2023-12-20 17:30:48,670 INFO [train.py:953] (2/4) Training started
2023-12-20 17:30:48,670 INFO [train.py:963] (2/4) Device: cuda:2
2023-12-20 17:30:48,670 INFO [train.py:965] (2/4) {'best_train_loss': inf, 'best_valid_loss': inf, 'best_train_epoch': -1, 'best_valid_epoch': -1, 'batch_idx_train': 0, 'log_interval': 50, 'reset_interval': 200, 'valid_interval': 3000, 'feature_dim': 80, 'subsampling_factor': 4, 'warm_step': 2000, 'env_info': {'k2-version': '1.24.3', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': '2b2ac14b326d61d79d04e53fbd69b1ff6d630411', 'k2-git-date': 'Thu Aug 24 05:58:26 2023', 'lhotse-version': '0.0.0+unknown.version', 'torch-version': '2.0.1+cu117', 'torch-cuda-available': True, 'torch-cuda-version': '11.7', 'python-version': '3.1', 'icefall-git-branch': 'audio_tagging', 'icefall-git-sha1': 'bd01c212-clean', 'icefall-git-date': 'Tue Dec 19 17:20:49 2023', 'icefall-path': '/star-xy/softwares/icefall_development/icefall_audio_tagging', 'k2-path': '/star-xy/softwares/k2_development/k2/k2/python/k2/__init__.py', 'lhotse-path': '/star-xy/softwares/lhotse_development/lhotse_at/lhotse/__init__.py', 'hostname': 'de-74279-k2-train-7-1218101249-5bcbfb5567-jsftr', 'IP address': '10.177.6.147'}, 'world_size': 4, 'master_port': 13455, 'tensorboard': True, 'num_epochs': 50, 'start_epoch': 1, 'start_batch': 0, 'exp_dir': PosixPath('zipformer/exp_at_as_full'), 'base_lr': 0.045, 'lr_batches': 7500, 'lr_epochs': 3.5, 'ref_duration': 600, 'seed': 42, 'print_diagnostics': False, 'inf_check': False, 'save_every_n': 4000, 'keep_last_k': 30, 'average_period': 200, 'use_fp16': True, 'num_encoder_layers': '2,2,3,4,3,2', 'downsampling_factor': '1,2,4,8,4,2', 'feedforward_dim': '512,768,1024,1536,1024,768', 'num_heads': '4,4,4,8,4,4', 'encoder_dim': '192,256,384,512,384,256', 'query_head_dim': '32', 'value_head_dim': '12', 'pos_head_dim': '4', 'pos_dim': 48, 'encoder_unmasked_dim': '192,192,256,256,256,192', 'cnn_module_kernel': '31,31,15,15,15,31', 'causal': False, 'chunk_size': '16,32,64,-1', 'left_context_frames': '64,128,256,-1', 'num_events': 527, 'audioset_subset': 'full', 'manifest_dir': PosixPath('data/fbank'), 'max_duration': 1000, 'bucketing_sampler': True, 'num_buckets': 30, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': True, 'drop_last': True, 'return_cuts': True, 'num_workers': 2, 'enable_spec_aug': True, 'spec_aug_time_warp_factor': 80, 'enable_musan': True, 'input_strategy': 'PrecomputedFeatures'}
2023-12-20 17:30:48,670 INFO [train.py:967] (2/4) About to create model
2023-12-20 17:30:54,289 INFO [train.py:971] (2/4) Number of model parameters: 64264454
2023-12-20 17:30:56,960 INFO [train.py:986] (2/4) Using DDP
2023-12-20 17:30:57,435 INFO [at_datamodule.py:398] (2/4) About to get the audioset cuts for KD.
2023-12-20 17:30:57,498 INFO [at_datamodule.py:223] (2/4) Enable MUSAN
2023-12-20 17:30:57,498 INFO [at_datamodule.py:224] (2/4) About to get Musan cuts
2023-12-20 17:30:59,983 INFO [at_datamodule.py:248] (2/4) Enable SpecAugment
2023-12-20 17:30:59,983 INFO [at_datamodule.py:249] (2/4) Time warp factor: 80
2023-12-20 17:30:59,984 INFO [at_datamodule.py:259] (2/4) Num frame mask: 10
2023-12-20 17:30:59,984 INFO [at_datamodule.py:272] (2/4) About to create train dataset
2023-12-20 17:30:59,984 INFO [at_datamodule.py:299] (2/4) Using DynamicBucketingSampler.
2023-12-20 17:31:02,097 INFO [at_datamodule.py:315] (2/4) About to create train dataloader
2023-12-20 17:31:02,098 INFO [at_datamodule.py:410] (2/4) About to get test-other cuts
2023-12-20 17:31:02,100 INFO [at_datamodule.py:346] (2/4) About to create dev dataset
2023-12-20 17:31:02,576 INFO [at_datamodule.py:363] (2/4) About to create dev dataloader
2023-12-20 17:31:25,020 INFO [train.py:886] (2/4) Epoch 1, batch 0, loss[loss=2.283, audio_tagging_loss=2.283, over 20581.00 frames. ], tot_loss[loss=2.283, audio_tagging_loss=2.283, over 20581.00 frames. ], batch size: 106, lr: 2.25e-02, grad_scale: 2.0
2023-12-20 17:31:25,021 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 17:31:46,185 INFO [train.py:917] (2/4) Epoch 1, validation: loss=1.716, audio_tagging_loss=1.716, over 3737520.00 frames. 
