diff --git "a/music/test/16splits/log/audio_tokenizer.2.log" "b/music/test/16splits/log/audio_tokenizer.2.log"
new file mode 100644--- /dev/null
+++ "b/music/test/16splits/log/audio_tokenizer.2.log"
@@ -0,0 +1,165 @@
+# python egs/pretraining/data_scripts/offline_tokenization_scp.py --rank 2 --input-file /turing_music_fs/music_data/ydc/code2/TokenPPL/data/music/test/16splits/wav.2.scp --output_file_reason /turing_music_fs/music_data/ydc/code2/TokenPPL/data/music/test/16splits/reason_tokens.2.pt --output_file_semantic /turing_music_fs/music_data/ydc/code2/TokenPPL/data/music/test/16splits/semantic_tokens.2.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 16:02:26,316 INFO [offline_tokenization_scp.py:44] max gpu 8
+2025-11-10 16:02:26,316 INFO [offline_tokenization_scp.py:47] Using device: cuda:1
+2025-11-10 16:02:27,906 DEBUG [__init__.py:44] Skipping import of cpp extensions
+[2025-11-10 16:02:32,411] [INFO] [real_accelerator.py:222:get_accelerator] Setting ds_accelerator to cuda (auto detect)
+2025-11-10 16:02:32,496 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/tmpkqqz60he/test.c -o /tmp/tmpkqqz60he/test.o
+2025-11-10 16:02:32,512 INFO [spawn.py:60] gcc -pthread -B /root/miniconda3/envs/uniaudio2/compiler_compat /tmp/tmpkqqz60he/test.o -laio -o /tmp/tmpkqqz60he/a.out
+2025-11-10 16:02:33,217 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/tmpb9odtmob/test.c -o /tmp/tmpb9odtmob/test.o
+2025-11-10 16:02:33,233 INFO [spawn.py:60] gcc -pthread -B /root/miniconda3/envs/uniaudio2/compiler_compat /tmp/tmpb9odtmob/test.o -L/usr/local/cuda -L/usr/local/cuda/lib64 -lcufile -o /tmp/tmpb9odtmob/a.out
+2025-11-10 16:02:34,058 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 16:02:34,060 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 16:02:34,318 DEBUG [auth.py:50] Trying paths: ['/root/.docker/config.json', '/root/.dockercfg']
+2025-11-10 16:02:34,318 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 16:02:35,795 DEBUG [__init__.py:342] matplotlib data path: /root/miniconda3/envs/uniaudio2/lib/python3.10/site-packages/matplotlib/mpl-data
+2025-11-10 16:02:35,800 DEBUG [__init__.py:342] CONFIGDIR=/root/.config/matplotlib
+2025-11-10 16:02:35,802 DEBUG [__init__.py:1557] interactive is False
+2025-11-10 16:02:35,802 DEBUG [__init__.py:1558] platform is linux
+2025-11-10 16:02:35,822 DEBUG [__init__.py:342] CACHEDIR=/root/.cache/matplotlib
+2025-11-10 16:02:35,824 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/tmp6f27pk0g']
+2025-11-10 16:02:42,983 DEBUG [__init__.py:47] Creating converter from 7 to 5
+2025-11-10 16:02:42,983 DEBUG [__init__.py:47] Creating converter from 5 to 7
+2025-11-10 16:02:42,984 DEBUG [__init__.py:47] Creating converter from 7 to 5
+2025-11-10 16:02:42,984 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 16:02:46,243 INFO [mert_pretraining.py:253] current directory is /turing_music_fs/music_data/ydc/code2/TokenPPL/Token_LM_dual
+2025-11-10 16:02:46,243 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 [00:05<00:05,  5.81s/it]
Loading checkpoint shards: 100%|██████████| 2/2 [00:07<00:00,  3.39s/it]
Loading checkpoint shards: 100%|██████████| 2/2 [00:07<00:00,  3.75s/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 16:04:30,469 INFO [offline_tokenization_scp.py:68] tokenizer built
+2025-11-10 16:04:30,472 INFO [offline_tokenization_scp.py:79] [Rank 1] Final output will be at: /turing_music_fs/music_data/ydc/code2/TokenPPL/data/music/test/16splits/reason_tokens.2.pt
+2025-11-10 16:04:30,472 INFO [offline_tokenization_scp.py:80] [Rank 1] Checkpoint path is: /turing_music_fs/music_data/ydc/code2/TokenPPL/data/music/test/16splits/semantic_tokens.2.pt.checkpoint_rec.pth, /turing_music_fs/music_data/ydc/code2/TokenPPL/data/music/test/16splits/reason_tokens.2.pt.checkpoint_reason.pth
+2025-11-10 16:04:30,472 INFO [offline_tokenization_scp.py:97] [Rank 1] No checkpoint found. Starting from scratch.
