From 54ee5ee643f29abba6852903ddfdb688c2361b5b Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Sat, 22 Aug 2026 10:35:50 +0200 Subject: [PATCH] mtmd: support dots3-note vision+audio (#27524) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * text: conversion * init impl * mtmd: conversion * impl mtmd cpp * Update gguf-py/gguf/tensor_mapping.py Co-authored-by: Sigbjørn Skjæret --------- Co-authored-by: Sigbjørn Skjæret --- conversion/__init__.py | 2 + conversion/dots3.py | 132 +++++++++++++++++++++++++++++++- gguf-py/gguf/constants.py | 19 +++++ gguf-py/gguf/gguf_writer.py | 6 ++ gguf-py/gguf/tensor_mapping.py | 55 ++++++++++++- tools/mtmd/CMakeLists.txt | 1 + tools/mtmd/clip-graph.h | 6 ++ tools/mtmd/clip-impl.h | 11 ++- tools/mtmd/clip-model.h | 8 ++ tools/mtmd/clip.cpp | 129 +++++++++++++++++++++++++++++-- tools/mtmd/models/dots3note.cpp | 61 +++++++++++++++ tools/mtmd/models/models.h | 5 ++ tools/mtmd/mtmd-audio.cpp | 94 +++++++++++++++++++++++ tools/mtmd/mtmd-audio.h | 9 +++ tools/mtmd/mtmd.cpp | 8 ++ 15 files changed, 535 insertions(+), 11 deletions(-) create mode 100644 tools/mtmd/models/dots3note.cpp diff --git a/conversion/__init__.py b/conversion/__init__.py index b4afdf0f8..aaaebba97 100644 --- a/conversion/__init__.py +++ b/conversion/__init__.py @@ -282,6 +282,8 @@ MMPROJ_MODEL_MAP: dict[str, str] = { "CogVLMForCausalLM": "cogvlm", "DeepseekOCR2ForCausalLM": "deepseek", "DeepseekOCRForCausalLM": "deepseek", + "Dots3NoteForCausalLM": "dots3", + "Dots3NoteForConditionalGeneration": "dots3", "DotsOCRForCausalLM": "dotsocr", "Exaone4_5_ForConditionalGeneration": "exaone", "Gemma3ForConditionalGeneration": "gemma", diff --git a/conversion/dots3.py b/conversion/dots3.py index 7c36b8482..c7ac2319e 100644 --- a/conversion/dots3.py +++ b/conversion/dots3.py @@ -3,12 +3,14 @@ from __future__ import annotations import math import re -from typing import TYPE_CHECKING, Callable, Iterable +import torch + +from typing import TYPE_CHECKING, Any, Callable, Iterable if TYPE_CHECKING: from torch import Tensor -from .base import ModelBase, gguf +from .base import MmprojModel, ModelBase, gguf from .deepseek import DeepseekV2Model @@ -193,3 +195,129 @@ class Dots3NoteModel(DeepseekV2Model): 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) diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 886253d88..8f6f55519 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -364,6 +364,8 @@ class Keys: IMAGE_MEAN = "clip.vision.image_mean" IMAGE_STD = "clip.vision.image_std" SPATIAL_MERGE_SIZE = "clip.vision.spatial_merge_size" + EXPERT_COUNT_PER_LAYER = "clip.vision.expert_count_per_layer" # dots3note pyramid MoE, 0 = dense layer + EXPERT_USED_COUNT = "clip.vision.expert_used_count" USE_GELU = "clip.use_gelu" USE_SILU = "clip.use_silu" N_WA_PATTERN = "clip.vision.n_wa_pattern" # used by qwen2.5vl @@ -874,6 +876,11 @@ class MODEL_TENSOR(IntEnum): V_ENC_FFN_UP = auto() V_ENC_FFN_GATE = auto() V_ENC_FFN_DOWN = auto() + V_ENC_FFN_GATE_INP = auto() # dots3note vision MoE router + V_ENC_FFN_GATE_EXPS = auto() + V_ENC_FFN_UP_EXPS = auto() + V_ENC_FFN_DOWN_EXPS = auto() + V_ENC_FFN_EXP_PROBS_B = auto() V_ENC_ATTN_POST_NORM = auto() # gemma4 V_ENC_FFN_POST_NORM = auto() V_LAYER_SCALE_1 = auto() @@ -1591,6 +1598,11 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = { MODEL_TENSOR.V_ENC_FFN_UP: "v.blk.{bid}.ffn_up", MODEL_TENSOR.V_ENC_FFN_GATE: "v.blk.{bid}.ffn_gate", MODEL_TENSOR.V_ENC_FFN_DOWN: "v.blk.{bid}.ffn_down", + MODEL_TENSOR.V_ENC_FFN_GATE_INP: "v.blk.{bid}.ffn_gate_inp", + MODEL_TENSOR.V_ENC_FFN_GATE_EXPS: "v.blk.{bid}.ffn_gate_exps", + MODEL_TENSOR.V_ENC_FFN_UP_EXPS: "v.blk.{bid}.ffn_up_exps", + MODEL_TENSOR.V_ENC_FFN_DOWN_EXPS: "v.blk.{bid}.ffn_down_exps", + MODEL_TENSOR.V_ENC_FFN_EXP_PROBS_B: "v.blk.{bid}.exp_probs_b", MODEL_TENSOR.V_ENC_ATTN_POST_NORM: "v.blk.{bid}.attn_post_norm", MODEL_TENSOR.V_ENC_FFN_POST_NORM: "v.blk.{bid}.ffn_post_norm", MODEL_TENSOR.V_LAYER_SCALE_1: "v.blk.