music/test/16splits/log/audio_tokenizer.3.log

# python egs/pretraining/data_scripts/offline_tokenization_scp.py --rank 3 --input-file /turing_music_fs/music_data/ydc/code2/TokenPPL/data/music/test/16splits/wav.3.scp --output_file_reason /turing_music_fs/music_data/ydc/code2/TokenPPL/data/music/test/16splits/reason_tokens.3.pt --output_file_semantic /turing_music_fs/music_data/ydc/code2/TokenPPL/data/music/test/16splits/semantic_tokens.3.pt --model_path /turing_music_fs/music_data/ydc/exp2/tmp_codec/reasoncodec_1024/reason_codec.checkpoint --train_config /turing_music_fs/music_data/ydc/code2/TokenPPL/Token_LM_dual/tools/tokenizer/ReasoningCodec_film_1024/infer_config.yaml --tokenizer reasoningCodec_film_1024 
# Started at Mon Nov 10 16:02:24 UTC 2025
#
2025-11-10 16:02:26,377 INFO [offline_tokenization_scp.py:44] max gpu 8
2025-11-10 16:02:26,377 INFO [offline_tokenization_scp.py:47] Using device: cuda:2
2025-11-10 16:02:27,972 DEBUG [__init__.py:44] Skipping import of cpp extensions
[2025-11-10 16:02:33,091] [INFO] [real_accelerator.py:222:get_accelerator] Setting ds_accelerator to cuda (auto detect)
2025-11-10 16:02:33,174 INFO [spawn.py:60] gcc -pthread -B /root/miniconda3/envs/uniaudio2/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /root/miniconda3/envs/uniaudio2/include -fPIC -O2 -isystem /root/miniconda3/envs/uniaudio2/include -fPIC -c /tmp/tmpyv9ep7jc/test.c -o /tmp/tmpyv9ep7jc/test.o
2025-11-10 16:02:33,190 INFO [spawn.py:60] gcc -pthread -B /root/miniconda3/envs/uniaudio2/compiler_compat /tmp/tmpyv9ep7jc/test.o -laio -o /tmp/tmpyv9ep7jc/a.out
2025-11-10 16:02:33,900 INFO [spawn.py:60] gcc -pthread -B /root/miniconda3/envs/uniaudio2/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /root/miniconda3/envs/uniaudio2/include -fPIC -O2 -isystem /root/miniconda3/envs/uniaudio2/include -fPIC -c /tmp/tmpk30ytxm5/test.c -o /tmp/tmpk30ytxm5/test.o
2025-11-10 16:02:33,916 INFO [spawn.py:60] gcc -pthread -B /root/miniconda3/envs/uniaudio2/compiler_compat /tmp/tmpk30ytxm5/test.o -L/usr/local/cuda -L/usr/local/cuda/lib64 -lcufile -o /tmp/tmpk30ytxm5/a.out
2025-11-10 16:02:34,754 DEBUG [cmd.py:1253] Popen(['git', 'version'], cwd=/turing_music_fs/music_data/ydc/code2/TokenPPL/Token_LM_dual, stdin=None, shell=False, universal_newlines=False)
2025-11-10 16:02:34,756 DEBUG [cmd.py:1253] Popen(['git', 'version'], cwd=/turing_music_fs/music_data/ydc/code2/TokenPPL/Token_LM_dual, stdin=None, shell=False, universal_newlines=False)
2025-11-10 16:02:35,019 DEBUG [auth.py:50] Trying paths: ['/root/.docker/config.json', '/root/.dockercfg']
2025-11-10 16:02:35,019 DEBUG [auth.py:57] No config file found
/turing_music_fs/music_data/ydc/code2/TokenPPL/Token_LM_dual/tools/tokenizer/ReasoningCodec_film_1024/modules/transformer.py:126: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
  @autocast(enabled = False)
/turing_music_fs/music_data/ydc/code2/TokenPPL/Token_LM_dual/tools/tokenizer/ReasoningCodec_film_1024/modules/transformer.py:151: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
  @autocast(enabled = False)
2025-11-10 16:02:36,456 DEBUG [__init__.py:342] matplotlib data path: /root/miniconda3/envs/uniaudio2/lib/python3.10/site-packages/matplotlib/mpl-data
2025-11-10 16:02:36,461 DEBUG [__init__.py:342] CONFIGDIR=/root/.config/matplotlib
2025-11-10 16:02:36,463 DEBUG [__init__.py:1557] interactive is False
2025-11-10 16:02:36,463 DEBUG [__init__.py:1558] platform is linux
2025-11-10 16:02:36,484 DEBUG [__init__.py:342] CACHEDIR=/root/.cache/matplotlib
2025-11-10 16:02:36,486 DEBUG [font_manager.py:1635] Using fontManager instance from /root/.cache/matplotlib/fontlist-v390.json
/root/miniconda3/envs/uniaudio2/lib/python3.10/site-packages/torch/nn/utils/weight_norm.py:134: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`.
