from __future__ import annotations
import math
import re
import torch
from typing import TYPE_CHECKING, Any, Callable, Iterable
if TYPE_CHECKING:
from torch import Tensor
from .base import MmprojModel, ModelBase, gguf
from .deepseek import DeepseekV2Model
@ModelBase.register("Dots3NoteForCausalLM", "Dots3NoteForConditionalGeneration", "Dots3NoteTextForCausalLM")
class Dots3NoteModel(DeepseekV2Model):
model_arch = gguf.MODEL_ARCH.DOTS3NOTE
skip_mtp = False
supports_mtp_export = True
# trunk layer count, stashed before indexing for filter_tensors (mirrors DeepseekV32Model)
_n_main_layers: int | None = None
def index_tensors(self, remote_hf_model_id: str | None = None):
type(self)._n_main_layers = self.hparams["num_hidden_layers"]
return super().index_tensors(remote_hf_model_id=remote_hf_model_id)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
hparams = self.hparams
# config file doesn't specify MTP block, detect it from model weight
self.n_nextn = 1 if "model.mtp.embed_tokens.weight" in self.model_tensors else 0
if self.n_nextn:
self.block_count += self.n_nextn
self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
self.layer_types = hparams["layer_types"]
if len(self.layer_types) < hparams["num_hidden_layers"]:
raise ValueError("layer_types is shorter than num_hidden_layers")
if hparams.get("use_dsa", True) is not True:
raise ValueError("dots3-note conversion requires use_dsa=true")
if hparams.get("normalization", "RMSNorm") != "RMSNorm" or hparams.get("final_norm", "RMSNorm") != "RMSNorm":
raise ValueError("dots3-note conversion only supports RMSNorm")
if hparams.get("k_rope_only_layernorm", True) is not True:
raise ValueError("dots3-note conversion requires k_rope_only_layernorm=true")
if hparams.get("topk_method", "noaux_tc") != "noaux_tc" or hparams.get("scoring_func") != "sigmoid":
raise ValueError("dots3-note conversion only supports noaux_tc/sigmoid expert gating")
if hparams.get("n_group", 1) != 1 or hparams.get("topk_group", 1) != 1:
raise ValueError("dots3-note conversion does not support grouped expert routing")
if hparams.get("use_dynamic_rsf", False) or hparams.get("moe_gating_fp32", False):
raise ValueError("dots3-note conversion does not support use_dynamic_rsf/moe_gating_fp32")
for key in ("attention_gate_type", "swa_attention_gate_type"):
if hparams.get(key, "headwise") != "headwise":
raise ValueError(f"dots3-note conversion only supports headwise attention gate, got {key}={hparams.get(key)!r}")
if hparams["swa_qk_nope_head_dim"] + hparams["swa_qk_rope_head_dim"] != hparams.get("swa_head_dim", 256):
raise ValueError("swa_head_dim must equal swa_qk_nope_head_dim + swa_qk_rope_head_dim")
if hparams["swa_qk_rope_head_dim"] != hparams["qk_rope_head_dim"]:
# both layer kinds share a single rope_dimension_count
raise ValueError("swa_qk_rope_head_dim must match qk_rope_head_dim")
self.apply_lora_rescale = hparams.get("apply_mla_qkv_lora_rescale", False)
def _is_swa_layer(self, bid: int) -> bool:
if bid >= self.hparams["num_hidden_layers"]:
# note: the NextN/MTP block uses the sliding-attention MLA
return True
return self.layer_types[bid] == "sliding_attention"
def set_vocab(self):
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
tokens, toktypes, tokpre = self.get_vocab_base()
self.gguf_writer.add_tokenizer_model("gpt2")
self.gguf_writer.add_tokenizer_pre(tokpre)
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_types(toktypes)
special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|endofassistant|>"]) # ty: ignore[unresolved-attribute]