{bid}.ls1", @@ -1913,6 +1925,11 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.V_ENC_FFN_UP, MODEL_TENSOR.V_ENC_FFN_GATE, MODEL_TENSOR.V_ENC_FFN_DOWN, + MODEL_TENSOR.V_ENC_FFN_GATE_INP, + MODEL_TENSOR.V_ENC_FFN_GATE_EXPS, + MODEL_TENSOR.V_ENC_FFN_UP_EXPS, + MODEL_TENSOR.V_ENC_FFN_DOWN_EXPS, + MODEL_TENSOR.V_ENC_FFN_EXP_PROBS_B, MODEL_TENSOR.V_ENC_ATTN_POST_NORM, MODEL_TENSOR.V_ENC_FFN_POST_NORM, MODEL_TENSOR.V_LAYER_SCALE_1, @@ -5497,6 +5514,8 @@ class VisionProjectorType: COGVLM = "cogvlm" JANUS_PRO = "janus_pro" DOTSOCR = "dots_ocr" + DOTS3NOTE_V = "dots3note_v" + DOTS3NOTE_A = "dots3note_a" # audio DEEPSEEKOCR = "deepseekocr" DEEPSEEKOCR2 = "deepseekocr2" LFM2A = "lfm2a" # audio diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 496882025..d8a96a27b 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -1327,6 +1327,12 @@ class GGUFWriter: def add_vision_spatial_merge_size(self, value: int) -> None: self.add_uint32(Keys.ClipVision.SPATIAL_MERGE_SIZE, value) + def add_vision_expert_count_per_layer(self, value: Sequence[int]) -> None: + self.add_array(Keys.ClipVision.EXPERT_COUNT_PER_LAYER, value) + + def add_vision_expert_used_count(self, value: int) -> None: + self.add_uint32(Keys.ClipVision.EXPERT_USED_COUNT, value) + def add_vision_use_gelu(self, value: bool) -> None: self.add_bool(Keys.ClipVision.USE_GELU, value) diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 1ff4b61d9..ef580518e 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -1454,6 +1454,7 @@ class TensorNameMap: "mlp_AR.linear_{bid}", # PaddleOCR-VL "merger.mlp.{bid}", "vision_tower.merger.mlp.{bid}", # dots.ocr + "vision_encoder.adapter.mlp.{bid}", # dots3note "vit.perceive.proj.{bid}", # HunyuanVL (proj.0 = conv1, proj.2 = conv2) ), @@ -1504,6 +1505,7 @@ class TensorNameMap: "vision_model.radio_model.model.patch_generator.embedder", # Nemotron Nano v2 VL "model.vision_tower.patch_embedder.input_proj", # gemma4 "vision_tower.patch_embed.patchifier.proj", # dots.ocr + "vision_encoder.patch_embed.proj", # dots3note "vision_model.conv1", # Step3-VL "model.vision_embedder.patch_dense", # gemma4 unified "model.vision_tower.patch_embedder.patch_embedding", # muse-glimmer @@ -1512,6 +1514,7 @@ class TensorNameMap: MODEL_TENSOR.V_ENC_EMBD_NORM: ( "visual.post_conv_layernorm", # glm4v "vision_tower.patch_embed.patchifier.norm", # dots.ocr + "vision_encoder.patch_embed.norm", # dots3note ), MODEL_TENSOR.V_ENC_EMBD_PATCH_NORM: ( @@ -1551,6 +1554,7 @@ class TensorNameMap: MODEL_TENSOR.V_ENC_ATTN_QKV: ( "visual.blocks.{bid}.attn.qkv", # qwen3vl "vision_tower.blocks.{bid}.attn.qkv", # dots.ocr + "vision_encoder.blocks.{bid}.attn.qkv", # dots3note "model.vision.transformer.layers.{bid}.attention.query_key_value", # cogvlm "model.vision_model.transformer.layers.{bid}.self_attn.qkv_proj", # Deepseek-OCR CLIP "vision_tower.encoder.blocks.{bid}.wqkv", # Kimi-K2.5 @@ -1579,6 +1583,7 @@ class TensorNameMap: ), MODEL_TENSOR.V_ENC_ATTN_Q_NORM: ( + "vision_encoder.blocks.{bid}.attn.q_norm", # dots3note "vision_tower.vision_model.encoder.layers.{bid}.attn.q_norm", # InternVL "model.vision_tower.encoder.layer.{bid}.attention.q_norm", # Intern-S1 "visual.blocks.{bid}.attn.q_norm", # GLM-OCR @@ -1606,6 +1611,7 @@ class TensorNameMap: ), MODEL_TENSOR.V_ENC_ATTN_K_NORM: ( + "vision_encoder.blocks.{bid}.attn.k_norm", # dots3note "vision_tower.vision_model.encoder.layers.{bid}.attn.k_norm", # InternVL "model.vision_tower.encoder.layer.{bid}.attention.k_norm", # Intern-S1 "visual.blocks.{bid}.attn.k_norm", # GLM-OCR @@ -1651,6 +1657,7 @@ class TensorNameMap: "siglip2.vision_model.encoder.layers.{bid}.layer_norm1", "vision_model.radio_model.model.blocks.{bid}.norm1", # Nemotron Nano v2 VL "vision_tower.blocks.{bid}.norm1", # dots.ocr + "vision_encoder.blocks.{bid}.norm_1", # dots3note "vision_model.transformer.resblocks.{bid}.ln_1", # Step3-VL "model.qwen2_model.model.model.layers.{bid}.input_layernorm", # Deepseek-OCR-2 qwen2 "model.vision_tower.layers.{bid}.norm1", # muse-glimmer @@ -1678,6 +1685,7 @@ class TensorNameMap: "model.qwen2_model.model.model.layers.{bid}.self_attn.o_proj", # Deepseek-OCR-2 qwen2 "vision_model.model.layers.{bid}.self_attn.o_proj.linear", # gemma4 "vision_tower.blocks.{bid}.attn.proj", # dots.ocr + "vision_encoder.blocks.{bid}.attn.proj", # dots3note "vision_model.transformer.resblocks.{bid}.attn.out_proj", # Step3-VL "model.vision_tower.layers.{bid}.attn.proj", # muse-glimmer ), @@ -1706,12 +1714,14 @@ class TensorNameMap: "vision_model.radio_model.model.blocks.{bid}.norm2", # Nemotron Nano v2 VL "vision_model.model.layers.{bid}.pre_feedforward_layernorm", # gemma4 "vision_tower.blocks.{bid}.norm2", # dots.ocr + "vision_encoder.blocks.{bid}.norm_2", # dots3note "vision_model.transformer.resblocks.{bid}.ln_2", # Step3-VL "model.qwen2_model.model.model.layers.{bid}.post_attention_layernorm", # Deepseek-OCR-2 qwen2 "model.vision_tower.layers.{bid}.norm2", # muse-glimmer ), MODEL_TENSOR.V_ENC_FFN_UP: ( + "vision_encoder.blocks.{bid}.mlp.fc3", # dots3note "model.vision_tower.vision_model.encoder.layers.{bid}.mlp.fc1", # Granite4Vision "vision_tower.vision_model.encoder.layers.{bid}.mlp.fc1", "model.vision_tower.encoder.layers.