2023-12-20 17:31:46,186 INFO [train.py:918] (2/4) Maximum memory allocated so far is 13081MB
2023-12-20 17:31:48,380 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.2.balancer2.prob, batch_count=0.0, ans=0.5
2023-12-20 17:31:50,482 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.0.feed_forward1.out_proj.dropout_p, batch_count=0.0, ans=0.3
2023-12-20 17:31:53,733 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.1.out_combiner.scale_min, batch_count=0.0, ans=0.9
2023-12-20 17:31:54,915 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.0.balancer1.prob, batch_count=0.0, ans=0.5
2023-12-20 17:31:56,787 WARNING [optim.py:484] (2/4) Clipping_scale=2.0, grad-norm quartiles 7.044e+02 8.568e+02 1.002e+03 1.369e+03 1.715e+03, threshold=4.006e+03, percent-clipped=0.0
2023-12-20 17:31:58,195 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.5.encoder.layers.1.bypass_mid.scale_min, batch_count=66.66666666666667, ans=0.8976666666666667
2023-12-20 17:31:59,471 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.2.conv_module2.whiten, num_groups=1, num_channels=384, metric=148.42 vs. limit=7.525
2023-12-20 17:32:01,115 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.1.encoder.layers.1.feed_forward1.hidden_balancer.prob, batch_count=66.66666666666667, ans=0.496875
2023-12-20 17:32:07,424 WARNING [optim.py:484] (2/4) Clipping_scale=2.0, grad-norm quartiles 7.268e+01 3.256e+02 7.044e+02 1.161e+03 1.783e+03, threshold=2.818e+03, percent-clipped=0.0
2023-12-20 17:32:09,839 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.2.nonlin_attention.balancer.max_positive, batch_count=133.33333333333334, ans=0.7513333333333333
2023-12-20 17:32:13,323 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.0.whiten, num_groups=1, num_channels=256, metric=60.35 vs. limit=4.053333333333334
2023-12-20 17:32:27,232 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.0.layers.1.feed_forward3.out_whiten, num_groups=1, num_channels=192, metric=60.54 vs. limit=7.575
2023-12-20 17:32:30,516 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.1.encoder.layers.1.nonlin_attention.whiten2, num_groups=1, num_channels=256, metric=222.77 vs. limit=5.133333333333334
2023-12-20 17:32:30,896 WARNING [optim.py:484] (2/4) Clipping_scale=2.0, grad-norm quartiles 3.273e+01 1.290e+02 2.793e+02 8.337e+02 1.783e+03, threshold=1.117e+03, percent-clipped=0.0
2023-12-20 17:32:32,702 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.1.encoder.layers.0.feed_forward1.out_whiten, num_groups=1, num_channels=256, metric=17.53 vs. limit=7.6
2023-12-20 17:32:33,402 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.5.encoder.layers.1.feed_forward3.hidden_balancer.prob, batch_count=266.6666666666667, ans=0.4875
2023-12-20 17:32:34,708 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.2.conv_module1.whiten, num_groups=1, num_channels=384, metric=313.04 vs. limit=7.6
2023-12-20 17:32:37,228 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.1.encoder.layers.0.whiten, num_groups=1, num_channels=256, metric=21.24 vs. limit=4.1066666666666665
2023-12-20 17:32:38,469 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.0.layers.0.self_attn1.whiten, num_groups=1, num_channels=192, metric=110.90 vs. limit=7.7
2023-12-20 17:32:39,231 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.3.whiten, num_groups=1, num_channels=512, metric=111.36 vs. limit=4.1066666666666665
2023-12-20 17:32:40,041 INFO [scaling.py:1118] (2/4) WithLoss: name=encoder.encoders.3.encoder.layers.1.self_attn_weights, loss-sum=0.000e+00
2023-12-20 17:32:41,292 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.0.self_attn1.whiten, num_groups=1, num_channels=256, metric=255.36 vs. limit=7.75
2023-12-20 17:32:41,374 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.0.feed_forward3.out_whiten, num_groups=1, num_channels=384, metric=366.89 vs. limit=7.625
2023-12-20 17:32:42,072 INFO [train.py:886] (2/4) Epoch 1, batch 50, loss[loss=0.06074, audio_tagging_loss=0.06074, over 25000.00 frames. ], tot_loss[loss=0.3051, audio_tagging_loss=0.3051, over 1114689.49 frames. ], batch size: 100, lr: 2.48e-02, grad_scale: 2.0
2023-12-20 17:33:00,438 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.0.layers.1.feed_forward2.out_whiten, num_groups=1, num_channels=192, metric=54.11 vs. limit=7.63
2023-12-20 17:33:07,720 INFO [train.py:886] (2/4) Epoch 2, batch 0, loss[loss=0.06753, audio_tagging_loss=0.06753, over 21552.00 frames. ], tot_loss[loss=0.06753, audio_tagging_loss=0.06753, over 21552.00 frames. ], batch size: 106, lr: 2.44e-02, grad_scale: 4.0
2023-12-20 17:33:07,721 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 17:33:15,956 INFO [zipformer.py:1858] (2/4) name=encoder.encoders.1.encoder.layers.0.self_attn_weights, attn_weights_entropy = tensor([5.0380, 5.2993, 4.8652, 5.2336], device='cuda:2')
2023-12-20 17:33:23,169 INFO [zipformer.py:1858] (2/4) name=encoder.encoders.2.encoder.layers.2.self_attn_weights, attn_weights_entropy = tensor([4.8107, 4.8136, 4.8127, 4.8180], device='cuda:2')
2023-12-20 17:33:28,174 INFO [train.py:917] (2/4) Epoch 2, validation: loss=0.0597, audio_tagging_loss=0.0597, over 3737520.00 frames. 
2023-12-20 17:33:28,175 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14643MB
2023-12-20 17:33:32,027 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.2.nonlin_attention.whiten2, num_groups=1, num_channels=512, metric=213.93 vs. limit=5.173333333333334
2023-12-20 17:33:36,055 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.0.feed_forward2.out_whiten, num_groups=1, num_channels=512, metric=496.21 vs. limit=7.63
2023-12-20 17:33:40,925 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.0.layers.0.conv_module2.whiten, num_groups=1, num_channels=192, metric=78.05 vs. limit=7.655
2023-12-20 17:33:41,696 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.0.self_attn2.whiten, num_groups=1, num_channels=384, metric=332.07 vs. limit=7.81
2023-12-20 17:33:42,886 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.1.feed_forward1.out_whiten, num_groups=1, num_channels=512, metric=413.56 vs. limit=7.655
2023-12-20 17:33:47,594 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.0.layers.1.feed_forward2.out_whiten, num_groups=1, num_channels=192, metric=40.32 vs. limit=7.655
2023-12-20 17:33:50,301 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.0.feed_forward1.out_whiten, num_groups=1, num_channels=384, metric=230.04 vs. limit=7.655
2023-12-20 17:33:51,571 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.2.conv_module2.whiten, num_groups=1, num_channels=384, metric=263.30 vs. limit=7.68
2023-12-20 17:33:51,711 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.0.layers.1.feed_forward3.out_whiten.whitening_limit, batch_count=480.0, ans=7.68
2023-12-20 17:33:52,235 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.1.nonlin_attention.balancer.prob, batch_count=480.0, ans=0.4775
2023-12-20 17:34:03,460 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.3.self_attn_weights.whiten_keys, num_groups=8, num_channels=256, metric=5.20 vs. limit=3.082
2023-12-20 17:34:06,524 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.0.conv_skip_rate, batch_count=546.6666666666666, ans=0.17950000000000002
2023-12-20 17:34:06,711 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.1.nonlin_attention.whiten2, num_groups=1, num_channels=256, metric=200.49 vs. limit=5.273333333333333
2023-12-20 17:34:14,753 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.0.whiten, num_groups=1, num_channels=512, metric=15.66 vs. limit=4.245333333333333
2023-12-20 17:34:19,210 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.3.whiten, num_groups=1, num_channels=512, metric=15.54 vs. limit=4.245333333333333
2023-12-20 17:34:21,478 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.2.nonlin_attention.whiten1, num_groups=1, num_channels=288, metric=20.41 vs. limit=5.153333333333333
2023-12-20 17:34:25,459 WARNING [optim.py:484] (2/4) Clipping_scale=2.0, grad-norm quartiles 1.802e+01 2.968e+01 6.154e+01 2.791e+02 2.019e+03, threshold=1.231e+02, percent-clipped=1.0
2023-12-20 17:34:26,577 INFO [train.py:886] (2/4) Epoch 2, batch 50, loss[loss=0.05319, audio_tagging_loss=0.05319, over 25000.00 frames. ], tot_loss[loss=0.05741, audio_tagging_loss=0.05741, over 1123870.55 frames. ], batch size: 100, lr: 2.66e-02, grad_scale: 2.0
2023-12-20 17:34:44,472 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.0.feed_forward1.out_whiten, num_groups=1, num_channels=384, metric=301.26 vs. limit=7.76
2023-12-20 17:34:52,047 INFO [train.py:886] (2/4) Epoch 3, batch 0, loss[loss=0.06629, audio_tagging_loss=0.06629, over 20834.00 frames. ], tot_loss[loss=0.06629, audio_tagging_loss=0.06629, over 20834.00 frames. ], batch size: 106, lr: 2.54e-02, grad_scale: 4.0
2023-12-20 17:34:52,048 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 17:35:12,448 INFO [train.py:917] (2/4) Epoch 3, validation: loss=0.05878, audio_tagging_loss=0.05878, over 3737520.00 frames. 