  WeightNorm.apply(module, name, dim)
/turing_music_fs/music_data/ydc/code2/TokenPPL/Token_LM_dual/tools/tokenizer/ReasoningCodec_film_1024/models/scalar24k.py:435: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  parameter_dict = torch.load(self.resume_path)
best_rq_ckpt  /turing_music_fs/music_data/ckpts/ckpts/ssl.pt
['/turing_music_fs/music_data/ydc/code2/TokenPPL/Token_LM_dual/tools/tokenizer/ReasoningCodec_film_1024/modules/our_MERT_BESTRQ', '/turing_music_fs/music_data/ydc/code2/TokenPPL/Token_LM_dual/egs/pretraining/data_scripts', '/turing_music_fs/music_data/ydc/code2/TokenPPL/Token_LM_dual', '/turing_music_fs/music_data/ydc/code2', '/root/miniconda3/envs/uniaudio2/lib/python310.zip', '/root/miniconda3/envs/uniaudio2/lib/python3.10', '/root/miniconda3/envs/uniaudio2/lib/python3.10/lib-dynload', '/root/.local/lib/python3.10/site-packages', '/root/miniconda3/envs/uniaudio2/lib/python3.10/site-packages', '/home/ydc/musicllm/v2_speech', '/turing_music_fs/music_data/ydc/code2/TokenPPL/Token_LM_dual', '/root/miniconda3/envs/uniaudio2/lib/python3.10/site-packages/setuptools/_vendor', '/tmp/tmpi8zuhb5n']
2025-11-10 16:02:47,049 DEBUG [__init__.py:47] Creating converter from 7 to 5
2025-11-10 16:02:47,050 DEBUG [__init__.py:47] Creating converter from 5 to 7
2025-11-10 16:02:47,050 DEBUG [__init__.py:47] Creating converter from 7 to 5
2025-11-10 16:02:47,050 DEBUG [__init__.py:47] Creating converter from 5 to 7
/root/miniconda3/envs/uniaudio2/lib/python3.10/site-packages/fairseq/checkpoint_utils.py:315: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  state = torch.load(f, map_location=torch.device("cpu"))
path_ls  /turing_music_fs/music_data/ydc/code2/TokenPPL/Token_LM_dual/tools/tokenizer/ReasoningCodec_film_1024/modules/our_MERT_BESTRQ/mert_fairseq/models/eat
path_ls  /turing_music_fs/music_data/ydc/code2/TokenPPL/Token_LM_dual/tools/tokenizer/ReasoningCodec_film_1024/modules/our_MERT_BESTRQ/mert_fairseq/models/eat
2025-11-10 16:02:52,106 INFO [mert_pretraining.py:253] current directory is /turing_music_fs/music_data/ydc/code2/TokenPPL/Token_LM_dual
2025-11-10 16:02:52,106 INFO [mert_pretraining.py:254] MERTPretrainingTask Config {'_name': 'mert_pretraining', 'data': '/apdcephfs_nj7/share_301796285/user/hainazhu/our-MERT/data/all_songs_20240430', 'sharding_data': -1, 'load_random_data_shard': True, 'fine_tuning': False, 'labels': [], 'label_dir': '', 'label_rate': 25.0, 'sample_rate': 24000, 'normalize': False, 'enable_padding': False, 'max_keep_size': None, 'max_sample_size': 720000, 'min_sample_size': 432000, 'single_target': False, 'random_crop': True, 'pad_audio': False, 'store_labels': False, 'numpy_memmap_label': False, 'augmentation_effects': '[]', 'augmentation_probs': '[]', 'inbatch_noise_augment_len_range': '[8000, 24000]', 'inbatch_noise_augment_number_range': '[1, 3]', 'inbatch_noise_augment_volume': 1.0, 'dynamic_crops': '[]', 'dynamic_crops_epoches': '[]', 'cqt_loss_bin_dataloader': -1, 'clip_secs': 30}
/root/miniconda3/envs/uniaudio2/lib/python3.10/site-packages/torch/nn/utils/weight_norm.py:134: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`.
  WeightNorm.apply(module, name, dim)
/root/miniconda3/envs/uniaudio2/lib/python3.10/site-packages/diffusers/configuration_utils.py:245: FutureWarning: It is deprecated to pass a pretrained model name or path to `from_config`.If you were trying to load a model, please use <class 'tools.tokenizer.ReasoningCodec_film_1024.models.transformer_1d_flow.Transformer1DModel'>.load_config(...) followed by <class 'tools.tokenizer.ReasoningCodec_film_1024.models.transformer_1d_flow.Transformer1DModel'>.from_config(...) instead. Otherwise, please make sure to pass a configuration dictionary instead. This functionality will be removed in v1.0.0.
  deprecate("config-passed-as-path", "1.0.0", deprecation_message, standard_warn=False)
No stats file found at `None`, use default from msd.
Checkpoint for rvq `/apdcephfs_nj7/share_301796285/user/hainazhu/our-MERT/data/fairseq_savedir/rvq/RVQ_4000.pth` not found. Using random initialization.