{bid}.mlp.fc1", # minicpmv4_6 @@ -1737,6 +1747,7 @@ class TensorNameMap: ), MODEL_TENSOR.V_ENC_FFN_GATE: ( + "vision_encoder.blocks.{bid}.mlp.fc1", # dots3note "vision_tower.transformer.layers.{bid}.feed_forward.gate_proj", # pixtral-hf "vision_encoder.transformer.layers.{bid}.feed_forward.w1", # pixtral "visual.blocks.{bid}.mlp.gate_proj", # qwen2.5vl @@ -1745,6 +1756,7 @@ class TensorNameMap: ), MODEL_TENSOR.V_ENC_FFN_DOWN: ( + "vision_encoder.blocks.{bid}.mlp.fc2", # dots3note "model.vision_tower.vision_model.encoder.layers.{bid}.mlp.fc2", # Granite4Vision "vision_tower.vision_model.encoder.layers.{bid}.mlp.fc2", "model.vision_tower.encoder.layers.{bid}.mlp.fc2", # minicpmv4_6 @@ -1769,6 +1781,29 @@ class TensorNameMap: "model.vision_tower.layers.{bid}.mlp.fc2", # muse-glimmer ), + + MODEL_TENSOR.V_ENC_FFN_GATE_INP: ( + "vision_encoder.blocks.{bid}.mlp.gate_weight", # dots3note + ), + + MODEL_TENSOR.V_ENC_FFN_EXP_PROBS_B: ( + "vision_encoder.blocks.{bid}.mlp.router_bias", # dots3note + ), + + # note: expert weights are stacked into a single 3D tensor in conversion code, + # which emits the pseudo-names below + MODEL_TENSOR.V_ENC_FFN_GATE_EXPS: ( + "vision_encoder.blocks.{bid}.mlp.experts.fc1", # dots3note + ), + + MODEL_TENSOR.V_ENC_FFN_UP_EXPS: ( + "vision_encoder.blocks.{bid}.mlp.experts.fc3", # dots3note + ), + + MODEL_TENSOR.V_ENC_FFN_DOWN_EXPS: ( + "vision_encoder.blocks.{bid}.mlp.experts.fc2", # dots3note + ), + MODEL_TENSOR.V_ENC_ATTN_POST_NORM: ( "vision_model.model.layers.{bid}.post_attention_layernorm", # gemma4 ), @@ -1800,6 +1835,7 @@ class TensorNameMap: "vision_model.layernorm_pre", # llama4 "model.vision_model.pre_layrnorm", # Deepseek-OCR CLIP "vision_tower.patch_embed.patchifier.norm", # dots.ocr + "vision_encoder.patch_embed.norm", # dots3note "vision_model.ln_pre", # Step3-VL "model.vision_tower.ln_pre", # muse-glimmer ), @@ -1821,6 +1857,7 @@ class TensorNameMap: MODEL_TENSOR.V_MM_POST_NORM: ( "visual.merger.post_projection_norm", # glm4v "vision_tower.post_trunk_norm", # dots.ocr + "vision_encoder.post_trunk_norm", # dots3note "vit.perceive.after_rms", # HunyuanVL ), @@ -1838,6 +1875,7 @@ class TensorNameMap: "mlp_AR.pre_norm", # PaddleOCR-VL "merger.ln_q", "vision_tower.merger.ln_q", # dots.ocr + "vision_encoder.adapter.ln_q", # dots3note "model.merger.mlp.0.pre_norm", # minicpmv4_6 ), @@ -2173,10 +2211,12 @@ class TensorNameMap: MODEL_TENSOR.A_ENC_CONV2D: ( "audio_tower.conv2d{bid}", # qwen3omni + "audio_encoder.dots_encoder.speech_encoder.conv2d{bid}", # dots3note ), MODEL_TENSOR.A_ENC_CONV_OUT: ( "audio_tower.conv_out", # qwen3omni + "audio_encoder.dots_encoder.speech_encoder.conv_out", # dots3note "speaker_encoder.mfa.conv", # qwen3tts speaker encoder: multi-layer feature aggregation ), @@ -2184,12 +2224,14 @@ class TensorNameMap: MODEL_TENSOR.A_POST_NORM: ( "audio_tower.layer_norm", # ultravox + "audio_encoder.dots_encoder.speech_encoder.layer_norm", # dots3note "audio_tower.ln_post", # qwen2omni "encoder.layer_norm", # mimo-audio-tokenizer ), MODEL_TENSOR.A_ENC_ATTN_Q: ( "audio_tower.layers.{bid}.self_attn.q_proj", # ultravox + "audio_encoder.dots_encoder.speech_encoder.layers.{bid}.self_attn.q_proj", # dots3note "conformer.layers.{bid}.self_attn.linear_q", # lfm2 "conformer.layers.{bid}.attention.attn.q_proj", # gemma3n "conformer.layers.{bid}.self_attn.q_proj", # gemma4 @@ -2200,6 +2242,7 @@ class TensorNameMap: MODEL_TENSOR.A_ENC_ATTN_K: ( "audio_tower.layers.{bid}.self_attn.k_proj", # ultravox + "audio_encoder.dots_encoder.speech_encoder.layers.{bid}.self_attn.k_proj", # dots3note "conformer.layers.{bid}.self_attn.linear_k", # lfm2 "conformer.layers.{bid}.attention.attn.k_proj", # gemma3n "conformer.layers.{bid}.self_attn.k_proj", # gemma4 @@ -2210,6 +2253,7 @@ class TensorNameMap: MODEL_TENSOR.A_ENC_ATTN_V: ( "audio_tower.layers.{bid}.self_attn.v_proj", # ultravox + "audio_encoder.dots_encoder.speech_encoder.layers.{bid}.self_attn.v_proj", # dots3note "conformer.layers.{bid}.self_attn.linear_v", # lfm2 "conformer.layers.{bid}.attention.attn.v_proj", # gemma3n "conformer.layers.{bid}.self_attn.v_proj", # gemma4 @@ -2241,6 +2285,7 @@ class TensorNameMap: MODEL_TENSOR.A_ENC_INPUT_NORM: ( "audio_tower.layers.{bid}.self_attn_layer_norm", # ultravox + "audio_encoder.dots_encoder.speech_encoder.layers.{bid}.self_attn_layer_norm", # dots3note "conformer.layers.{bid}.norm_self_att", # lfm2 "conformer.layers.{bid}.attention.pre_attn_norm", # gemma3n "sound_encoder.encoder.layers.{bid}.norm_self_att", # parakeet @@ -2250,6 +2295,7 @@ class TensorNameMap: MODEL_TENSOR.A_ENC_OUTPUT: ( "audio_tower.layers.