)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
# Gemma-style (1 + w) RMSNorm: bake the +1 in so llama.cpp can use plain RMSNorm
if name.endswith("norm.weight"):
data_torch = data_torch + 1.0
yield from super().modify_tensors(data_torch, name, bid)
@ModelBase.register("MiniMaxM3SparseForConditionalGeneration", "MiniMaxM3VLForConditionalGeneration")
@ModelBase.example("MiniMaxAI/MiniMax-M3")
class MiniMaxM3VisionModel(MmprojModel):
@classmethod
def filter_tensors(cls, item):
name, gen = item
# keep only the vision-side tensors; text / mtp / sparse-index are dropped
if not name.startswith(("vision_tower.", "multi_modal_projector.", "patch_merge_mlp.")):
return None
return super().filter_tensors((name, gen))
def set_gguf_parameters(self):
super().set_gguf_parameters()
assert self.hparams_vision is not None
self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.MINIMAXM3)
self.gguf_writer.add_vision_use_gelu(True)
# the ViT carries its own LayerNorm eps (text tower uses a different one)
self.gguf_writer.add_vision_attention_layernorm_eps(
self.hparams_vision.get("layer_norm_eps", 1e-5)
)
comp = self.hparams_vision.get("img_token_compression_config", {})
merge_size = comp.get("spatial_merge_size", 2)
self.gguf_writer.add_vision_spatial_merge_size(int(merge_size))
def modify_tensors(self, data_torch, name, bid):
assert self.hparams_vision is not None
# Conv3d patch embed -> Conv2d slices
if name == "vision_tower.vision_model.embeddings.patch_embedding.weight":
if data_torch.ndim != 5:
raise ValueError(f"unexpected patch_embedding rank {data_torch.ndim} for {name}")
kt = data_torch.shape[2]
base = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_ENC_EMBD_PATCH]
for t in range(kt):
suffix = ".weight" if t == 0 else f".weight.{t}"
yield (base + suffix, data_torch[:, :, t, ...])
return
# Permute ViT q/k. HF [Ta Ha Wa | Tb Hb Wb | pad] reorder to [Ta Tb | Ha Hb | Wa Wb | pad].
for new_name, tensor in super().modify_tensors(data_torch, name, bid):
if ".attn_q." in new_name or ".attn_k." in new_name:
tensor = self._permute_vit_qk(tensor, new_name)
yield new_name, tensor
def _permute_vit_qk(self, t: "Tensor", new_name: str) -> "Tensor":
assert self.hparams_vision is not None
n_head = self.hparams_vision["num_attention_heads"]
d_head = t.shape[0] // n_head
axis_dim = 2 * ((2 * (d_head // 2) // 3) // 2)
ah = axis_dim // 2
half = 3 * ah
perm = []
perm += list(range(0, ah))
perm += list(range(half, half + ah))
perm += list(range(ah, 2 * ah))
perm += list(range(half + ah, half + 2 * ah))
perm += list(range(2 * ah, 3 * ah))
perm += list(range(half + 2 * ah, half + 3 * ah))
perm += list(range(2 * half, d_head))
assert axis_dim % 2 == 0
assert 3 * axis_dim <= d_head
assert len(perm) == d_head
assert sorted(perm) == list(range(d_head)), "perm is not a bijection of d_head"
assert t.shape[0] == n_head * d_head, f"{new_name}: {t.shape[0]} != {n_head}*{d_head}"
assert d_head == 80
idx = torch.tensor(perm, dtype=torch.long)
if t.ndim == 2:
return t.reshape(n_head, d_head, t.shape[1])[:, idx, :].reshape(t.shape)
return t.reshape(n_head, d_head)[:, idx].reshape(t.shape)