# of parameters: 313.9525146484375
Power Normalization requires removing norms, setting remove_norms to True
Power Normalization requires removing norms, setting remove_norms to True
Power Normalization requires removing norms, setting remove_norms to True
Power Normalization requires removing norms, setting remove_norms to True
Power Normalization requires removing norms, setting remove_norms to True
Power Normalization requires removing norms, setting remove_norms to True
Power Normalization requires removing norms, setting remove_norms to True
Power Normalization requires removing norms, setting remove_norms to True
Power Normalization requires removing norms, setting remove_norms to True
Power Normalization requires removing norms, setting remove_norms to True
Power Normalization requires removing norms, setting remove_norms to True
Power Normalization requires removing norms, setting remove_norms to True
Power Normalization requires removing norms, setting remove_norms to True

Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]
Loading checkpoint shards:  50%|█████     | 1/2 [00:06<00:06,  6.09s/it]
Loading checkpoint shards: 100%|██████████| 2/2 [00:07<00:00,  3.54s/it]
Loading checkpoint shards: 100%|██████████| 2/2 [00:07<00:00,  3.92s/it]
The new embeddings will be initialized from a multivariate normal distribution that has old embeddings' mean and covariance. As described in this article: https://nlp.stanford.edu/~johnhew/vocab-expansion.html. To disable this, use `mean_resizing=False`
/turing_music_fs/music_data/ydc/code2/TokenPPL/Token_LM_dual/tools/tokenizer/ReasoningCodec_film_1024/models/model_utils.py:42: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  ckpt = torch.load(ckpt_path, map_location=device)
/turing_music_fs/music_data/ydc/code2/TokenPPL/Token_LM_dual/tools/tokenizer/ReasoningCodec_film_1024/reason_tokenizer.py:57: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  state_dict = torch.load(model_path, map_location='cpu')['model']
trainable params: 9,175,040 || all params: 3,221,927,936 || trainable%: 0.2848
Loading training prompts done!
loadable_state  dict_keys(['cls_token', 'llama_model.base_model.model.model.layers.0.self_attn.q_proj.lora_A.default.weight', 'llama_model.base_model.model.model.layers.0.self_attn.q_proj.lora_B.default.weight', 'llama_model.base_model.model.model.layers.0.self_attn.v_proj.lora_A.default.weight', 'llama_model.base_model.model.model.layers.0.self_attn.v_proj.lora_B.default.weight', 'llama_model.base_model.model.model.layers.1.self_attn.q_proj.lora_A.default.weight', 'llama_model.base_model.model.model.layers.1.self_attn.q_proj.lora_B.default.weight', 'llama_model.base_model.model.model.layers.1.self_attn.v_proj.lora_A.default.weight', 'llama_model.base_model.model.model.layers.1.self_attn.v_proj.lora_B.default.weight', 'llama_model.base_model.model.model.layers.2.self_attn.q_proj.lora_A.default.weight', 'llama_model.base_model.model.model.layers.2.self_attn.q_proj.lora_B.default.weight', 'llama_model.base_model.model.model.layers.2.self_attn.v_proj.lora_A.default.weight', 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'llama_model.base_model.model.model.layers.22.self_attn.q_proj.lora_A.default.weight', 'llama_model.base_model.model.model.layers.22.self_attn.q_proj.lora_B.default.weight', 'llama_model.base_model.model.model.layers.22.self_attn.v_proj.lora_A.default.weight', 'llama_model.base_model.model.model.layers.22.self_attn.v_proj.lora_B.default.weight', 'llama_model.base_model.model.model.layers.23.self_attn.q_proj.lora_A.default.weight', 'llama_model.base_model.model.model.layers.23.self_attn.q_proj.lora_B.default.weight', 'llama_model.base_model.model.model.layers.23.self_attn.v_proj.lora_A.default.weight', 'llama_model.base_model.model.model.layers.23.self_attn.v_proj.lora_B.default.weight', 'llama_model.base_model.model.model.layers.24.self_attn.q_proj.lora_A.default.weight', 'llama_model.base_model.model.model.layers.24.self_attn.q_proj.lora_B.default.weight', 'llama_model.base_model.model.model.layers.24.self_attn.v_proj.lora_A.default.weight', 'llama_model.base_model.model.model.layers.24.self_attn.v_proj.lora_B.default.weight', 'llama_model.base_model.model.model.layers.25.self_attn.q_proj.lora_A.default.weight', 'llama_model.base_model.model.model.layers.25.self_attn.q_proj.lora_B.default.weight', 'llama_model.base_model.model.model.layers.25.self_attn.v_proj.lora_A.default.weight', 'llama_model.base_model.model.model.layers.25.self_attn.v_proj.lora_B.default.weight', 