2023-12-20 17:35:12,448 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14778MB
2023-12-20 17:35:13,766 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.2.feed_forward1.hidden_balancer.prob, batch_count=693.3333333333334, ans=0.4675
2023-12-20 17:35:22,421 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.2.whiten, num_groups=1, num_channels=512, metric=13.84 vs. limit=4.277333333333333
2023-12-20 17:35:29,437 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.0.conv_module1.balancer1.max_abs, batch_count=760.0, ans=5.475
2023-12-20 17:35:31,698 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.1.self_attn2.whiten, num_groups=1, num_channels=384, metric=146.93 vs. limit=8.07
2023-12-20 17:35:33,492 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.2.conv_skip_rate, batch_count=760.0, ans=0.1715
2023-12-20 17:35:41,630 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.0.conv_module1.whiten, num_groups=1, num_channels=256, metric=182.75 vs. limit=7.81
2023-12-20 17:35:45,467 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.0.feed_forward2.out_whiten, num_groups=1, num_channels=256, metric=238.44 vs. limit=7.81
2023-12-20 17:35:48,261 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.0.layers.0.conv_module1.whiten, num_groups=1, num_channels=192, metric=22.94 vs. limit=7.835
2023-12-20 17:35:51,122 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.2.feed_forward1.hidden_balancer.prob, batch_count=893.3333333333334, ans=0.458125
2023-12-20 17:35:55,313 WARNING [optim.py:500] (2/4) Scaling gradients by 0.09217905253171921, model_norm_threshold=123.07855224609375
2023-12-20 17:35:55,463 WARNING [optim.py:572] (2/4) Parameter dominating tot_sumsq module.encoder_embed.conv.7.weight with proportion 0.48, where dominant_sumsq=(grad_sumsq*orig_rms_sq)=8.614e+05, grad_sumsq=6.752e+08, orig_rms_sq=1.276e-03
2023-12-20 17:35:55,891 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.1.feed_forward3.out_whiten, num_groups=1, num_channels=384, metric=348.28 vs. limit=7.835
2023-12-20 17:35:56,730 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.1.encoder.layers.0.feed_forward1.hidden_balancer.prob, batch_count=893.3333333333334, ans=0.458125
2023-12-20 17:36:06,020 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.2.feed_forward1.out_whiten, num_groups=1, num_channels=384, metric=320.93 vs. limit=7.86
2023-12-20 17:36:09,438 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.3.nonlin_attention.whiten2, num_groups=1, num_channels=512, metric=159.67 vs. limit=5.48
2023-12-20 17:36:11,142 INFO [train.py:886] (2/4) Epoch 3, batch 50, loss[loss=0.0548, audio_tagging_loss=0.0548, over 25000.00 frames. ], tot_loss[loss=0.05632, audio_tagging_loss=0.05632, over 1116987.31 frames. ], batch size: 100, lr: 2.75e-02, grad_scale: 4.0
2023-12-20 17:36:11,464 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.0.nonlin_attention.whiten2, num_groups=1, num_channels=256, metric=200.40 vs. limit=5.513333333333334
2023-12-20 17:36:35,802 INFO [train.py:886] (2/4) Epoch 4, batch 0, loss[loss=0.05267, audio_tagging_loss=0.05267, over 25000.00 frames. ], tot_loss[loss=0.05267, audio_tagging_loss=0.05267, over 25000.00 frames. ], batch size: 100, lr: 2.58e-02, grad_scale: 8.0
2023-12-20 17:36:35,803 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 17:36:54,828 INFO [zipformer.py:1858] (2/4) name=encoder.encoders.1.encoder.layers.1.self_attn_weights, attn_weights_entropy = tensor([4.2928, 5.0731, 4.1999, 4.6702], device='cuda:2')
2023-12-20 17:36:55,849 INFO [train.py:917] (2/4) Epoch 4, validation: loss=0.05673, audio_tagging_loss=0.05673, over 3737520.00 frames. 
