diff --git "a/music/test/16splits/log/audio_tokenizer.3.log" "b/music/test/16splits/log/audio_tokenizer.3.log"
new file mode 100644--- /dev/null
+++ "b/music/test/16splits/log/audio_tokenizer.3.log"
@@ -0,0 +1,165 @@
+# 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