'llama_model.base_model.model.model.layers.26.self_attn.q_proj.lora_A.default.weight', 'llama_model.base_model.model.model.layers.26.self_attn.q_proj.lora_B.default.weight', 'llama_model.base_model.model.model.layers.26.self_attn.v_proj.lora_A.default.weight', 'llama_model.base_model.model.model.layers.26.self_attn.v_proj.lora_B.default.weight', 'llama_model.base_model.model.model.layers.27.self_attn.q_proj.lora_A.default.weight', 'llama_model.base_model.model.model.layers.27.self_attn.q_proj.lora_B.default.weight', 'llama_model.base_model.model.model.layers.27.self_attn.v_proj.lora_A.default.weight', 'llama_model.base_model.model.model.layers.27.self_attn.v_proj.lora_B.default.weight', 'llama_model.base_model.model.lm_head.weight', 'llm_proj.weight', 'llm_proj.bias', 'semantic_merge_proj.weight', 'semantic_merge_proj.bias', 'encoder_transformers.0.self_attn.to_qkv.parametrizations.weight.original0', 'encoder_transformers.0.self_attn.to_qkv.parametrizations.weight.original1', 'encoder_transformers.0.self_attn.to_out.parametrizations.weight.original0', 'encoder_transformers.0.self_attn.to_out.parametrizations.weight.original1', 'encoder_transformers.0.self_attn.q_norm.weight', 'encoder_transformers.0.self_attn.q_norm.bias', 'encoder_transformers.0.self_attn.k_norm.weight', 'encoder_transformers.0.self_attn.k_norm.bias', 'encoder_transformers.0.self_attn_scale.scale', 'encoder_transformers.0.ff.ff.0.proj.bias', 'encoder_transformers.0.ff.ff.0.proj.parametrizations.weight.original0', 'encoder_transformers.0.ff.ff.0.proj.parametrizations.weight.original1', 'encoder_transformers.0.ff.ff.2.bias', 'encoder_transformers.0.ff.ff.2.parametrizations.weight.original0', 'encoder_transformers.0.ff.ff.2.parametrizations.weight.original1', 'encoder_transformers.0.ff_scale.scale', 'encoder_transformers.0.rope.inv_freq', 'encoder_transformers.1.self_attn.to_qkv.parametrizations.weight.original0', 'encoder_transformers.1.self_attn.to_qkv.parametrizations.weight.original1', 'encoder_transformers.1.self_attn.to_out.parametrizations.weight.original0', 'encoder_transformers.1.self_attn.to_out.parametrizations.weight.original1', 'encoder_transformers.1.self_attn.q_norm.weight', 'encoder_transformers.1.self_attn.q_norm.bias', 'encoder_transformers.1.self_attn.k_norm.weight', 'encoder_transformers.1.self_attn.k_norm.bias', 'encoder_transformers.1.self_attn_scale.scale', 'encoder_transformers.1.ff.ff.0.proj.bias', 'encoder_transformers.1.ff.ff.0.proj.parametrizations.weight.original0', 'encoder_transformers.1.ff.ff.0.proj.parametrizations.weight.original1', 'encoder_transformers.1.ff.ff.2.bias', 'encoder_transformers.1.ff.ff.2.parametrizations.weight.original0', 'encoder_transformers.1.ff.ff.2.parametrizations.weight.original1', 'encoder_transformers.1.ff_scale.scale', 'encoder_transformers.1.rope.inv_freq', 'encoder_transformers.2.self_attn.to_qkv.parametrizations.weight.original0', 'encoder_transformers.2.self_attn.to_qkv.parametrizations.weight.original1', 'encoder_transformers.2.self_attn.to_out.parametrizations.weight.original0', 'encoder_transformers.2.self_attn.to_out.parametrizations.weight.original1', 'encoder_transformers.2.self_attn.q_norm.weight', 'encoder_transformers.2.self_attn.q_norm.bias', 'encoder_transformers.2.self_attn.k_norm.weight', 'encoder_transformers.2.self_attn.k_norm.bias', 'encoder_transformers.2.self_attn_scale.scale', 'encoder_transformers.2.ff.ff.0.proj.bias', 'encoder_transformers.2.ff.ff.0.proj.parametrizations.weight.original0', 'encoder_transformers.2.ff.ff.0.proj.parametrizations.weight.original1', 'encoder_transformers.2.ff.ff.2.bias', 'encoder_transformers.2.ff.ff.2.parametrizations.weight.original0', 'encoder_transformers.2.ff.ff.2.parametrizations.weight.original1', 'encoder_transformers.2.ff_scale.scale', 'encoder_transformers.2.rope.inv_freq', 'encoder_transformers.3.self_attn.to_qkv.parametrizations.weight.original0', 'encoder_transformers.3.self_attn.to_qkv.parametrizations.weight.original1', 'encoder_transformers.3.self_attn.to_out.parametrizations.weight.original0', 'encoder_transformers.3.self_attn.to_out.parametrizations.weight.original1', 'encoder_transformers.3.self_attn.q_norm.weight', 'encoder_transformers.3.self_attn.q_norm.bias', 'encoder_transformers.3.self_attn.k_norm.weight', 'encoder_transformers.3.self_attn.k_norm.bias', 'encoder_transformers.3.self_attn_scale.scale', 'encoder_transformers.3.ff.ff.0.proj.bias', 'encoder_transformers.3.ff.ff.0.proj.parametrizations.weight.original0', 