2023-12-20 17:36:55,850 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14778MB
2023-12-20 17:37:05,033 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.0.layers.1.self_attn2.whiten, num_groups=1, num_channels=192, metric=13.17 vs. limit=8.28
2023-12-20 17:37:11,618 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.0.nonlin_attention.whiten2, num_groups=1, num_channels=256, metric=134.90 vs. limit=5.553333333333334
2023-12-20 17:37:11,986 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.1.encoder.layers.0.feed_forward3.out_whiten, num_groups=1, num_channels=256, metric=103.01 vs. limit=7.915
2023-12-20 17:37:15,307 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.1.encoder.layers.0.whiten, num_groups=1, num_channels=256, metric=7.41 vs. limit=4.442666666666667
2023-12-20 17:37:17,163 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.0.layers.1.whiten, num_groups=1, num_channels=192, metric=5.47 vs. limit=4.442666666666667
2023-12-20 17:37:18,120 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.1.nonlin_attention.whiten2, num_groups=1, num_channels=384, metric=152.91 vs. limit=5.553333333333334
2023-12-20 17:37:19,163 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.0.self_attn1.whiten, num_groups=1, num_channels=512, metric=105.25 vs. limit=8.33
2023-12-20 17:37:22,105 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.1.encoder.layers.0.bypass_mid.scale_min, batch_count=1173.3333333333333, ans=0.8589333333333333
2023-12-20 17:37:22,214 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.1.attention_skip_rate, batch_count=1173.3333333333333, ans=0.156
2023-12-20 17:37:22,415 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.1.nonlin_attention.whiten1, num_groups=1, num_channels=288, metric=35.86 vs. limit=5.293333333333333
2023-12-20 17:37:24,415 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.1.bypass_mid.scale_min, batch_count=1173.3333333333333, ans=0.8589333333333333
2023-12-20 17:37:27,530 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.0.layers.0.feed_forward2.hidden_balancer.prob, batch_count=1173.3333333333333, ans=0.445
2023-12-20 17:37:30,098 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.2.self_attn2.whiten, num_groups=1, num_channels=384, metric=325.36 vs. limit=8.38
2023-12-20 17:37:31,186 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.1.self_attn2.whiten, num_groups=1, num_channels=256, metric=238.31 vs. limit=8.43
2023-12-20 17:37:33,213 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.1.encoder.layers.1.feed_forward1.out_proj.dropout_p, batch_count=1240.0, ans=0.28759999999999997
2023-12-20 17:37:33,470 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.2.conv_module1.whiten, num_groups=1, num_channels=384, metric=114.42 vs. limit=7.965
2023-12-20 17:37:34,896 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.0.feed_forward1.out_whiten, num_groups=1, num_channels=384, metric=332.68 vs. limit=7.965
2023-12-20 17:37:38,260 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.1.encoder.layers.0.feed_forward2.out_whiten, num_groups=1, num_channels=256, metric=162.86 vs. limit=7.965
2023-12-20 17:37:40,255 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.0.feed_forward2.out_whiten, num_groups=1, num_channels=384, metric=131.95 vs. limit=7.965
2023-12-20 17:37:42,771 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.1.encoder.layers.0.nonlin_attention.whiten2, num_groups=1, num_channels=256, metric=64.16 vs. limit=5.653333333333333
2023-12-20 17:37:49,920 WARNING [optim.py:484] (2/4) Clipping_scale=2.0, grad-norm quartiles 2.093e+01 2.504e+01 2.720e+01 3.182e+01 1.335e+03, threshold=5.440e+01, percent-clipped=1.0
2023-12-20 17:37:54,275 INFO [train.py:886] (2/4) Epoch 4, batch 50, loss[loss=0.05111, audio_tagging_loss=0.05111, over 25000.00 frames. ], tot_loss[loss=0.05369, audio_tagging_loss=0.05369, over 1121668.45 frames. ], batch size: 100, lr: 2.77e-02, grad_scale: 4.0
2023-12-20 17:38:12,172 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.0.feed_forward3.out_whiten, num_groups=1, num_channels=384, metric=295.48 vs. limit=8.02
2023-12-20 17:38:19,535 INFO [train.py:886] (2/4) Epoch 5, batch 0, loss[loss=0.06715, audio_tagging_loss=0.06715, over 20425.00 frames. ], tot_loss[loss=0.06715, audio_tagging_loss=0.06715, over 20425.00 frames. ], batch size: 106, lr: 2.59e-02, grad_scale: 8.0
2023-12-20 17:38:19,536 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 17:38:39,889 INFO [train.py:917] (2/4) Epoch 5, validation: loss=0.05523, audio_tagging_loss=0.05523, over 3737520.00 frames. 
2023-12-20 17:38:39,890 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14778MB
2023-12-20 17:38:44,050 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.0.layers.1.whiten, num_groups=1, num_channels=192, metric=5.36 vs. limit=4.554666666666667
2023-12-20 17:38:47,392 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.1.encoder.layers.0.ff3_skip_rate, batch_count=1386.6666666666667, ans=0.0688
2023-12-20 17:38:54,839 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.1.encoder.layers.1.conv_module2.balancer2.prob, batch_count=1453.3333333333333, ans=0.431875
2023-12-20 17:39:00,571 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.1.feed_forward1.out_proj.dropout_p, batch_count=1453.3333333333333, ans=0.28546666666666665
2023-12-20 17:39:05,393 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.2.self_attn1.whiten, num_groups=1, num_channels=384, metric=96.82 vs. limit=8.64
2023-12-20 17:39:10,046 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.1.nonlin_attention.whiten2.whitening_limit, batch_count=1520.0, ans=5.76
2023-12-20 17:39:14,111 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.1.conv_module1.balancer2.min_abs, batch_count=1520.0, ans=0.2228
2023-12-20 17:39:16,626 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.1.conv_module1.whiten, num_groups=1, num_channels=256, metric=49.08 vs. limit=8.095
2023-12-20 17:39:21,446 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.0.feed_forward1.out_whiten, num_groups=1, num_channels=512, metric=458.13 vs. limit=8.095
2023-12-20 17:39:38,872 INFO [train.py:886] (2/4) Epoch 5, batch 50, loss[loss=0.05064, audio_tagging_loss=0.05064, over 25000.00 frames. ], tot_loss[loss=0.05248, audio_tagging_loss=0.05248, over 1117817.77 frames. ], batch size: 100, lr: 2.77e-02, grad_scale: 8.0
2023-12-20 17:39:39,258 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.2.nonlin_attention.whiten2, num_groups=1, num_channels=384, metric=78.15 vs. limit=5.86
2023-12-20 17:40:04,929 INFO [train.py:886] (2/4) Epoch 6, batch 0, loss[loss=0.04925, audio_tagging_loss=0.04925, over 25000.00 frames. ], tot_loss[loss=0.04925, audio_tagging_loss=0.04925, over 25000.00 frames. ], batch size: 100, lr: 2.59e-02, grad_scale: 16.0
2023-12-20 17:40:04,930 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 17:40:19,729 INFO [zipformer.py:1858] (2/4) name=encoder.encoders.0.layers.1.self_attn_weights, attn_weights_entropy = tensor([4.0755, 3.7526, 5.2326, 3.6878], device='cuda:2')
2023-12-20 17:40:25,816 INFO [train.py:917] (2/4) Epoch 6, validation: loss=0.05425, audio_tagging_loss=0.05425, over 3737520.00 frames. 