special_vocab.add_to_gguf(self.gguf_writer)
@classmethod
def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
if (titem := super().filter_tensors(item)) is None:
return None
name, gen = titem
if name.startswith(("vision_encoder.", "audio_encoder.")):
return None
assert cls._n_main_layers is not None
is_mtp = name.startswith("model.mtp.") or \
((m := re.match(r"model\.layers\.(\d+)\.", name)) is not None and int(m.group(1)) >= cls._n_main_layers)
# --no-mtp: drop the NextN/MTP block; --mtp: keep only that block plus the shared embeddings/norm/lm_head
if is_mtp and cls.no_mtp:
return None
if cls.mtp_only and not is_mtp and name not in (
"model.embed_tokens.weight", "model.norm.weight", "lm_head.weight",
):
return None
return name, gen
def set_gguf_parameters(self):
hparams = self.hparams
# head_count is a per-layer array because the two layer kinds have different head counts
n_layer = hparams["num_hidden_layers"]
hparams["num_attention_heads"] = [
hparams["swa_num_attention_heads"] if self._is_swa_layer(il) else hparams["num_attention_heads"]
for il in range(self.block_count)
]
# prevent the base class from emitting key/value_length from the unused head_dim
hparams.pop("head_dim", None)
super().set_gguf_parameters()
# MLA geometry of the sliding-window layers (rope.freq_base_swa is emitted by the base class)
swa_kv_lora_rank = hparams["swa_kv_lora_rank"]
self.gguf_writer.add_sliding_window(hparams["sliding_window_size"])
self.gguf_writer.add_sliding_window_pattern([self._is_swa_layer(il) for il in range(n_layer)])
self.gguf_writer.add_kv_lora_rank_swa(swa_kv_lora_rank)
self.gguf_writer.add_key_length_swa(swa_kv_lora_rank + hparams["swa_qk_rope_head_dim"])
self.gguf_writer.add_value_length_swa(swa_kv_lora_rank)
self.gguf_writer.add_key_length_mla_swa(hparams["swa_qk_nope_head_dim"] + hparams["swa_qk_rope_head_dim"])
self.gguf_writer.add_value_length_mla_swa(hparams["swa_v_head_dim"])
if hparams["swa_q_lora_rank"] != hparams["q_lora_rank"]:
raise ValueError("dots3-note conversion assumes a shared q_lora_rank for both layer kinds")
if self.n_nextn:
self.gguf_writer.add_nextn_predict_layers(self.n_nextn)
# DSA indexer (full-attention layers only)
self.gguf_writer.add_indexer_head_count(hparams["index_n_heads"])
self.gguf_writer.add_indexer_key_length(hparams["index_head_dim"])
self.gguf_writer.add_indexer_top_k(hparams["index_topk"])
self.gguf_writer.add_indexer_types([not self._is_swa_layer(il) for il in range(n_layer)])
def prepare_metadata(self, vocab_only: bool):
from_dir = self.fname_out.is_dir()
super().prepare_metadata(vocab_only=vocab_only)
if not self.mtp_only or not from_dir:
return
output_type: str = self.ftype.name.partition("_")[2]
fname_default: str = gguf.naming_convention(
self.metadata.name, self.metadata.basename, self.metadata.finetune,
self.metadata.version, size_label=None, output_type=output_type, model_type=None)
self.fname_out = self.fname_out.parent / f"mtp-{fname_default}.gguf"
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# move the MTP token embedding into the NextN block so the standard nextn mapping picks it up
if name == "model.mtp.embed_tokens.weight":
name = f"model.layers.{self.hparams['num_hidden_layers']}.embed_tokens.weight"
bid = self.hparams["num_hidden_layers"]
# fold the activation rescale sqrt(n_embd/lora_rank) into the preceding RMSNorm weight
# this also covers the indexer wq_b, which reads the same rescaled q_lora activation
if self.apply_lora_rescale and bid is not None:
if name.endswith("q_a_layernorm.weight"):
data_torch = data_torch * math.sqrt(self.hparams["hidden_size"] / self.hparams["q_lora_rank"])
elif name.endswith("kv_a_layernorm.weight"):
rank = self.hparams["swa_kv_lora_rank"] if self._is_swa_layer(bid) else self.hparams["kv_lora_rank"]
data_torch = data_torch * math.sqrt(self.hparams["hidden_size"] / rank)
# MLA absorption: split kv_b_proj into k_b (transposed) and v_b, per-layer-kind geometry
if name.endswith("kv_b_proj.weight"):
assert bid is not None
if self._is_swa_layer(bid):
n_head = self.hparams["swa_num_attention_heads"]
qk_nope_head_dim = self.hparams["swa_qk_nope_head_dim"]
v_head_dim = self.hparams["swa_v_head_dim"]
else:
n_head = self.hparams["num_attention_heads"]
qk_nope_head_dim = self.hparams["qk_nope_head_dim"]
v_head_dim = self.hparams["v_head_dim"]
if isinstance(n_head, list): # set_gguf_parameters turns this into a per-layer array
n_head = n_head[bid]
assert data_torch.shape[0] == n_head * (qk_nope_head_dim + v_head_dim)
kv_b = data_torch.view(n_head, qk_nope_head_dim + v_head_dim, data_torch.shape[-1])
k_b, v_b = kv_b.split([qk_nope_head_dim, v_head_dim], dim=1)
k_b = k_b.transpose(1, 2)
yield from ModelBase.modify_tensors(self, k_b, name.replace("kv_b_proj", "k_b_proj"), bid)
yield from ModelBase.modify_tensors(self, v_b, name.replace("kv_b_proj", "v_b_proj"), bid)
return
yield from super().modify_tensors(data_torch, name, bid)
@ModelBase.register("Dots3NoteForCausalLM", "Dots3NoteForConditionalGeneration")
class Dots3NoteMmprojModel(MmprojModel):
has_vision_encoder = True
has_audio_encoder = True
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
assert self.hparams_vision is not None
assert self.hparams_audio is not None
# preprocessor_config.json nests the image params under vision_config
self.preprocessor_config = {**self.preprocessor_config, **self.preprocessor_config.get("vision_config", {})}
vis = self.hparams_vision
# in this config, hidden_size is the adapter output width; embed_dim is the tower width
vis["hidden_size"] = vis["embed_dim"]
vis["image_size"] = 0 # dynamic resolution
self.pyramid = [max(0, n) for n in vis["pyramid_num_routed"]]
if vis.get("adapter_type") != "patch_merger" or not vis.get("pre_pixel_shuffle"):
raise ValueError("dots3-note vision conversion requires adapter_type=patch_merger and pre_pixel_shuffle")
if vis.get("router_scoring_func", "sigmoid") != "sigmoid" or vis.get("router_scale", 1.0) != 1.0:
raise ValueError("dots3-note vision conversion only supports sigmoid routing with router_scale=1.0")
if vis.get("temporal_patch_size", 1) != 1 or vis.get("use_bias") or not vis.get("use_qk_norm"):
raise ValueError("unsupported dots3-note vision config variant")
aud = self.hparams_audio
if not aud.get("use_conv2d_stem") or not aud.get("use_rope") or not aud.get("use_rms_norm") or aud.get("use_causal"):
raise ValueError("unsupported dots3-note audio config variant")
if aud["whisper_config"].get("activation_function") != "swiglu":
raise ValueError("dots3-note audio conversion requires the swiglu activation")
if aud.get("merge_factor", 1) != 1 or aud.get("chunk_seconds") != 60:
raise ValueError("unsupported dots3-note audio chunking config")
# the graph hard-codes these rope parameters
rope = aud.get("rope_parameters", {})
if rope.get("partial_rotary_factor") != 0.5 or rope.get("rope_theta") != 10000.0:
raise ValueError("unsupported dots3-note audio rope config")
def get_audio_config(self) -> dict[str, Any] | None:
cfg = self.global_config.get("audio_config")