+
[Rank 1] 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 1] Tokenizing:   8%|▊         | 1/13 [00:01<00:13,  1.12s/file]
[Rank 1] Tokenizing:  15%|█▌        | 2/13 [00:01<00:07,  1.54file/s]
[Rank 1] Tokenizing:  23%|██▎       | 3/13 [00:01<00:05,  2.00file/s]
[Rank 1] Tokenizing:  31%|███       | 4/13 [00:02<00:03,  2.31file/s]
[Rank 1] Tokenizing:  38%|███▊      | 5/13 [00:02<00:03,  2.55file/s]
[Rank 1] Tokenizing:  46%|████▌     | 6/13 [00:02<00:02,  2.72file/s]
[Rank 1] Tokenizing:  54%|█████▍    | 7/13 [00:03<00:02,  2.84file/s]
[Rank 1] Tokenizing:  62%|██████▏   | 8/13 [00:03<00:01,  2.92file/s]
[Rank 1] Tokenizing:  69%|██████▉   | 9/13 [00:03<00:01,  2.99file/s]
[Rank 1] Tokenizing:  77%|███████▋  | 10/13 [00:04<00:00,  3.02file/s]
[Rank 1] Tokenizing:  85%|████████▍ | 11/13 [00:04<00:00,  3.05file/s]
[Rank 1] Tokenizing:  92%|█████████▏| 12/13 [00:04<00:00,  3.06file/s]
[Rank 1] Tokenizing: 100%|██████████| 13/13 [00:04<00:00,  3.08file/s]
[Rank 1] Tokenizing: 100%|██████████| 13/13 [00:04<00:00,  2.61file/s]
+2025-11-10 16:04:35,453 INFO [offline_tokenization_scp.py:166] [Rank 1] Processing complete. Saving final data...
+2025-11-10 16:04:35,455 INFO [offline_tokenization_scp.py:169] [Rank 1] Final data saved to /turing_music_fs/music_data/ydc/code2/TokenPPL/data/music/test/16splits/reason_tokens.2.pt with 13 entries.
+2025-11-10 16:04:35,466 INFO [offline_tokenization_scp.py:185] [Rank 1] Final GPU cache cleanup completed
+2025-11-10 16:04:35,538 DEBUG [_api.py:331] Attempting to acquire lock 140243613905680 on /root/.triton/autotune/Fp16Matmul_2d_kernel.pickle.lock
+2025-11-10 16:04:35,538 DEBUG [_api.py:334] Lock 140243613905680 acquired on /root/.triton/autotune/Fp16Matmul_2d_kernel.pickle.lock
+2025-11-10 16:04:35,538 DEBUG [_api.py:364] Attempting to release lock 140243613905680 on /root/.triton/autotune/Fp16Matmul_2d_kernel.pickle.lock
+2025-11-10 16:04:35,538 DEBUG [_api.py:367] Lock 140243613905680 released on /root/.triton/autotune/Fp16Matmul_2d_kernel.pickle.lock
+2025-11-10 16:04:35,541 DEBUG [_api.py:331] Attempting to acquire lock 140243613907792 on /root/.triton/autotune/Fp16Matmul_4d_kernel.pickle.lock
+2025-11-10 16:04:35,541 DEBUG [_api.py:334] Lock 140243613907792 acquired on /root/.triton/autotune/Fp16Matmul_4d_kernel.pickle.lock
+2025-11-10 16:04:35,541 DEBUG [_api.py:364] Attempting to release lock 140243613907792 on /root/.triton/autotune/Fp16Matmul_4d_kernel.pickle.lock
+2025-11-10 16:04:35,541 DEBUG [_api.py:367] Lock 140243613907792 released on /root/.triton/autotune/Fp16Matmul_4d_kernel.pickle.lock
+# Accounting: time=135 threads=1
+# Ended (code 0) at Mon Nov 10 16:04:39 UTC 2025, elapsed time 135 seconds