2023-12-20 17:40:25,817 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 17:40:26,280 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.0.self_attn1.whiten, num_groups=1, num_channels=384, metric=81.13 vs. limit=8.8
2023-12-20 17:40:28,352 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.1.conv_module1.balancer2.min_positive, batch_count=1733.3333333333333, ans=0.08916666666666667
2023-12-20 17:40:29,609 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.1.nonlin_attention.whiten1, num_groups=1, num_channels=288, metric=22.31 vs. limit=5.433333333333334
2023-12-20 17:40:36,941 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.0.whiten, num_groups=1, num_channels=384, metric=14.60 vs. limit=4.72
2023-12-20 17:40:37,688 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.0.layers.0.bypass_mid.scale_min, batch_count=1800.0, ans=0.837
2023-12-20 17:40:52,541 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.1.conv_module1.balancer1.prob, batch_count=1866.6666666666667, ans=0.4125
2023-12-20 17:40:52,584 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.1.bypass_mid.scale_min, batch_count=1866.6666666666667, ans=0.8346666666666667
2023-12-20 17:40:53,040 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.0.feed_forward2.out_whiten, num_groups=1, num_channels=384, metric=319.38 vs. limit=8.2
2023-12-20 17:40:58,289 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.5.encoder.layers.0.feed_forward1.out_proj.dropout_p, batch_count=1866.6666666666667, ans=0.2813333333333333
2023-12-20 17:41:03,045 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.1.self_attn2.whiten, num_groups=1, num_channels=512, metric=122.10 vs. limit=8.95
2023-12-20 17:41:07,078 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.0.layers.0.feed_forward1.hidden_balancer.prob, batch_count=1933.3333333333333, ans=0.409375
2023-12-20 17:41:07,257 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.5.encoder.layers.1.feed_forward3.hidden_balancer.prob, batch_count=1933.3333333333333, ans=0.409375
2023-12-20 17:41:10,638 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.1.ff2_skip_rate, batch_count=1933.3333333333333, ans=0.05650000000000001
2023-12-20 17:41:10,726 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.1.nonlin_attention.whiten1, num_groups=1, num_channels=192, metric=17.73 vs. limit=5.483333333333333
2023-12-20 17:41:13,404 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.1.encoder.layers.0.self_attn_weights.whiten_keys, num_groups=4, num_channels=128, metric=2.98 vs. limit=3.3
2023-12-20 17:41:14,959 WARNING [optim.py:484] (2/4) Clipping_scale=2.0, grad-norm quartiles 2.090e+01 2.556e+01 2.831e+01 3.472e+01 7.747e+01, threshold=5.662e+01, percent-clipped=6.0
2023-12-20 17:41:18,424 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.1.encoder.layers.0.bypass_mid.scale_min, batch_count=2000.0, ans=0.8300000000000001
2023-12-20 17:41:20,897 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.2.self_attn1.whiten, num_groups=1, num_channels=384, metric=64.62 vs. limit=9.0
2023-12-20 17:41:23,850 INFO [train.py:886] (2/4) Epoch 6, batch 50, loss[loss=0.04601, audio_tagging_loss=0.04601, over 25000.00 frames. ], tot_loss[loss=0.0512, audio_tagging_loss=0.0512, over 1124299.94 frames. ], batch size: 100, lr: 2.76e-02, grad_scale: 16.0
2023-12-20 17:41:49,202 INFO [train.py:886] (2/4) Epoch 7, batch 0, loss[loss=0.05184, audio_tagging_loss=0.05184, over 24101.00 frames. ], tot_loss[loss=0.05184, audio_tagging_loss=0.05184, over 24101.00 frames. ], batch size: 100, lr: 2.60e-02, grad_scale: 32.0
2023-12-20 17:41:49,202 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 17:42:09,822 INFO [train.py:917] (2/4) Epoch 7, validation: loss=0.05269, audio_tagging_loss=0.05269, over 3737520.00 frames. 
2023-12-20 17:42:09,823 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 17:42:11,269 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.0.conv_module2.balancer1.prob, batch_count=2080.0, ans=0.40249999999999997
2023-12-20 17:42:13,768 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.1.conv_module1.whiten, num_groups=1, num_channels=512, metric=39.36 vs. limit=8.28
2023-12-20 17:42:13,877 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.0.feed_forward3.out_whiten, num_groups=1, num_channels=384, metric=286.41 vs. limit=8.28
2023-12-20 17:42:21,934 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.0.nonlin_attention.balancer.prob, batch_count=2146.6666666666665, ans=0.399375
2023-12-20 17:42:26,575 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.0.layers.1.conv_module1.whiten, num_groups=1, num_channels=192, metric=13.94 vs. limit=8.305
2023-12-20 17:42:30,575 INFO [scaling.py:1022] (2/4) Whitening: name=encoder_embed.out_whiten, num_groups=1, num_channels=192, metric=9.86 vs. limit=4.429333333333333
2023-12-20 17:42:35,984 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.1.encoder.layers.0.self_attn2.whiten, num_groups=1, num_channels=256, metric=25.24 vs. limit=9.16
2023-12-20 17:42:37,806 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.1.balancer2.prob, batch_count=2213.3333333333335, ans=0.39625
2023-12-20 17:43:07,588 INFO [train.py:886] (2/4) Epoch 7, batch 50, loss[loss=0.04344, audio_tagging_loss=0.04344, over 25000.00 frames. ], tot_loss[loss=0.05087, audio_tagging_loss=0.05087, over 1122403.67 frames. ], batch size: 100, lr: 2.76e-02, grad_scale: 1.0
2023-12-20 17:43:08,096 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.1.conv_module2.whiten, num_groups=1, num_channels=512, metric=33.27 vs. limit=8.405
2023-12-20 17:43:32,848 INFO [train.py:886] (2/4) Epoch 8, batch 0, loss[loss=0.05077, audio_tagging_loss=0.05077, over 24170.00 frames. ], tot_loss[loss=0.05077, audio_tagging_loss=0.05077, over 24170.00 frames. ], batch size: 100, lr: 2.60e-02, grad_scale: 2.0
2023-12-20 17:43:32,848 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 17:43:47,796 INFO [zipformer.py:1858] (2/4) name=encoder.encoders.4.encoder.layers.2.self_attn_weights, attn_weights_entropy = tensor([3.8993, 4.3654, 3.4870, 3.3905], device='cuda:2')
2023-12-20 17:43:53,651 INFO [train.py:917] (2/4) Epoch 8, validation: loss=0.05155, audio_tagging_loss=0.05155, over 3737520.00 frames. 
2023-12-20 17:43:53,652 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 17:44:23,899 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.1.self_attn2.whiten, num_groups=1, num_channels=512, metric=40.97 vs. limit=9.42
2023-12-20 17:44:23,966 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.1.whiten, num_groups=1, num_channels=384, metric=6.71 vs. limit=5.024
2023-12-20 17:44:37,004 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.0.bypass.skip_rate, batch_count=2626.6666666666665, ans=0.5
2023-12-20 17:44:42,525 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.5.encoder.layers.1.attention_skip_rate, batch_count=2693.3333333333335, ans=0.09899999999999999
2023-12-20 17:44:43,338 WARNING [optim.py:484] (2/4) Clipping_scale=2.0, grad-norm quartiles 2.635e+01 3.487e+01 4.265e+01 5.657e+01 4.687e+02, threshold=8.530e+01, percent-clipped=24.0
2023-12-20 17:44:43,510 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.1.encoder.layers.1.conv_module2.balancer1.min_positive, batch_count=2693.3333333333335, ans=0.04158333333333333
2023-12-20 17:44:48,472 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.0.conv_module1.whiten, num_groups=1, num_channels=384, metric=26.61 vs. limit=8.51
2023-12-20 17:44:50,241 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.2.bypass.scale_min, batch_count=2760.0, ans=0.8034
2023-12-20 17:44:51,056 INFO [train.py:886] (2/4) Epoch 8, batch 50, loss[loss=0.04868, audio_tagging_loss=0.04868, over 25000.00 frames. ], tot_loss[loss=0.04903, audio_tagging_loss=0.04903, over 1126572.41 frames. ], batch size: 100, lr: 2.75e-02, grad_scale: 2.0
2023-12-20 17:45:09,995 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.0.layers.0.nonlin_attention.whiten2, num_groups=1, num_channels=192, metric=12.07 vs. limit=6.386666666666667
2023-12-20 17:45:16,350 INFO [train.py:886] (2/4) Epoch 9, batch 0, loss[loss=0.0511, audio_tagging_loss=0.0511, over 24103.00 frames. ], tot_loss[loss=0.0511, audio_tagging_loss=0.0511, over 24103.00 frames. ], batch size: 100, lr: 2.61e-02, grad_scale: 4.0
2023-12-20 17:45:16,350 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 17:45:37,427 INFO [train.py:917] (2/4) Epoch 9, validation: loss=0.04977, audio_tagging_loss=0.04977, over 3737520.00 frames. 