{bid}.self_attn.out_proj", # ultravox + "audio_encoder.dots_encoder.speech_encoder.layers.{bid}.self_attn.out_proj", # dots3note "conformer.layers.{bid}.self_attn.linear_out", # lfm2 "conformer.layers.{bid}.attention.post", # gemma3n "conformer.layers.{bid}.self_attn.post", # gemma4 @@ -2260,6 +2306,7 @@ class TensorNameMap: MODEL_TENSOR.A_ENC_OUTPUT_NORM: ( "audio_tower.layers.{bid}.final_layer_norm", # ultravox + "audio_encoder.dots_encoder.speech_encoder.layers.{bid}.final_layer_norm", # dots3note "conformer.layers.{bid}.norm_out", # lfm2 "conformer.layers.{bid}.attention.post_norm", # gemma3n "sound_encoder.encoder.layers.{bid}.norm_out", # parakeet @@ -2285,6 +2332,7 @@ class TensorNameMap: ), MODEL_TENSOR.A_ENC_FFN_UP: ( + "audio_encoder.dots_encoder.speech_encoder.layers.{bid}.fc1_up", # dots3note (split from fc1 in conversion code) "audio_tower.layers.{bid}.fc1", # ultravox "conformer.layers.{bid}.feed_forward1.linear1", # lfm2 "conformer.layers.{bid}.ffw_layer_start.ffw_layer_1", # gemma3n @@ -2294,9 +2342,12 @@ class TensorNameMap: "encoder.layers.{bid}.fc1", # mimo-audio-tokenizer ), - MODEL_TENSOR.A_ENC_FFN_GATE: (), + MODEL_TENSOR.A_ENC_FFN_GATE: ( + "audio_encoder.dots_encoder.speech_encoder.layers.{bid}.fc1_gate", # dots3note (split from fc1 in conversion code) + ), MODEL_TENSOR.A_ENC_FFN_DOWN: ( + "audio_encoder.dots_encoder.speech_encoder.layers.{bid}.fc2", # dots3note "audio_tower.layers.{bid}.fc2", # ultravox "conformer.layers.{bid}.feed_forward1.linear2", # lfm2 "conformer.layers.{bid}.ffw_layer_start.ffw_layer_2", # gemma3n @@ -2380,6 +2431,7 @@ class TensorNameMap: MODEL_TENSOR.A_MMPROJ: ( "audio.multi_modal_projector.linear_{bid}", # ultravox, meralion + "audio_encoder.audio_adapter.proj.{bid}", # dots3note (proj.1, proj.3) "audio_adapter.model.{bid}", # lfm2 "audio_tower.proj{bid}", # qwen3omni "sound_projection.linear{bid}", # parakeet (linear1, linear2) @@ -2394,6 +2446,7 @@ class TensorNameMap: MODEL_TENSOR.A_MM_NORM_PRE: ( "audio.multi_modal_projector.ln_pre", # ultravox + "audio_encoder.audio_adapter.proj.0", # dots3note "sound_projection.norm", # parakeet ), diff --git a/tools/mtmd/CMakeLists.txt b/tools/mtmd/CMakeLists.txt index 63a8b17a5..e60c9c878 100644 --- a/tools/mtmd/CMakeLists.txt +++ b/tools/mtmd/CMakeLists.txt @@ -30,6 +30,7 @@ add_library(mtmd models/models.h models/cogvlm.cpp models/conformer.cpp + models/dots3note.cpp models/dotsocr.cpp models/exaone4_5.cpp models/gemma4a.cpp diff --git a/tools/mtmd/clip-graph.h b/tools/mtmd/clip-graph.h index e12140ba0..2cf1b683a 100644 --- a/tools/mtmd/clip-graph.h +++ b/tools/mtmd/clip-graph.h @@ -120,6 +120,12 @@ struct clip_graph { ffn_op_type type_op, int il) const; + ggml_tensor * build_moe_ffn( + ggml_tensor * cur, + const clip_layer & layer, + ffn_op_type type_op, + int il) const; + ggml_tensor * build_attn( ggml_tensor * wo, ggml_tensor * wo_b, diff --git a/tools/mtmd/clip-impl.h b/tools/mtmd/clip-impl.h index ea8549776..f6045093c 100644 --- a/tools/mtmd/clip-impl.h +++ b/tools/mtmd/clip-impl.h @@ -75,6 +75,7 @@ #define KEY_SAM_N_HEAD "clip.vision.sam.head_count" #define KEY_SAM_N_BLOCK "clip.vision.sam.block_count" #define KEY_SAM_N_EMBD "clip.vision.sam.embedding_length" +#define KEY_VISION_N_EXPERT_USED "clip.vision.expert_used_count" // audio-specific #define KEY_AUDIO_PROJ_TYPE "clip.audio.projector_type" // for models with mixed modalities #define KEY_A_NUM_MEL_BINS "clip.audio.num_mel_bins" @@ -119,7 +120,11 @@ #define TN_FFN_DOWN "%s.blk.%d.ffn_down.%s" #define TN_FFN_GATE "%s.blk.%d.ffn_gate.%s" #define TN_FFN_UP "%s.blk.%d.ffn_up.%s" -#define TN_FFN_GATE "%s.blk.%d.ffn_gate.%s" +#define TN_FFN_GATE_INP "%s.blk.%d.ffn_gate_inp.%s" // MoE router (dots3note) +#define TN_FFN_GATE_EXPS "%s.blk.%d.ffn_gate_exps.%s" +#define TN_FFN_UP_EXPS "%s.blk.%d.ffn_up_exps.%s" +#define TN_FFN_DOWN_EXPS "%s.blk.%d.ffn_down_exps.%s" +#define TN_FFN_EXP_PROBS_B "%s.blk.%d.exp_probs_b.%s" #define TN_LN_1 "%s.blk.%d.ln1.%s" // layer norm #define TN_LN_2 "%s.blk.%d.ln2.%s" // layer norm #define TN_LS_1 "%s.blk.%d.ls1.