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9f818c5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 | # SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: OpenMDW-1.1
import functools
import re
import shutil
from pathlib import Path
from uuid import uuid4
import pydantic
from cosmos_framework.inference.common.config import CONFIG_DIR
from cosmos_framework.utils.checkpoint_db import (
CheckpointConfig,
CheckpointDirHf,
CheckpointDirS3,
CheckpointFileHf,
CheckpointFileS3,
RepositoryType,
register_checkpoint,
)
from cosmos_framework.utils.flags import TRAINING
_AVAE_LEGACY_CKPT_NAME = "avae_48k_noncausal_25hz_64ch.ckpt"
_AVAE_LEGACY_JSON_NAME = "avae_48k_noncausal_25hz_64ch.json"
# Inside a residual unit the legacy nn.Sequential layout is [snake1, conv1,
# snake2, conv2]; map the named diffusers attribute back to its sub-index.
_AVAE_RES_UNIT_INNER_INDEX = {"snake1": 0, "conv1": 1, "snake2": 2, "conv2": 3}
def _avae_block_key_to_legacy(key: str, num_blocks: int) -> str:
"""Map a diffusers OobleckDecoder key (`decoder.block.*`) back to the legacy
nn.Sequential layout (`decoder.layers.*`) the native AVAE loader expects.
Exact inverse of ``_sound_tokenizer_remap_flat_layout`` in
``cosmos_framework/scripts/_convert_model_to_diffusers.py``. The legacy decoder
is ``Sequential([conv1, block_0..block_{N-1}, snake1, conv2])``; each block is
``Sequential([snake1, conv_t1, res_unit1, res_unit2, res_unit3])`` and each
residual unit is ``Sequential([snake1, conv1, snake2, conv2])``.
"""
snake1_idx = num_blocks + 1
conv2_idx = num_blocks + 2
m = re.fullmatch(r"decoder\.block\.(\d+)\.res_unit(\d+)\.(snake1|conv1|snake2|conv2)\.(.+)", key)
if m:
block_idx, res_idx, inner, rest = int(m.group(1)), int(m.group(2)), m.group(3), m.group(4)
return f"decoder.layers.{block_idx + 1}.layers.{res_idx + 1}.layers.{_AVAE_RES_UNIT_INNER_INDEX[inner]}.{rest}"
m = re.fullmatch(r"decoder\.block\.(\d+)\.snake1\.(.+)", key)
if m:
return f"decoder.layers.{int(m.group(1)) + 1}.layers.0.{m.group(2)}"
m = re.fullmatch(r"decoder\.block\.(\d+)\.conv_t1\.(.+)", key)
if m:
return f"decoder.layers.{int(m.group(1)) + 1}.layers.1.{m.group(2)}"
m = re.fullmatch(r"decoder\.conv1\.(.+)", key)
if m:
return f"decoder.layers.0.{m.group(1)}"
m = re.fullmatch(r"decoder\.snake1\.(.+)", key)
if m:
return f"decoder.layers.{snake1_idx}.{m.group(1)}"
m = re.fullmatch(r"decoder\.conv2\.(.+)", key)
if m:
return f"decoder.layers.{conv2_idx}.{m.group(1)}"
return key
def _materialize_avae_ckpt(local_dir: str) -> None:
"""Synthesize the legacy ``.ckpt`` + ``.json`` the native AVAE loader expects
from the decoder-only ``sound_tokenizer/`` safetensors.
The new HF layout ships ``sound_tokenizer/{config.json,
diffusion_pytorch_model.safetensors}`` in the diffusers OobleckDecoder layout
(``decoder.block.*`` keys, Snake1d ``alpha``/``beta`` shaped ``[1, C, 1]``). The
native loader in ``cosmos_framework/model/vfm/tokenizers/audio/avae.py`` builds an
``nn.Sequential`` decoder keyed ``decoder.layers.*`` with Snake params shaped
``[C]`` and loads via ``load_state_dict(strict=False)`` — so without remapping
the keys, none match and every decoder weight is silently left at init (noise).
We invert the forward conversion (key remap + snake reshape) and wrap the result
under ``state_dict``. Decoder-only is sufficient: generation only decodes sound
latents to a waveform. Idempotent.