2023-12-20 17:45:37,428 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 17:45:37,767 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.0.self_attn2.whiten, num_groups=1, num_channels=256, metric=92.44 vs. limit=9.58
2023-12-20 17:45:38,899 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.0.feed_forward2.out_whiten, num_groups=1, num_channels=256, metric=35.85 vs. limit=8.54
2023-12-20 17:45:52,080 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.1.encoder.layers.0.feed_forward2.out_whiten, num_groups=1, num_channels=256, metric=167.20 vs. limit=8.565
2023-12-20 17:45:55,238 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.2.conv_module2.whiten, num_groups=1, num_channels=512, metric=25.80 vs. limit=8.565
2023-12-20 17:46:04,782 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.0.layers.0.feed_forward1.hidden_balancer.prob, batch_count=2906.6666666666665, ans=0.36375
2023-12-20 17:46:06,137 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.0.feed_forward1.out_whiten, num_groups=1, num_channels=256, metric=135.02 vs. limit=8.59
2023-12-20 17:46:10,579 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.0.feed_forward1.out_whiten, num_groups=1, num_channels=384, metric=93.55 vs. limit=8.615
2023-12-20 17:46:14,905 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.0.conv_module2.whiten, num_groups=1, num_channels=256, metric=18.12 vs. limit=8.615
2023-12-20 17:46:15,943 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.3.conv_module1.balancer2.prob, batch_count=2973.3333333333335, ans=0.360625
2023-12-20 17:46:19,650 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.2.self_attn2.whiten, num_groups=1, num_channels=384, metric=31.90 vs. limit=9.73
2023-12-20 17:46:23,966 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.1.nonlin_attention.whiten1.whitening_limit, batch_count=3040.0, ans=5.76
2023-12-20 17:46:24,027 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.2.feed_forward1.out_whiten, num_groups=1, num_channels=384, metric=121.48 vs. limit=8.64
2023-12-20 17:46:30,447 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.1.nonlin_attention.whiten2, num_groups=1, num_channels=256, metric=25.05 vs. limit=6.52
2023-12-20 17:46:32,350 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.0.bypass.scale_min, batch_count=3106.6666666666665, ans=0.7912666666666667
2023-12-20 17:46:33,270 INFO [train.py:886] (2/4) Epoch 9, batch 50, loss[loss=0.04414, audio_tagging_loss=0.04414, over 25000.00 frames. ], tot_loss[loss=0.04714, audio_tagging_loss=0.04714, over 1123255.65 frames. ], batch size: 100, lr: 2.75e-02, grad_scale: 4.0
2023-12-20 17:46:33,779 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.2.feed_forward3.out_whiten, num_groups=1, num_channels=384, metric=32.84 vs. limit=8.665
2023-12-20 17:46:52,658 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.2.nonlin_attention.whiten1, num_groups=1, num_channels=288, metric=5.87 vs. limit=5.78
2023-12-20 17:46:59,483 INFO [train.py:886] (2/4) Epoch 10, batch 0, loss[loss=0.04603, audio_tagging_loss=0.04603, over 24103.00 frames. ], tot_loss[loss=0.04603, audio_tagging_loss=0.04603, over 24103.00 frames. ], batch size: 100, lr: 2.62e-02, grad_scale: 8.0
2023-12-20 17:46:59,484 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 17:47:20,692 INFO [train.py:917] (2/4) Epoch 10, validation: loss=0.04858, audio_tagging_loss=0.04858, over 3737520.00 frames. 
2023-12-20 17:47:20,693 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 17:47:21,188 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.3.conv_module1.whiten, num_groups=1, num_channels=512, metric=25.96 vs. limit=8.67
2023-12-20 17:47:21,339 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.0.whiten, num_groups=1, num_channels=384, metric=5.49 vs. limit=5.248
2023-12-20 17:47:31,424 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.5.encoder.layers.1.balancer_ff2.min_abs, batch_count=3186.6666666666665, ans=0.07966666666666668
2023-12-20 17:47:37,859 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.1.encoder.layers.1.conv_module2.balancer2.prob, batch_count=3186.6666666666665, ans=0.35062499999999996
2023-12-20 17:47:45,651 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.0.feed_forward2.hidden_balancer.prob, batch_count=3253.3333333333335, ans=0.34750000000000003
2023-12-20 17:47:48,903 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.1.conv_skip_rate, batch_count=3253.3333333333335, ans=0.07799999999999999
2023-12-20 17:47:49,448 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.1.encoder.layers.0.conv_module1.whiten, num_groups=1, num_channels=256, metric=31.91 vs. limit=8.72
2023-12-20 17:47:58,344 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.0.layers.0.self_attn2.whiten, num_groups=1, num_channels=192, metric=17.51 vs. limit=9.99
2023-12-20 17:48:01,088 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.2.conv_skip_rate, batch_count=3320.0, ans=0.0755
2023-12-20 17:48:03,741 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.2.nonlin_attention.whiten2, num_groups=1, num_channels=384, metric=9.44 vs. limit=6.66
2023-12-20 17:48:04,134 WARNING [optim.py:484] (2/4) Clipping_scale=2.0, grad-norm quartiles 2.619e+01 3.726e+01 4.484e+01 5.424e+01 1.858e+02, threshold=8.969e+01, percent-clipped=3.0
2023-12-20 17:48:15,997 INFO [train.py:886] (2/4) Epoch 10, batch 50, loss[loss=0.04358, audio_tagging_loss=0.04358, over 25000.00 frames. ], tot_loss[loss=0.0462, audio_tagging_loss=0.0462, over 1119906.38 frames. ], batch size: 100, lr: 2.71e-02, grad_scale: 8.0
2023-12-20 17:48:40,825 INFO [train.py:886] (2/4) Epoch 11, batch 0, loss[loss=0.04723, audio_tagging_loss=0.04723, over 24078.00 frames. ], tot_loss[loss=0.04723, audio_tagging_loss=0.04723, over 24078.00 frames. ], batch size: 100, lr: 2.58e-02, grad_scale: 16.0
2023-12-20 17:48:40,826 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 17:48:53,636 INFO [zipformer.py:1858] (2/4) name=encoder.encoders.0.layers.1.self_attn_weights, attn_weights_entropy = tensor([2.3948, 2.3390, 3.4762, 2.4205], device='cuda:2')
2023-12-20 17:49:01,994 INFO [train.py:917] (2/4) Epoch 11, validation: loss=0.04728, audio_tagging_loss=0.04728, over 3737520.00 frames. 