%s" // layer scale @@ -471,6 +476,8 @@ enum projector_type { PROJECTOR_TYPE_COGVLM, PROJECTOR_TYPE_JANUS_PRO, PROJECTOR_TYPE_DOTS_OCR, + PROJECTOR_TYPE_DOTS3NOTE_V, + PROJECTOR_TYPE_DOTS3NOTE_A, PROJECTOR_TYPE_DEEPSEEKOCR, PROJECTOR_TYPE_DEEPSEEKOCR2, PROJECTOR_TYPE_LFM2A, @@ -533,6 +540,8 @@ static std::map PROJECTOR_TYPE_NAMES = { { PROJECTOR_TYPE_COGVLM, "cogvlm"}, { PROJECTOR_TYPE_JANUS_PRO, "janus_pro"}, { PROJECTOR_TYPE_DOTS_OCR, "dots_ocr"}, + { PROJECTOR_TYPE_DOTS3NOTE_V, "dots3note_v"}, + { PROJECTOR_TYPE_DOTS3NOTE_A, "dots3note_a"}, { PROJECTOR_TYPE_DEEPSEEKOCR, "deepseekocr"}, { PROJECTOR_TYPE_DEEPSEEKOCR2, "deepseekocr2"}, { PROJECTOR_TYPE_LFM2A, "lfm2a"}, diff --git a/tools/mtmd/clip-model.h b/tools/mtmd/clip-model.h index ad25c008e..fcdabd633 100644 --- a/tools/mtmd/clip-model.h +++ b/tools/mtmd/clip-model.h @@ -93,6 +93,7 @@ struct clip_hparams { float eps = 1e-6; float rope_theta = 0.0; + int32_t n_expert_used = 0; std::vector feature_layers; int32_t attn_window_size = 0; int32_t n_wa_pattern = 0; @@ -259,6 +260,13 @@ struct clip_layer { ggml_tensor * ff_down_w = nullptr; ggml_tensor * ff_down_b = nullptr; + // MoE FFN (dots3note vision pyramid blocks) + ggml_tensor * ff_gate_inp_w = nullptr; + ggml_tensor * ff_gate_exps_w = nullptr; + ggml_tensor * ff_up_exps_w = nullptr; + ggml_tensor * ff_down_exps_w = nullptr; + ggml_tensor * ff_exp_probs_b = nullptr; + // layernorm 2 (or pre-FFN norm) ggml_tensor * ln_2_w = nullptr; ggml_tensor * ln_2_b = nullptr; diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp index 45e33042d..9977ed490 100644 --- a/tools/mtmd/clip.cpp +++ b/tools/mtmd/clip.cpp @@ -514,11 +514,13 @@ ggml_tensor * clip_graph::build_vit( cb(cur, "ffn_inp_normed", il); // ffn - cur = build_ffn(cur, - layer.ff_up_w, layer.ff_up_b, - layer.ff_gate_w, layer.ff_gate_b, - layer.ff_down_w, layer.ff_down_b, - ffn_t, il); + cur = layer.ff_gate_exps_w + ? build_moe_ffn(cur, layer, ffn_t, il) + : build_ffn(cur, + layer.ff_up_w, layer.ff_up_b, + layer.ff_gate_w, layer.ff_gate_b, + layer.ff_down_w, layer.ff_down_b, + ffn_t, il); cb(cur, "ffn_out", il); @@ -699,6 +701,50 @@ ggml_tensor * clip_graph::build_ffn( return cur; } +// MoE FFN with sigmoid router and normalized top-k weights (dots3note vision) +// the router runs in fp32; exp_probs_b only affects expert selection, not the weights +ggml_tensor * clip_graph::build_moe_ffn(ggml_tensor * cur, const clip_layer & layer, ffn_op_type type_op, int il) const { + const int64_t n_tokens = cur->ne[1]; + const int64_t n_expert = layer.ff_gate_exps_w->ne[2]; + const int64_t n_expert_used = std::min((int64_t) hparams.n_expert_used, n_expert); + GGML_ASSERT(n_expert_used > 0); + GGML_ASSERT(type_op == FFN_SILU); + + ggml_tensor * probs = ggml_sigmoid(ctx0, build_mm(layer.ff_gate_inp_w, cur)); // [n_expert, n_tokens] + cb(probs, "ffn_moe_probs", il); + + ggml_tensor * sel = layer.ff_exp_probs_b + ? ggml_add(ctx0, probs, layer.ff_exp_probs_b) + : probs; + ggml_tensor * selected = ggml_top_k(ctx0, sel, n_expert_used); // [n_expert_used, n_tokens] + + ggml_tensor * weights = ggml_get_rows(ctx0, + ggml_reshape_3d(ctx0, probs, 1, n_expert, n_tokens), selected); + weights = ggml_reshape_2d(ctx0, weights, n_expert_used, n_tokens); + weights = ggml_div(ctx0, weights, ggml_sum_rows(ctx0, weights)); + weights = ggml_reshape_3d(ctx0, weights, 1, n_expert_used, n_tokens); + cb(weights, "ffn_moe_weights", il); + + cur = ggml_reshape_3d(ctx0, cur, cur->ne[0], 1, n_tokens); + ggml_tensor * gate = ggml_mul_mat_id(ctx0, layer.ff_gate_exps_w, cur, selected); // [n_ff, n_expert_used, n_tokens] + ggml_tensor * up = ggml_mul_mat_id(ctx0, layer.ff_up_exps_w, cur, selected); + cur = ggml_mul(ctx0, ggml_silu(ctx0, gate), up); + cur = ggml_mul_mat_id(ctx0, layer.ff_down_exps_w, cur, selected); // [n_embd, n_expert_used, n_tokens] + cur = ggml_mul(ctx0, cur, weights); + + // sum over the selected experts + ggml_tensor * out = nullptr; + for (int64_t i = 0; i < n_expert_used; i++) { + ggml_tensor * v = ggml_view_2d(ctx0, cur, cur->ne[0], n_tokens, cur->nb[2], i * cur->nb[1]); + out = out ? ggml_add(ctx0, out, v) : v; + } + if (n_expert_used == 1) { + out = ggml_cont(ctx0, out); + } + cb(out, "ffn_moe_out", il); + return out; +} + ggml_tensor * clip_graph::build_attn( ggml_tensor * wo, ggml_tensor * wo_b, @@ -933,9 +979,14 @@ static std::unique_ptr clip_get_graph_builder(clip_ctx * ctx, const builder = std::make_unique(ctx, img); } break; case PROJECTOR_TYPE_DOTS_OCR: + case PROJECTOR_TYPE_DOTS3NOTE_V: // same ViT + merger; pyramid MoE is handled by build_vit { builder = std::make_unique(ctx, img); } break; + case PROJECTOR_TYPE_DOTS3NOTE_A: + { + builder = std::make_unique(ctx, img); + } break; case PROJECTOR_TYPE_QWEN2VL: case PROJECTOR_TYPE_QWEN25VL: { @@ -1510,6 +1561,25 @@ struct clip_model_loader { get_u32(KEY_IMAGE_MAX_PIXELS, hparams.image_max_pixels); hparams.set_warmup_n_tokens(46*46); // avoid OOM on warmup } break; + case PROJECTOR_TYPE_DOTS3NOTE_V: + { + hparams.rope_theta = 10000.0f; + hparams.image_resize_algo = RESIZE_ALGO_BICUBIC_PILLOW; + get_u32(KEY_SPATIAL_MERGE_SIZE, hparams.n_merge); + get_u32(KEY_IMAGE_MIN_PIXELS, hparams.image_min_pixels); + get_u32(KEY_IMAGE_MAX_PIXELS, hparams.image_max_pixels); + get_u32(KEY_VISION_N_EXPERT_USED, hparams.n_expert_used); + hparams.set_warmup_n_tokens(46*46); // avoid OOM on warmup + } break; + case PROJECTOR_TYPE_DOTS3NOTE_A: + { + hparams.rope_theta = 10000.0f; + hparams.audio_chunk_len = 60; // in seconds + hparams.audio_sample_rate = 16000; + hparams.audio_n_fft = 400; + hparams.audio_window_len = 400; + hparams.audio_hop_len = 160; + } break; case PROJECTOR_TYPE_KIMIVL: { hparams.image_resize_algo = RESIZE_ALGO_BILINEAR; @@ -2190,12 +2260,20 @@ struct clip_model_loader { layer.ln_1_b = get_tensor(string_format(TN_LN_1, prefix, il, "bias"), false); layer.ln_2_b = get_tensor(string_format(TN_LN_2, prefix, il, "bias"), false); + // MoE ffn (dots3note vision pyramid blocks); replaces the dense ffn when present + layer.ff_gate_inp_w = get_tensor(string_format(TN_FFN_GATE_INP, prefix, il, "weight"), false); + layer.ff_gate_exps_w = get_tensor(string_format(TN_FFN_GATE_EXPS, prefix, il, "weight"), false); + layer.ff_up_exps_w = get_tensor(string_format(TN_FFN_UP_EXPS, prefix, il, "weight"), false); + layer.ff_down_exps_w = get_tensor(string_format(TN_FFN_DOWN_EXPS, prefix, il, "weight"), false); + layer.ff_exp_probs_b = get_tensor(string_format(TN_FFN_EXP_PROBS_B, prefix, il, "weight"), false); + const bool is_moe = layer.ff_gate_exps_w != nullptr; + // ffn - layer.ff_up_w = get_tensor(string_format(TN_FFN_UP, prefix, il, "weight")); + layer.ff_up_w = get_tensor(string_format(TN_FFN_UP, prefix, il, "weight"), !is_moe); layer.ff_up_b = get_tensor(string_format(TN_FFN_UP, prefix, il, "bias"), false); layer.ff_gate_w = get_tensor(string_format(TN_FFN_GATE, prefix, il, "weight"), false); layer.ff_gate_b = get_tensor(string_format(TN_FFN_GATE, prefix, il, "bias"), false); - layer.ff_down_w = get_tensor(string_format(TN_FFN_DOWN, prefix, il, "weight")); + layer.ff_down_w = get_tensor(string_format(TN_FFN_DOWN, prefix, il, "weight"), !is_moe); layer.ff_down_b = get_tensor(string_format(TN_FFN_DOWN, prefix, il, "bias"), false); // mimovl per-head attention sink bias @@ -2677,6 +2755,7 @@ struct clip_model_loader { model.mm_patch_merger_w = get_tensor(string_format(TN_MM_PATCH_MERGER, "weight"), false); } break; case PROJECTOR_TYPE_DOTS_OCR: + case PROJECTOR_TYPE_DOTS3NOTE_V: { model.mm_0_w = get_tensor(string_format(TN_LLAVA_PROJ, 0, "weight")); model.mm_0_b = get_tensor(string_format(TN_LLAVA_PROJ, 0, "bias")); @@ -2687,6 +2766,23 @@ struct clip_model_loader { // post_trunk_norm: applied after all ViT blocks, before the merger model.post_ln_w = get_tensor(string_format(TN_MM_POST_NORM, "weight")); } break; + case PROJECTOR_TYPE_DOTS3NOTE_A: + { + model.conv2d_1_w = get_tensor(string_format(TN_CONV2D, 1, "weight")); + model.conv2d_1_b = get_tensor(string_format(TN_CONV2D, 1, "bias")); + model.conv2d_2_w = get_tensor(string_format(TN_CONV2D, 2, "weight")); + model.conv2d_2_b = get_tensor(string_format(TN_CONV2D, 2, "bias")); + model.conv2d_3_w = get_tensor(string_format(TN_CONV2D, 3, "weight")); + model.conv2d_3_b = get_tensor(string_format(TN_CONV2D, 3, "bias")); + model.conv_out_w = get_tensor(string_format(TN_CONV_OUT, "weight")); // no bias + // adapter: LayerNorm -> Linear -> GELU -> Linear + model.mm_norm_pre_w = get_tensor(string_format(TN_MM_NORM_PRE, "weight")); + model.mm_norm_pre_b = get_tensor(string_format(TN_MM_NORM_PRE, "bias")); + model.mm_1_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 1, "weight")); + model.mm_1_b = get_tensor(string_format(TN_MM_AUDIO_MLP, 1, "bias")); + model.mm_2_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 3, "weight")); + model.mm_2_b = get_tensor(string_format(TN_MM_AUDIO_MLP, 3, "bias")); + } break; case PROJECTOR_TYPE_ULTRAVOX: { model.conv1d_1_w = get_tensor(string_format(TN_CONV1D, 1, "weight")); @@ -4075,12 +4171,18 @@ int clip_n_output_tokens(const clip_ctx * ctx, const clip_image_f32 * img) { } break; case PROJECTOR_TYPE_PADDLEOCR: case PROJECTOR_TYPE_DOTS_OCR: + case PROJECTOR_TYPE_DOTS3NOTE_V: { // dynamic size int n_merge = ctx->model.hparams.n_merge; int stride = n_merge * n_merge; n_patches = CLIP_ALIGN(n_patches, stride) / stride; } break; + case PROJECTOR_TYPE_DOTS3NOTE_A: + { + // 3x stride-2 conv2d over mel frames + n_patches = (img->nx() + 7) / 8; + } break; case PROJECTOR_TYPE_PIXTRAL: case PROJECTOR_TYPE_LIGHTONOCR: { @@ -4727,6 +4829,7 @@ bool clip_encode(struct clip_ctx * ctx, struct clip_encode_params * params) { set_input_i32("minimax_pos_w", pos_w); } break; case PROJECTOR_TYPE_DOTS_OCR: + case