"""
import torch
from safetensors.torch import load_file
local = Path(local_dir)
ckpt_path = local / _AVAE_LEGACY_CKPT_NAME
json_path = local / _AVAE_LEGACY_JSON_NAME
if ckpt_path.exists() and json_path.exists():
return
safetensors_path = local / "diffusion_pytorch_model.safetensors"
if not safetensors_path.exists():
safetensors_path = local / "model.safetensors"
config_path = local / "config.json"
if not safetensors_path.exists() or not config_path.exists():
raise FileNotFoundError(
f"AVAE shim: expected diffusion_pytorch_model.safetensors (or model.safetensors) "
f"and {config_path.name} in {local}"
)
src = load_file(str(safetensors_path))
block_ids = {int(m.group(1)) for k in src if (m := re.fullmatch(r"decoder\.block\.(\d+)\..+", k))}
if not block_ids:
raise RuntimeError(f"No `decoder.block.*` keys in {safetensors_path}; cannot remap AVAE decoder.")
num_blocks = max(block_ids) + 1
state_dict: dict = {}
for key, value in src.items():
legacy_key = _avae_block_key_to_legacy(key, num_blocks)
if (legacy_key.endswith(".alpha") or legacy_key.endswith(".beta")) and value.ndim == 3:
value = value.reshape(-1).contiguous() # Snake1d [1, C, 1] -> [C]
state_dict[legacy_key] = value
if any(k.startswith("decoder.block.") for k in state_dict):
raise RuntimeError("`decoder.block.*` keys remain after AVAE remap; conversion is incomplete.")
if not ckpt_path.exists():
torch.save({"state_dict": state_dict}, str(ckpt_path))
if not json_path.exists():
shutil.copyfile(str(config_path), str(json_path))
@functools.cache
def register_checkpoints():
"""Register checkpoints used in hydra configs (tokenizers, VLM)."""
for repository, revision in [
("Qwen/Qwen3-0.6B", "c1899de289a04d12100db370d81485cdf75e47ca"),
("Qwen/Qwen3-VL-2B-Instruct", "89644892e4d85e24eaac8bacfd4f463576704203"),
("Qwen/Qwen3-VL-8B-Instruct", "0c351dd01ed87e9c1b53cbc748cba10e6187ff3b"),
("Qwen/Qwen3-VL-32B-Instruct", "0cfaf48183f594c314753d30a4c4974bc75f3ccb"),
]:
for s3_prefix in [
# 'cosmos_framework.configs.base.defaults.vlm.download_tokenizer_files'
"cosmos3/pretrained/huggingface",
# 'cosmos_framework.utils.vfm.vlm.pretrained_models_downloader.maybe_download_hf_model_from_s3'
"cosmos_reason2/hf_models",
]:
register_checkpoint(
CheckpointConfig(
uuid=uuid4().hex,
name=repository,
s3=CheckpointDirS3(
uri=f"s3://bucket/{s3_prefix}/{repository}",
),
hf=CheckpointDirHf(
repository=repository,
revision=revision,
include=() if TRAINING else ("*.json", "*.txt"),
),
),
)
register_checkpoint(
CheckpointConfig(
uuid=uuid4().hex,
name="Cosmos3-Reasoner-8B-Private",
s3=CheckpointDirS3(
uri="s3://bucket/cosmos3/pretrained/huggingface/Cosmos-Reason/Cosmos3-Reasoner-8B-Private",
),
hf=CheckpointDirHf(
repository="nvidia/Cosmos3-Nano-Reasoner",
revision="6406357cdc32fbf8db5f51ff7992343803b06961",
),
),
)
register_checkpoint(
CheckpointConfig(
uuid=uuid4().hex,
name="Cosmos3-Reasoner-32B-Private",
s3=CheckpointDirS3(
uri="s3://bucket/cosmos3/pretrained/huggingface/Cosmos-Reason/Cosmos3-Reasoner-32B-Private",
),
hf=CheckpointDirHf(
repository="nvidia/Cosmos3-Super-Reasoner",
revision="b9b716f3508dfa442e0c8ba32fb5d0c9adf2a32c",
),
),
)
register_checkpoint(
CheckpointConfig(
uuid="c5236e3a-e846-49e3-a40c-67dfceefff5d",
name="Cosmos3-Nano-Reasoner-bb9c6f5",
s3=CheckpointDirS3(
uri="s3://bucket/cosmos3/pretrained/huggingface/Cosmos-Reason/Cosmos3-Nano-Reasoner-bb9c6f5",
),
hf=CheckpointDirHf(
repository="nvidia/Cosmos3-Experimental",
subdirectory="c5236e3a-e846-49e3-a40c-67dfceefff5d",
revision="6ca42c5d0b96cb133e811c1bcced048d4acfaa12",
),
),
)
register_checkpoint(
CheckpointConfig(