'encoder_transformers.3.ff.ff.0.proj.parametrizations.weight.original1', 'encoder_transformers.3.ff.ff.2.bias', 'encoder_transformers.3.ff.ff.2.parametrizations.weight.original0', 'encoder_transformers.3.ff.ff.2.parametrizations.weight.original1', 'encoder_transformers.3.ff_scale.scale', 'encoder_transformers.3.rope.inv_freq', 'encoder_transformers.4.self_attn.to_qkv.parametrizations.weight.original0', 'encoder_transformers.4.self_attn.to_qkv.parametrizations.weight.original1', 'encoder_transformers.4.self_attn.to_out.parametrizations.weight.original0', 'encoder_transformers.4.self_attn.to_out.parametrizations.weight.original1', 'encoder_transformers.4.self_attn.q_norm.weight', 'encoder_transformers.4.self_attn.q_norm.bias', 'encoder_transformers.4.self_attn.k_norm.weight', 'encoder_transformers.4.self_attn.k_norm.bias', 'encoder_transformers.4.self_attn_scale.scale', 'encoder_transformers.4.ff.ff.0.proj.bias', 'encoder_transformers.4.ff.ff.0.proj.parametrizations.weight.original0', 'encoder_transformers.4.ff.ff.0.proj.parametrizations.weight.original1', 'encoder_transformers.4.ff.ff.2.bias', 'encoder_transformers.4.ff.ff.2.parametrizations.weight.original0', 'encoder_transformers.4.ff.ff.2.parametrizations.weight.original1', 'encoder_transformers.4.ff_scale.scale', 'encoder_transformers.4.rope.inv_freq', 'reasoning_vq.project_in.weight', 'reasoning_vq.project_in.bias', 'reasoning_vq.project_out.weight', 'reasoning_vq.project_out.bias', 'reasoning_vq.layers.0._codebook.initted', 'reasoning_vq.layers.0._codebook.cluster_size', 'reasoning_vq.layers.0._codebook.embed_avg', 'reasoning_vq.layers.0._codebook.embed', 'reasoning_vq.layers.1._codebook.initted', 'reasoning_vq.layers.1._codebook.cluster_size', 'reasoning_vq.layers.1._codebook.embed_avg', 'reasoning_vq.layers.1._codebook.embed', 'reasoning_vq.layers.2._codebook.initted', 'reasoning_vq.layers.2._codebook.cluster_size', 'reasoning_vq.layers.2._codebook.embed_avg', 'reasoning_vq.layers.2._codebook.embed', 'reasoning_vq.layers.3._codebook.initted', 'reasoning_vq.layers.3._codebook.cluster_size', 'reasoning_vq.layers.3._codebook.embed_avg', 'reasoning_vq.layers.3._codebook.embed', 'reasoning_vq.layers.4._codebook.initted', 'reasoning_vq.layers.4._codebook.cluster_size', 'reasoning_vq.layers.4._codebook.embed_avg', 'reasoning_vq.layers.4._codebook.embed', 'reasoning_vq.layers.5._codebook.initted', 'reasoning_vq.layers.5._codebook.cluster_size', 'reasoning_vq.layers.5._codebook.embed_avg', 'reasoning_vq.layers.5._codebook.embed', 'reasoning_vq.layers.6._codebook.initted', 'reasoning_vq.layers.6._codebook.cluster_size', 'reasoning_vq.layers.6._codebook.embed_avg', 'reasoning_vq.layers.6._codebook.embed', 'reasoning_vq.layers.7._codebook.initted', 'reasoning_vq.layers.7._codebook.cluster_size', 'reasoning_vq.layers.7._codebook.embed_avg', 'reasoning_vq.layers.7._codebook.embed', 'down_sampling_layer_whisper.weight', 'down_sampling_layer_whisper.bias'])

[LoRA‑Loader]  matched=241  mismatched=0  missing=254  unexpected=108
 →  254  keys present in model but missing in ckpt, e.g.:
llama_model.base_model.model.model.embed_tokens.weight
llama_model.base_model.model.model.layers.0.self_attn.q_proj.base_layer.weight
llama_model.base_model.model.model.layers.0.self_attn.k_proj.weight
llama_model.base_model.model.model.layers.0.self_attn.v_proj.base_layer.weight
llama_model.base_model.model.model.layers.0.self_attn.o_proj.weight
llama_model.base_model.model.model.layers.0.mlp.gate_proj.weight
llama_model.base_model.model.model.layers.0.mlp.up_proj.weight
llama_model.base_model.model.model.layers.0.mlp.down_proj.weight
llama_model.base_model.model.model.layers.0.input_layernorm.weight
llama_model.base_model.model.model.layers.0.post_attention_layernorm.weight
llama_model.base_model.model.model.layers.1.self_attn.q_proj.base_layer.weight
llama_model.base_model.model.model.layers.1.self_attn.k_proj.weight
llama_model.base_model.model.model.layers.1.self_attn.v_proj.base_layer.weight
llama_model.base_model.model.model.layers.1.self_attn.o_proj.weight
llama_model.base_model.model.model.layers.1.mlp.gate_proj.weight
llama_model.base_model.model.model.layers.1.mlp.up_proj.weight
llama_model.base_model.model.model.layers.1.mlp.down_proj.weight
llama_model.base_model.model.model.layers.1.input_layernorm.weight
llama_model.base_model.model.model.layers.1.post_attention_layernorm.weight
llama_model.base_model.model.model.layers.2.self_attn.q_proj.base_layer.weight ...