PROJECTOR_TYPE_DOTS3NOTE_V: { const int pw = image_size_width / patch_size; const int ph = image_size_height / patch_size; @@ -5217,6 +5320,16 @@ bool clip_encode(struct clip_ctx * ctx, struct clip_encode_params * params) { } set_input_i32("pos_w", pos_data); } break; + case PROJECTOR_TYPE_DOTS3NOTE_A: + { + GGML_ASSERT(imgs.entries.size() == 1); + const int n_pos = (imgs.entries.front().nx() + 7) / 8; // 3x stride-2 conv2d + std::vector positions(n_pos); + for (int i = 0; i < n_pos; i++) { + positions[i] = i; + } + set_input_i32("positions", positions); + } break; case PROJECTOR_TYPE_GEMMA4A: { GGML_ASSERT(imgs.entries.size() == 1); @@ -5713,6 +5826,8 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) { case PROJECTOR_TYPE_PIXTRAL: case PROJECTOR_TYPE_LIGHTONOCR: case PROJECTOR_TYPE_DOTS_OCR: + case PROJECTOR_TYPE_DOTS3NOTE_V: + case PROJECTOR_TYPE_DOTS3NOTE_A: return ctx->model.mm_2_w->ne[1]; case PROJECTOR_TYPE_MLP_NORM: return ctx->model.mm_3_b->ne[0]; diff --git a/tools/mtmd/models/dots3note.cpp b/tools/mtmd/models/dots3note.cpp new file mode 100644 index 000000000..93c14fc79 --- /dev/null +++ b/tools/mtmd/models/dots3note.cpp @@ -0,0 +1,61 @@ +#include "models.h" + +ggml_cgraph * clip_graph_dots3note_a::build() { + // inp_raw: [n_frames, n_mel, 1], one 60s chunk, mel frames not padded + // the reference impl zero-masks conv inputs beyond the valid length at each stage; + // running on exactly the valid frames with the convs' zero padding is equivalent + ggml_tensor * inp = build_inp_raw(1); + GGML_ASSERT(inp->type == GGML_TYPE_F32); + + // 3x conv2d (k=3, s=2, p=1) + gelu + { + auto conv_block = [&](ggml_tensor * x, ggml_tensor * w, ggml_tensor * b) { + x = ggml_conv_2d(ctx0, w, x, 2, 2, 1, 1, 1, 1); + x = ggml_add(ctx0, x, ggml_reshape_4d(ctx0, b, 1, 1, x->ne[2], 1)); + return ggml_gelu_erf(ctx0, x); + }; + + inp = conv_block(inp, model.conv2d_1_w, model.conv2d_1_b); + inp = conv_block(inp, model.conv2d_2_w, model.conv2d_2_b); + inp = conv_block(inp, model.conv2d_3_w, model.conv2d_3_b); + // inp: [OW=n_frames/8, OH=n_mel/8, OC=480, 1] + cb(inp, "after_conv_stem", -1); + } + + // [OW, OH, OC, 1] -> [OH*OC, OW], feature index f + OH*c (matches the reference permute+reshape) + inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 2, 0, 1, 3)); + inp = ggml_reshape_2d(ctx0, inp, inp->ne[0] * inp->ne[1], inp->ne[2]); + + // project to d_model (no bias) + inp = ggml_mul_mat(ctx0, model.conv_out_w, inp); + cb(inp, "after_conv_out", -1); + + const int64_t n_pos = inp->ne[1]; + + ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos); + ggml_set_name(positions, "positions"); + ggml_set_input(positions); + + // partial rotary: first half of each head, NEOX style + auto add_pos = [&](ggml_tensor * cur, const clip_layer &) { + return ggml_rope_ext(ctx0, cur, positions, nullptr, d_head/2, + GGML_ROPE_TYPE_NEOX, 0, hparams.rope_theta, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f); + }; + + ggml_tensor * cur = build_vit(inp, n_pos, + NORM_TYPE_RMS, hparams.ffn_op, + nullptr, add_pos); + cb(cur, "after_transformer", -1); + + // adapter: LayerNorm -> Linear -> GELU -> Linear + cur = build_norm(cur, model.mm_norm_pre_w, model.mm_norm_pre_b, NORM_TYPE_NORMAL, 1e-5, -1); + cur = build_ffn(cur, + model.mm_1_w, model.mm_1_b, + nullptr, nullptr, + model.mm_2_w, model.mm_2_b, + FFN_GELU_ERF, -1); + cb(cur, "projected", -1); + + ggml_build_forward_expand(gf, cur); + return gf; +} diff --git a/tools/mtmd/models/models.h b/tools/mtmd/models/models.h index 3631d849b..10546fa5d 100644 --- a/tools/mtmd/models/models.h +++ b/tools/mtmd/models/models.h @@ -119,6 +119,11 @@ struct clip_graph_dotsocr : clip_graph { ggml_cgraph * build() override; }; +struct clip_graph_dots3note_a : clip_graph { + clip_graph_dots3note_a(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} + ggml_cgraph * build() override; +}; + struct clip_graph_cogvlm : clip_graph { clip_graph_cogvlm(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} ggml_cgraph * build() override; diff --git a/tools/mtmd/mtmd-audio.cpp b/tools/mtmd/mtmd-audio.cpp index 98a8c11ee..ce08f9e93 100644 --- a/tools/mtmd/mtmd-audio.cpp +++ b/tools/mtmd/mtmd-audio.cpp @@ -723,6 +723,100 @@ bool mtmd_audio_preprocessor_qwen3a::preprocess(const float * sa return true; } +// +// mtmd_audio_preprocessor_dots3note +// +// Matches Dots3NoteFeatureExtractor: the waveform is split into 60s chunks and each chunk gets +// its own whisper-style log-mel (center=True, log10 + (max-8)/4). Only sample_length//hop frames +// per chunk are valid; the reference masks everything beyond them, so