uuid="4cb0c125-49a8-4e66-aebb-06e100affdb0",
name="Cosmos3-Super-Reasoner-b6df0d1",
s3=CheckpointDirS3(
uri="s3://bucket/cosmos3/pretrained/huggingface/Cosmos-Reason/Cosmos3-Super-Reasoner-b6df0d1",
),
hf=CheckpointDirHf(
repository="nvidia/Cosmos3-Experimental",
subdirectory="4cb0c125-49a8-4e66-aebb-06e100affdb0",
revision="6ca42c5d0b96cb133e811c1bcced048d4acfaa12",
),
)
)
register_checkpoint(
CheckpointConfig(
uuid=uuid4().hex,
name="Wan2.1/vae",
s3=CheckpointFileS3(
uri="s3://bucket/pretrained/tokenizers/video/wan2pt1/Wan2.1_VAE.pth",
),
hf=CheckpointFileHf(
repository="Wan-AI/Wan2.1-T2V-14B",
revision="a064a6c71f5be440641209c07bf2a5ce7a2ff5e4",
filename="Wan2.1_VAE.pth",
),
),
)
register_checkpoint(
CheckpointConfig(
uuid=uuid4().hex,
name="Wan2.2/vae",
s3=CheckpointFileS3(
uri="s3://bucket/pretrained/tokenizers/video/wan2pt2/Wan2.2_VAE.pth",
),
hf=CheckpointFileHf(
repository="Wan-AI/Wan2.2-TI2V-5B",
revision="921dbaf3f1674a56f47e83fb80a34bac8a8f203e",
filename="Wan2.2_VAE.pth",
),
),
)
register_checkpoint(
CheckpointConfig(
uuid=uuid4().hex,
name="AVAE",
s3=CheckpointDirS3(
uri="s3://bucket/pretrained/tokenizers/audio/avae",
),
hf=CheckpointDirHf(
repository="nvidia/Cosmos3-Nano",
revision="main",
subdirectory="sound_tokenizer",
),
# The sound_tokenizer/ safetensors are decoder-only and use the diffusers
# OobleckDecoder key layout; _materialize_avae_ckpt remaps them back to the
# legacy decoder.layers.* layout the native AVAE loader expects.
post_download=_materialize_avae_ckpt,
),
)
CHECKPOINTS: dict[str, CheckpointConfig] = {
# Created using 'convert_model_to_dcp'
"Cosmos3-Nano-Train": CheckpointConfig(
name="Cosmos3-Nano-Train",
uuid=uuid4().hex,
config_file=str(CONFIG_DIR / "model/Cosmos3-Nano.yaml"),
experiment="cosmos3_ga_16bm8b_v1_midtrain",
s3=CheckpointDirS3(
uri="s3://bucket1/cosmos3_vfm/cosmos3_ga_midtraining/cosmos3_ga_16bm8b_v1_midtrain/checkpoints/iter_000012000/",
),
hf=CheckpointDirHf(
repository="nvidia/Cosmos3-Experimental",
revision="a3743aa1092fbefc9c6f6ae8c8c17e56a78aea4b",
subdirectory="e77a607f-af13-4321-bbf5-92f3e90f05e1-train",
),
),
"Cosmos3-Super-Train": CheckpointConfig(
name="Cosmos3-Super-Train",
uuid=uuid4().hex,
config_file=str(CONFIG_DIR / "model/Cosmos3-Super.yaml"),
experiment="cosmos3_ga_64bm32b_v1_midtrain",
s3=CheckpointDirS3(
uri="s3://bucket1/cosmos3_vfm/cosmos3_ga_midtraining/cosmos3_ga_64bm32b_v1_midtrain/checkpoints/iter_000005000/",
),
hf=CheckpointDirHf(
repository="nvidia/Cosmos3-Experimental",
revision="a3743aa1092fbefc9c6f6ae8c8c17e56a78aea4b",
subdirectory="d92be19a-42ab-4a96-bdf2-98d1c9724cd9-train",
),
),
}
"""Checkpoints used by tests."""
class DatasetConfig(pydantic.BaseModel):
model_config = pydantic.ConfigDict(extra="forbid", frozen=True)
hf: CheckpointDirHf
"""Config for dataset on Hugging Face."""
DATASETS = {
"nvidia/BridgeData2-Subset-Synthetic-Captions": DatasetConfig(
hf=CheckpointDirHf(
repository_type=RepositoryType.DATASET,
repository="nvidia/BridgeData2-Subset-Synthetic-Captions",
revision="40d018ac1c1a2a4b9734f17fdb21f3d933c49a01",
subdirectory="sft_dataset_bridge",
),
),
"nvidia/LIBERO_LeRobot_v3": DatasetConfig(
hf=CheckpointDirHf(
repository_type=RepositoryType.DATASET,
repository="nvidia/LIBERO_LeRobot_v3",
revision="ddc1edeb6e51e2b7d4d2ba7a1433daaecd37aa64",
),
),
"nvidia/bridge_lerobot_v3": DatasetConfig(
hf=CheckpointDirHf(
repository_type=RepositoryType.DATASET,
repository="nvidia/bridge_lerobot_v3",
revision="b887e193b141f2fe5b6e3d567577aa51c475693b",
),
),
}
"""Datasets used by tests."""
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