2023-12-20 17:49:01,995 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 17:49:05,994 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.1.nonlin_attention.balancer.max_positive, batch_count=3466.6666666666665, ans=0.7846666666666666
2023-12-20 17:49:10,845 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.1.self_attn2.whiten, num_groups=1, num_channels=384, metric=21.75 vs. limit=10.1
2023-12-20 17:49:13,799 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.2.whiten, num_groups=1, num_channels=384, metric=7.27 vs. limit=5.413333333333333
2023-12-20 17:49:22,617 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.0.layers.1.conv_module2.balancer2.prob, batch_count=3533.3333333333335, ans=0.334375
2023-12-20 17:49:24,148 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.0.nonlin_attention.whiten2, num_groups=1, num_channels=512, metric=12.46 vs. limit=6.766666666666667
2023-12-20 17:49:28,421 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.0.nonlin_attention.whiten2, num_groups=1, num_channels=512, metric=11.80 vs. limit=6.8
2023-12-20 17:49:31,381 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.1.encoder.layers.0.conv_module2.balancer1.prob, batch_count=3600.0, ans=0.33125
2023-12-20 17:49:32,726 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.1.conv_module1.whiten, num_groups=1, num_channels=384, metric=14.87 vs. limit=8.85
2023-12-20 17:49:33,807 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.1.feed_forward2.out_whiten, num_groups=1, num_channels=384, metric=43.37 vs. limit=8.85
2023-12-20 17:49:34,197 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.0.layers.0.self_attn2.whiten, num_groups=1, num_channels=192, metric=15.32 vs. limit=10.2
2023-12-20 17:49:34,814 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.1.self_attn1.whiten, num_groups=1, num_channels=256, metric=33.69 vs. limit=10.2
2023-12-20 17:49:40,594 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.1.encoder.layers.1.self_attn1.whiten, num_groups=1, num_channels=256, metric=20.99 vs. limit=10.25
2023-12-20 17:49:42,417 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.0.conv_skip_rate, batch_count=3666.6666666666665, ans=0.06249999999999997
2023-12-20 17:49:43,617 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.2.feed_forward3.out_whiten, num_groups=1, num_channels=384, metric=62.52 vs. limit=8.875
2023-12-20 17:49:46,822 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.1.whiten, num_groups=1, num_channels=256, metric=9.27 vs. limit=5.493333333333333
2023-12-20 17:49:48,262 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.0.feed_forward2.out_whiten, num_groups=1, num_channels=384, metric=31.55 vs. limit=8.9
2023-12-20 17:49:49,453 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.0.layers.0.feed_forward3.out_whiten, num_groups=1, num_channels=192, metric=11.65 vs. limit=8.9
2023-12-20 17:49:53,288 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.2.feed_forward3.out_whiten, num_groups=1, num_channels=384, metric=62.67 vs. limit=8.9
2023-12-20 17:49:56,289 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.1.conv_skip_rate, batch_count=3733.3333333333335, ans=0.05999999999999997
2023-12-20 17:49:57,433 INFO [scaling.py:1118] (2/4) WithLoss: name=encoder.encoders.3.encoder.layers.3.self_attn_weights, loss-sum=4.452e+00
2023-12-20 17:49:57,469 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.2.ff2_skip_rate, batch_count=3800.0, ans=0.014499999999999985
2023-12-20 17:49:57,531 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.1.feed_forward2.out_whiten, num_groups=1, num_channels=256, metric=42.27 vs. limit=8.925
2023-12-20 17:49:58,312 INFO [train.py:886] (2/4) Epoch 11, batch 50, loss[loss=0.04255, audio_tagging_loss=0.04255, over 25000.00 frames. ], tot_loss[loss=0.04557, audio_tagging_loss=0.04557, over 1117498.70 frames. ], batch size: 100, lr: 2.58e-02, grad_scale: 16.0
2023-12-20 17:49:58,780 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.2.conv_module2.whiten, num_groups=1, num_channels=512, metric=12.69 vs. limit=8.925
2023-12-20 17:50:23,186 INFO [train.py:886] (2/4) Epoch 12, batch 0, loss[loss=0.0448, audio_tagging_loss=0.0448, over 24138.00 frames. ], tot_loss[loss=0.0448, audio_tagging_loss=0.0448, over 24138.00 frames. ], batch size: 100, lr: 2.47e-02, grad_scale: 32.0
2023-12-20 17:50:23,187 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 17:50:36,041 INFO [zipformer.py:1858] (2/4) name=encoder.encoders.2.encoder.layers.0.self_attn_weights, attn_weights_entropy = tensor([1.9587, 1.7990, 1.8133, 1.8953], device='cuda:2')
2023-12-20 17:50:44,477 INFO [train.py:917] (2/4) Epoch 12, validation: loss=0.04619, audio_tagging_loss=0.04619, over 3737520.00 frames. 
2023-12-20 17:50:44,478 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 17:50:51,736 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.2.nonlin_attention.whiten1, num_groups=1, num_channels=384, metric=6.73 vs. limit=5.953333333333333
2023-12-20 17:50:51,760 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.1.conv_module1.whiten, num_groups=1, num_channels=512, metric=12.91 vs. limit=8.93
2023-12-20 17:50:57,718 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.1.feed_forward1.out_whiten, num_groups=1, num_channels=256, metric=71.88 vs. limit=8.955
2023-12-20 17:50:57,757 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.0.nonlin_attention.whiten2, num_groups=1, num_channels=256, metric=10.99 vs. limit=6.9399999999999995
2023-12-20 17:51:00,137 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.0.conv_module1.whiten, num_groups=1, num_channels=384, metric=11.65 vs. limit=8.955
2023-12-20 17:51:06,694 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.2.conv_module2.whiten, num_groups=1, num_channels=384, metric=20.92 vs. limit=8.955
2023-12-20 17:51:07,607 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.2.conv_module1.balancer2.min_abs, batch_count=3946.6666666666665, ans=0.2592
2023-12-20 17:51:10,838 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.1.encoder.layers.0.attention_skip_rate, batch_count=3946.6666666666665, ans=0.05199999999999999
2023-12-20 17:51:11,498 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.0.layers.1.self_attn1.whiten, num_groups=1, num_channels=192, metric=11.21 vs. limit=10.46
2023-12-20 17:51:24,217 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.1.encoder.layers.1.balancer1.prob, batch_count=4013.3333333333335, ans=0.311875
2023-12-20 17:51:25,063 WARNING [optim.py:484] (2/4) Clipping_scale=2.0, grad-norm quartiles 2.695e+01 3.849e+01 4.841e+01 5.572e+01 8.770e+01, threshold=9.682e+01, percent-clipped=0.0
2023-12-20 17:51:26,808 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.0.feed_forward2.out_whiten, num_groups=1, num_channels=384, metric=27.57 vs. limit=9.004999999999999
2023-12-20 17:51:27,436 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.3.conv_module2.balancer2.prob, batch_count=4013.3333333333335, ans=0.311875
2023-12-20 17:51:33,074 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.2.nonlin_attention.whiten2, num_groups=1, num_channels=512, metric=7.21 vs. limit=7.04
2023-12-20 17:51:35,052 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.0.feed_forward2.out_whiten.whitening_limit, batch_count=4080.0, ans=9.03
2023-12-20 17:51:40,154 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.2.attention_skip_rate, batch_count=4146.666666666667, ans=0.04938888888888889
2023-12-20 17:51:40,951 INFO [train.py:886] (2/4) Epoch 12, batch 50, loss[loss=0.04178, audio_tagging_loss=0.04178, over 25000.00 frames. ], tot_loss[loss=0.04376, audio_tagging_loss=0.04376, over 1120004.19 frames. ], batch size: 100, lr: 2.47e-02, grad_scale: 32.0
2023-12-20 17:52:04,705 INFO [train.py:886] (2/4) Epoch 13, batch 0, loss[loss=0.03956, audio_tagging_loss=0.03956, over 25000.00 frames. ], tot_loss[loss=0.03956, audio_tagging_loss=0.03956, over 25000.00 frames. ], batch size: 100, lr: 2.38e-02, grad_scale: 32.0
2023-12-20 17:52:04,705 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 17:52:12,866 INFO [zipformer.py:1858] (2/4) name=encoder.encoders.4.encoder.layers.2.self_attn_weights, attn_weights_entropy = tensor([1.9063, 1.1653, 1.8288, 1.8131], device='cuda:2')
2023-12-20 17:52:25,607 INFO [train.py:917] (2/4) Epoch 13, validation: loss=0.04525, audio_tagging_loss=0.04525, over 3737520.00 frames. 