 →  108  keys in ckpt but not used, e.g.:
llm_mapping.weight
llm_mapping.bias
muencoder.model.preprocessor_melspec_2048.mel_stft.spectrogram.window
muencoder.model.preprocessor_melspec_2048.mel_stft.mel_scale.fb
muencoder.model.rvq.quantizers.0.stale_counter
muencoder.model.rvq.quantizers.1.stale_counter
muencoder.model.rvq.quantizers.2.stale_counter
muencoder.model.rvq.quantizers.3.stale_counter
muencoder.model.rvq.quantizers.4.stale_counter
muencoder.model.rvq.quantizers.5.stale_counter
muencoder.model.rvq.quantizers.6.stale_counter
muencoder.model.rvq.quantizers.7.stale_counter
muencoder.model.conv.conv.0.bn1.running_mean
muencoder.model.conv.conv.0.bn1.running_var
muencoder.model.conv.conv.0.bn1.num_batches_tracked
muencoder.model.conv.conv.0.bn2.running_mean
muencoder.model.conv.conv.0.bn2.running_var
muencoder.model.conv.conv.0.bn2.num_batches_tracked
muencoder.model.conv.conv.0.bn3.running_mean
muencoder.model.conv.conv.0.bn3.running_var ...
[LoRA‑Loader]  done.

loadable_state  dict_keys(['zero_cond_embedding1', 'whisper_encoder.conv1.weight', 'whisper_encoder.conv1.bias', 'whisper_encoder.conv2.weight', 'whisper_encoder.conv2.bias', 'whisper_encoder.embed_positions.weight', 'whisper_encoder.layers.0.self_attn.k_proj.weight', 'whisper_encoder.layers.0.self_attn.v_proj.weight', 'whisper_encoder.layers.0.self_attn.v_proj.bias', 'whisper_encoder.layers.0.self_attn.q_proj.weight', 'whisper_encoder.layers.0.self_attn.q_proj.bias', 'whisper_encoder.layers.0.self_attn.out_proj.weight', 'whisper_encoder.layers.0.self_attn.out_proj.bias', 'whisper_encoder.layers.0.self_attn_layer_norm.weight', 'whisper_encoder.layers.0.self_attn_layer_norm.bias', 'whisper_encoder.layers.0.fc1.weight', 'whisper_encoder.layers.0.fc1.bias', 'whisper_encoder.layers.0.fc2.weight', 'whisper_encoder.layers.0.fc2.bias', 'whisper_encoder.layers.0.final_layer_norm.weight', 'whisper_encoder.layers.0.final_layer_norm.bias', 'whisper_encoder.layers.1.self_attn.k_proj.weight', 'whisper_encoder.layers.1.self_attn.v_proj.weight', 'whisper_encoder.layers.1.self_attn.v_proj.bias', 'whisper_encoder.layers.1.self_attn.q_proj.weight', 'whisper_encoder.layers.1.self_attn.q_proj.bias', 'whisper_encoder.layers.1.self_attn.out_proj.weight', 'whisper_encoder.layers.1.self_attn.out_proj.bias', 'whisper_encoder.layers.1.self_attn_layer_norm.weight', 'whisper_encoder.layers.1.self_attn_layer_norm.bias', 'whisper_encoder.layers.1.fc1.weight', 'whisper_encoder.layers.1.fc1.bias', 'whisper_encoder.layers.1.fc2.weight', 'whisper_encoder.layers.1.fc2.bias', 'whisper_encoder.layers.1.final_layer_norm.weight', 'whisper_encoder.layers.1.final_layer_norm.bias', 'whisper_encoder.layers.2.self_attn.k_proj.weight', 'whisper_encoder.layers.2.self_attn.v_proj.weight', 'whisper_encoder.layers.2.self_attn.v_proj.bias', 'whisper_encoder.layers.2.self_attn.q_proj.weight', 'whisper_encoder.layers.2.self_attn.q_proj.bias', 'whisper_encoder.layers.2.self_attn.out_proj.weight', 'whisper_encoder.layers.2.self_attn.out_proj.bias', 'whisper_encoder.layers.2.self_attn_layer_norm.weight', 'whisper_encoder.layers.2.self_attn_layer_norm.bias', 'whisper_encoder.layers.2.fc1.weight', 'whisper_encoder.layers.2.fc1.bias', 'whisper_encoder.layers.2.fc2.weight', 'whisper_encoder.layers.2.fc2.bias', 'whisper_encoder.layers.2.final_layer_norm.weight', 'whisper_encoder.layers.2.final_layer_norm.bias', 'whisper_encoder.layers.3.self_attn.k_proj.weight', 'whisper_encoder.layers.3.self_attn.v_proj.weight', 'whisper_encoder.layers.3.self_attn.v_proj.bias', 'whisper_encoder.layers.3.self_attn.q_proj.weight', 'whisper_encoder.layers.3.self_attn.q_proj.bias', 'whisper_encoder.layers.3.self_attn.out_proj.weight', 'whisper_encoder.layers.3.self_attn.out_proj.bias', 