we emit exactly that many. +// + +void mtmd_audio_preprocessor_dots3note::initialize() { + cache.fill_sin_cos_table(hparams.audio_n_fft); + cache.fill_hann_window(hparams.audio_window_len, true); + cache.fill_mel_filterbank_matrix(hparams.n_mel_bins, hparams.audio_n_fft, hparams.audio_sample_rate); +} + +bool mtmd_audio_preprocessor_dots3note::preprocess(const float * samples, + size_t n_samples, + std::vector & output) { + if (n_samples == 0) { + return false; + } + + GGML_ASSERT(!cache.sin_vals.empty()); + GGML_ASSERT(!cache.cos_vals.empty()); + GGML_ASSERT(!cache.filters.data.empty()); + + const int pad = hparams.audio_n_fft / 2; // center=True padding + const int hop = hparams.audio_hop_len; + const size_t chunk_samples = (size_t) hparams.audio_chunk_len * hparams.audio_sample_rate; + + for (size_t start = 0; start < n_samples; start += chunk_samples) { + const size_t n_chunk = std::min(chunk_samples, n_samples - start); + const float * chunk = samples + start; + + const int64_t n_valid = n_chunk / hop; + if (n_valid == 0) { + continue; // sub-hop tail, contributes no frames + } + + // reflect-pad the start; the reference zero-pads partial chunks to 60s before the STFT, + // so a partial chunk sees zeros past its end while a full chunk reflects its own tail + std::vector padded(n_chunk + 2 * pad, 0.0f); + for (int i = 0; i < pad; i++) { + int src = pad - i; + padded[i] = (src < (int) n_chunk) ? chunk[src] : 0.0f; + } + std::copy(chunk, chunk + n_chunk, padded.begin() + pad); + if (n_chunk == chunk_samples) { + for (int i = 0; i < pad; i++) { + int src = (int) n_chunk - 2 - i; + padded[n_chunk + pad + i] = (src >= 0) ? chunk[src] : 0.0f; + } + } + + filter_params params; + params.n_mel = hparams.n_mel_bins; + params.n_fft_bins = 1 + (hparams.audio_n_fft / 2); + params.hann_window_size = hparams.audio_window_len; + params.hop_length = hop; + params.sample_rate = hparams.audio_sample_rate; + params.no_padding = true; // padding already applied above + params.use_natural_log = false; + + mtmd_audio_mel mel_full; + if (!log_mel_spectrogram(padded.data(), (int) padded.size(), 4, params, cache, mel_full)) { + return false; + } + GGML_ASSERT(mel_full.n_len >= n_valid); + + // per-chunk whisper-style normalization, then keep only the valid frames + mtmd_audio_mel out; + out.n_mel = mel_full.n_mel; + out.n_len = n_valid; + out.n_len_org = n_valid; + out.data.resize((size_t) out.n_mel * (size_t) out.n_len); + + double mmax = -1e20; + for (int64_t m = 0; m < out.n_mel; m++) { + for (int64_t t = 0; t < n_valid; t++) { + mmax = std::max(mmax, (double) mel_full.data[(size_t) m * mel_full.n_len + t]); + } + } + mmax -= 8.0; + for (int64_t m = 0; m < out.n_mel; m++) { + for (int64_t t = 0; t < n_valid; t++) { + const double v = std::max((double) mel_full.data[(size_t) m * mel_full.n_len + t], mmax); + out.data[(size_t) m * n_valid + t] = (float) ((v + 4.0) / 4.0); + } + } + + output.push_back(std::move(out)); + } + return !output.empty(); +} + // // mtmd_audio_preprocessor_mimo_audio // diff --git a/tools/mtmd/mtmd-audio.h b/tools/mtmd/mtmd-audio.h index 44ad098ae..0f47d4502 100644 --- a/tools/mtmd/mtmd-audio.h +++ b/tools/mtmd/mtmd-audio.h @@ -111,6 +111,15 @@ struct mtmd_audio_preprocessor_qwen3a : mtmd_audio_preprocessor { mtmd_audio_cache cache; }; +struct mtmd_audio_preprocessor_dots3note : mtmd_audio_preprocessor { + mtmd_audio_preprocessor_dots3note(const clip_ctx * ctx) : mtmd_audio_preprocessor(ctx) {} + void initialize() override; + bool preprocess(const float * samples, size_t n_samples, std::vector & output) override; + + private: + mtmd_audio_cache cache; +}; + struct mtmd_audio_preprocessor_mimo_audio : mtmd_audio_preprocessor { mtmd_audio_preprocessor_mimo_audio(const clip_ctx * ctx) : mtmd_audio_preprocessor(ctx) {} void initialize() override; diff --git a/tools/mtmd/mtmd.cpp b/tools/mtmd/mtmd.cpp index 95f17f7af..5b306180d 100644 --- a/tools/mtmd/mtmd.cpp +++ b/tools/mtmd/mtmd.cpp @@ -825,6 +825,7 @@ struct mtmd_context { image_preproc = std::make_unique(ctx_v); } break; case PROJECTOR_TYPE_DOTS_OCR: + case PROJECTOR_TYPE_DOTS3NOTE_V: { // <|img|> ... (image embeddings) ... <|endofimg|> img_beg = "<|img|>"; @@ -976,6 +977,13 @@ struct mtmd_context { aud_end = ""; audio_preproc = std::make_unique(ctx_a); } break; + case PROJECTOR_TYPE_DOTS3NOTE_A: + { + // <|audio_comp_start|> ... (embeddings) ... <|audio_comp_end|> + aud_beg = "<|audio_comp_start|>"; + aud_end = "<|audio_comp_end|>"; + audio_preproc = std::make_unique(ctx_a); + } break; case PROJECTOR_TYPE_MIMO_AUDIO: { aud_beg = "<|mimo_audio_start|>";