if cfg is not None:
# aliases so MmprojModel.find_aparam() / n_block_keys can resolve them
whisper = cfg["whisper_config"]
cfg["hidden_size"] = whisper["d_model"]
cfg["intermediate_size"] = whisper["encoder_ffn_dim"]
cfg["num_attention_heads"] = whisper["encoder_attention_heads"]
cfg["num_hidden_layers"] = whisper["encoder_layers"]
return cfg
def set_gguf_parameters(self):
super().set_gguf_parameters()
assert self.hparams_vision is not None
assert self.hparams_audio is not None
self.gguf_writer.add_clip_vision_projector_type(gguf.VisionProjectorType.DOTS3NOTE_V)
self.gguf_writer.add_vision_use_silu(True)
self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams_vision["rms_norm_eps"])
self.gguf_writer.add_vision_spatial_merge_size(self.hparams_vision["spatial_merge_size"])
self.gguf_writer.add_vision_min_pixels(self.preprocessor_config["min_pixels"])
self.gguf_writer.add_vision_max_pixels(self.preprocessor_config["max_pixels"])
# pyramid MoE: per-block routed expert count, 0 = dense block
self.gguf_writer.add_vision_expert_count_per_layer(self.pyramid)
self.gguf_writer.add_vision_expert_used_count(int(self.hparams_vision["capacity_factor"]))
self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.DOTS3NOTE_A)
self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["whisper_config"]["num_mel_bins"])
self.gguf_writer.add_audio_attention_layernorm_eps(1e-6) # Dots3NoteAudioRMSNorm default
@classmethod
def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
name, _ = item
if not name.startswith(("vision_encoder.", "audio_encoder.")):
return None
return super().filter_tensors(item)
_vis_experts: dict[int, dict[str, Tensor]] | None = None
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# router params have no .weight suffix in the checkpoint, but gguf tools expect one
if name.endswith((".gate_weight", ".router_bias")):
name += ".weight"
# audio fc1 fuses gate and up for swiglu; split it
if ".speech_encoder.layers." in name and ".fc1." in name:
gate, up = data_torch.chunk(2, dim=0)
yield from super().modify_tensors(gate, name.replace(".fc1.", ".fc1_gate."), bid)
yield from super().modify_tensors(up, name.replace(".fc1.", ".fc1_up."), bid)
return
# vision MoE: stack per-expert weights into a single 3D tensor per block
if ".mlp.experts." in name:
assert bid is not None
n_expert = self.pyramid[bid]
if self._vis_experts is None:
self._vis_experts = {}
buf = self._vis_experts.setdefault(bid, {})
buf[name] = data_torch
if len(buf) >= n_expert * 3:
for w_name in ("fc1", "fc2", "fc3"):
datas: list[Tensor] = []
for xid in range(n_expert):
ename = f"vision_encoder.blocks.{bid}.mlp.experts.{xid}.{w_name}.weight"
datas.append(buf.pop(ename))
merged = torch.stack(datas, dim=0)
yield from super().modify_tensors(merged, f"vision_encoder.blocks.{bid}.mlp.experts.{w_name}.weight", bid)
return
yield from super().modify_tensors(data_torch, name, bid)
def prepare_tensors(self):
super().prepare_tensors()
if self._vis_experts is not None:
leftover = [k for d in self._vis_experts.values() for k in d.keys()]
if leftover:
raise ValueError(f"unprocessed vision experts: {leftover}")
def tensor_force_quant(self, name, new_name, bid, n_dims):
# FP32 routing is load-bearing for the vision MoE (near-tied expert scores)
if ".ffn_gate_inp." in new_name or ".exp_probs_b." in new_name:
return gguf.GGMLQuantizationType.F32
if ".conv2d" in new_name or "a.conv_out" in new_name:
return gguf.GGMLQuantizationType.F32
return super().tensor_force_quant(name, new_name, bid, n_dims)