'whisper_encoder.layers.3.self_attn_layer_norm.weight', 'whisper_encoder.layers.3.self_attn_layer_norm.bias', 'whisper_encoder.layers.3.fc1.weight', 'whisper_encoder.layers.3.fc1.bias', 'whisper_encoder.layers.3.fc2.weight', 'whisper_encoder.layers.3.fc2.bias', 'whisper_encoder.layers.3.final_layer_norm.weight', 'whisper_encoder.layers.3.final_layer_norm.bias', 'whisper_encoder.layers.4.self_attn.k_proj.weight', 'whisper_encoder.layers.4.self_attn.v_proj.weight', 'whisper_encoder.layers.4.self_attn.v_proj.bias', 'whisper_encoder.layers.4.self_attn.q_proj.weight', 'whisper_encoder.layers.4.self_attn.q_proj.bias', 'whisper_encoder.layers.4.self_attn.out_proj.weight', 'whisper_encoder.layers.4.self_attn.out_proj.bias', 'whisper_encoder.layers.4.self_attn_layer_norm.weight', 'whisper_encoder.layers.4.self_attn_layer_norm.bias', 'whisper_encoder.layers.4.fc1.weight', 'whisper_encoder.layers.4.fc1.bias', 'whisper_encoder.layers.4.fc2.weight', 'whisper_encoder.layers.4.fc2.bias', 'whisper_encoder.layers.4.final_layer_norm.weight', 'whisper_encoder.layers.4.final_layer_norm.bias', 'whisper_encoder.layers.5.self_attn.k_proj.weight', 'whisper_encoder.layers.5.self_attn.v_proj.weight', 'whisper_encoder.layers.5.self_attn.v_proj.bias', 'whisper_encoder.layers.5.self_attn.q_proj.weight', 'whisper_encoder.layers.5.self_attn.q_proj.bias', 'whisper_encoder.layers.5.self_attn.out_proj.weight', 'whisper_encoder.layers.5.self_attn.out_proj.bias', 'whisper_encoder.layers.5.self_attn_layer_norm.weight', 'whisper_encoder.layers.5.self_attn_layer_norm.bias', 'whisper_encoder.layers.5.fc1.weight', 'whisper_encoder.layers.5.fc1.bias', 'whisper_encoder.layers.5.fc2.weight', 'whisper_encoder.layers.5.fc2.bias', 'whisper_encoder.layers.5.final_layer_norm.weight', 'whisper_encoder.layers.5.final_layer_norm.bias', 'whisper_encoder.layers.6.self_attn.k_proj.weight', 'whisper_encoder.layers.6.self_attn.v_proj.weight', 'whisper_encoder.layers.6.self_attn.v_proj.bias', 'whisper_encoder.layers.6.self_attn.q_proj.weight', 'whisper_encoder.layers.6.self_attn.q_proj.bias', 'whisper_encoder.layers.6.self_attn.out_proj.weight', 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'cfm_wrapper.estimator.adaln_single.emb.timestep_embedder.linear_1.weight', 'cfm_wrapper.estimator.adaln_single.emb.timestep_embedder.linear_1.bias', 'cfm_wrapper.estimator.adaln_single.emb.timestep_embedder.linear_2.weight', 'cfm_wrapper.estimator.adaln_single.emb.timestep_embedder.linear_2.bias', 'cfm_wrapper.estimator.adaln_single.linear.weight', 'cfm_wrapper.estimator.adaln_single.linear.bias'])

[LoRA‑Loader]  matched=1816  mismatched=0  missing=503  unexpected=0
 →  503  keys present in model but missing in ckpt, e.g.:
time_film_phone.weight
time_film_phone.bias
time_film_semantic.weight
time_film_semantic.bias
time_film_acoustic.weight
time_film_acoustic.bias
reason_adaptor.weight
reason_adaptor.bias
audio_thinking.cls_token
audio_thinking.encoder_transformers.0.self_attn.to_qkv.parametrizations.weight.original0
audio_thinking.encoder_transformers.0.self_attn.to_qkv.parametrizations.weight.original1
audio_thinking.encoder_transformers.0.self_attn.to_out.parametrizations.weight.original0
audio_thinking.encoder_transformers.0.self_attn.to_out.parametrizations.weight.original1
audio_thinking.encoder_transformers.0.self_attn.q_norm.weight
audio_thinking.encoder_transformers.0.self_attn.q_norm.bias
audio_thinking.encoder_transformers.0.self_attn.k_norm.weight
audio_thinking.encoder_transformers.0.self_attn.k_norm.bias
audio_thinking.encoder_transformers.0.self_attn_scale.scale
audio_thinking.encoder_transformers.0.ff.ff.0.proj.bias
audio_thinking.encoder_transformers.0.ff.ff.0.proj.parametrizations.weight.original0 ...
[LoRA‑Loader]  done.

Successfully loaded checkpoint from: /turing_music_fs/music_data/ydc/exp2/tmp_codec/reasoncodec_1024/reason_codec.checkpoint
2025-11-10 16:04:43,558 INFO [offline_tokenization_scp.py:68] tokenizer built
2025-11-10 16:04:43,560 INFO [offline_tokenization_scp.py:79] [Rank 2] Final output will be at: /turing_music_fs/music_data/ydc/code2/TokenPPL/data/music/test/16splits/reason_tokens.3.pt
2025-11-10 16:04:43,560 INFO [offline_tokenization_scp.py:80] [Rank 2] Checkpoint path is: /turing_music_fs/music_data/ydc/code2/TokenPPL/data/music/test/16splits/semantic_tokens.3.pt.checkpoint_rec.pth, /turing_music_fs/music_data/ydc/code2/TokenPPL/data/music/test/16splits/reason_tokens.3.pt.checkpoint_reason.pth
2025-11-10 16:04:43,560 INFO [offline_tokenization_scp.py:97] [Rank 2] No checkpoint found. Starting from scratch.

[Rank 2] Tokenizing:   0%|          | 0/13 [00:00<?, ?file/s]/turing_music_fs/music_data/ydc/code2/TokenPPL/Token_LM_dual/tools/tokenizer/ReasoningCodec_film_1024/models/AudioDiffusion1D.py:662: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
  with torch.cuda.amp.autocast(enabled=False):
/root/miniconda3/envs/uniaudio2/lib/python3.10/contextlib.py:103: FutureWarning: `torch.backends.cuda.sdp_kernel()` is deprecated. In the future, this context manager will be removed. Please see `torch.nn.attention.sdpa_kernel()` for the new context manager, with updated signature.
  self.gen = func(*args, **kwds)

[Rank 2] Tokenizing:   8%|▊         | 1/13 [00:01<00:15,  1.26s/file]
[Rank 2] Tokenizing:  15%|█▌        | 2/13 [00:01<00:09,  1.20file/s]
[Rank 2] Tokenizing:  23%|██▎       | 3/13 [00:02<00:06,  1.62file/s]
[Rank 2] Tokenizing:  31%|███       | 4/13 [00:02<00:05,  1.65file/s]
[Rank 2] Tokenizing:  38%|███▊      | 5/13 [00:03<00:04,  1.63file/s]
[Rank 2] Tokenizing:  46%|████▌     | 6/13 [00:03<00:04,  1.65file/s]
[Rank 2] Tokenizing:  54%|█████▍    | 7/13 [00:04<00:03,  1.65file/s]
[Rank 2] Tokenizing:  62%|██████▏   | 8/13 [00:05<00:03,  1.65file/s]
[Rank 2] Tokenizing:  69%|██████▉   | 9/13 [00:05<00:02,  1.65file/s]
[Rank 2] Tokenizing:  77%|███████▋  | 10/13 [00:06<00:01,  1.65file/s]
[Rank 2] Tokenizing:  85%|████████▍ | 11/13 [00:07<00:01,  1.64file/s]
[Rank 2] Tokenizing:  92%|█████████▏| 12/13 [00:07<00:00,  1.65file/s]
[Rank 2] Tokenizing: 100%|██████████| 13/13 [00:08<00:00,  1.65file/s]
[Rank 2] Tokenizing: 100%|██████████| 13/13 [00:08<00:00,  1.58file/s]
2025-11-10 16:04:51,781 INFO [offline_tokenization_scp.py:166] [Rank 2] Processing complete. Saving final data...
2025-11-10 16:04:51,783 INFO [offline_tokenization_scp.py:169] [Rank 2] Final data saved to /turing_music_fs/music_data/ydc/code2/TokenPPL/data/music/test/16splits/reason_tokens.3.pt with 13 entries.
2025-11-10 16:04:51,920 INFO [offline_tokenization_scp.py:185] [Rank 2] Final GPU cache cleanup completed
2025-11-10 16:04:51,990 DEBUG [_api.py:331] Attempting to acquire lock 139923780301584 on /root/.triton/autotune/Fp16Matmul_2d_kernel.pickle.lock
2025-11-10 16:04:51,990 DEBUG [_api.py:334] Lock 139923780301584 acquired on /root/.triton/autotune/Fp16Matmul_2d_kernel.pickle.lock
2025-11-10 16:04:51,991 DEBUG [_api.py:364] Attempting to release lock 139923780301584 on /root/.triton/autotune/Fp16Matmul_2d_kernel.pickle.lock
2025-11-10 16:04:51,991 DEBUG [_api.py:367] Lock 139923780301584 released on /root/.triton/autotune/Fp16Matmul_2d_kernel.pickle.lock
2025-11-10 16:04:51,993 DEBUG [_api.py:331] Attempting to acquire lock 139923780303696 on /root/.triton/autotune/Fp16Matmul_4d_kernel.pickle.lock
2025-11-10 16:04:51,994 DEBUG [_api.py:334] Lock 139923780303696 acquired on /root/.triton/autotune/Fp16Matmul_4d_kernel.pickle.lock
2025-11-10 16:04:51,994 DEBUG [_api.py:364] Attempting to release lock 139923780303696 on /root/.triton/autotune/Fp16Matmul_4d_kernel.pickle.lock
2025-11-10 16:04:51,994 DEBUG [_api.py:367] Lock 139923780303696 released on /root/.triton/autotune/Fp16Matmul_4d_kernel.pickle.lock
# Accounting: time=152 threads=1
# Ended (code 0) at Mon Nov 10 16:04:56 UTC 2025, elapsed time 152 seconds