2023-12-20 17:52:25,608 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 17:52:25,877 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.1.feed_forward1.out_proj.dropout_p, batch_count=4160.0, ans=0.2584
2023-12-20 17:52:25,887 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.2.conv_module1.balancer2.min_positive, batch_count=4160.0, ans=0.07400000000000001
2023-12-20 17:52:28,476 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.1.encoder.layers.0.conv_module1.whiten, num_groups=1, num_channels=256, metric=17.39 vs. limit=9.06
2023-12-20 17:52:29,023 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.1.self_attn_weights.pos_emb_skip_rate, batch_count=4160.0, ans=0.0
2023-12-20 17:52:29,163 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.1.self_attn1.whiten, num_groups=1, num_channels=256, metric=24.32 vs. limit=10.620000000000001
2023-12-20 17:52:36,978 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.0.ff3_skip_rate, batch_count=4226.666666666667, ans=0.009950724637681159
2023-12-20 17:52:39,203 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.1.self_attn1.whiten, num_groups=1, num_channels=256, metric=29.03 vs. limit=10.67
2023-12-20 17:52:41,561 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.0.self_attn2.whiten, num_groups=1, num_channels=512, metric=18.78 vs. limit=10.67
2023-12-20 17:52:42,810 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.1.encoder.layers.0.feed_forward3.out_whiten, num_groups=1, num_channels=256, metric=33.46 vs. limit=9.085
2023-12-20 17:52:42,961 INFO [scaling.py:1022] (2/4) Whitening: name=encoder_embed.convnext.out_whiten, num_groups=1, num_channels=128, metric=5.62 vs. limit=5.0
2023-12-20 17:52:49,800 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.2.bypass.skip_rate, batch_count=4293.333333333333, ans=0.07
2023-12-20 17:52:50,872 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.1.encoder.layers.1.bypass.skip_rate, batch_count=4293.333333333333, ans=0.04949747468305833
2023-12-20 17:52:59,815 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.1.feed_forward2.out_whiten, num_groups=1, num_channels=384, metric=11.58 vs. limit=9.135
2023-12-20 17:53:04,180 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.0.feed_forward1.out_whiten, num_groups=1, num_channels=384, metric=15.15 vs. limit=9.135
2023-12-20 17:53:11,233 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.1.feed_forward3.out_whiten, num_groups=1, num_channels=384, metric=51.27 vs. limit=9.16
2023-12-20 17:53:15,285 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.2.ff2_skip_rate, batch_count=4426.666666666667, ans=0.009907246376811594
2023-12-20 17:53:19,091 INFO [train.py:886] (2/4) Epoch 13, batch 50, loss[loss=0.04299, audio_tagging_loss=0.04299, over 25000.00 frames. ], tot_loss[loss=0.04317, audio_tagging_loss=0.04317, over 1121045.90 frames. ], batch size: 100, lr: 2.38e-02, grad_scale: 32.0
2023-12-20 17:53:43,854 INFO [train.py:886] (2/4) Epoch 14, batch 0, loss[loss=0.04288, audio_tagging_loss=0.04288, over 25000.00 frames. ], tot_loss[loss=0.04288, audio_tagging_loss=0.04288, over 25000.00 frames. ], batch size: 100, lr: 2.29e-02, grad_scale: 32.0
2023-12-20 17:53:43,854 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 17:53:55,626 INFO [zipformer.py:1858] (2/4) name=encoder.encoders.1.encoder.layers.0.self_attn_weights, attn_weights_entropy = tensor([1.9270, 1.7806, 2.1339, 1.9149], device='cuda:2')
2023-12-20 17:54:05,165 INFO [train.py:917] (2/4) Epoch 14, validation: loss=0.04503, audio_tagging_loss=0.04503, over 3737520.00 frames. 
2023-12-20 17:54:05,166 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 17:54:11,065 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.0.layers.1.self_attn2.whiten, num_groups=1, num_channels=192, metric=11.61 vs. limit=10.879999999999999
2023-12-20 17:54:26,446 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.2.feed_forward1.out_whiten, num_groups=1, num_channels=512, metric=13.15 vs. limit=9.24
2023-12-20 17:54:29,279 INFO [scaling.py:1118] (2/4) WithLoss: name=encoder.encoders.3.encoder.layers.2.self_attn_weights, loss-sum=2.038e+01
2023-12-20 17:54:31,267 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.1.balancer1.prob, batch_count=4640.0, ans=0.2825
2023-12-20 17:54:31,642 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.2.feed_forward3.out_whiten, num_groups=1, num_channels=384, metric=9.40 vs. limit=9.24
2023-12-20 17:54:32,612 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.0.nonlin_attention.whiten2, num_groups=1, num_channels=384, metric=7.14 vs. limit=7.32
2023-12-20 17:54:35,687 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.0.self_attn2.whiten, num_groups=1, num_channels=256, metric=24.44 vs. limit=10.98
2023-12-20 17:54:37,652 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.5.encoder.layers.0.conv_module2.balancer1.prob, batch_count=4706.666666666667, ans=0.27937500000000004
2023-12-20 17:54:38,362 WARNING [optim.py:484] (2/4) Clipping_scale=2.0, grad-norm quartiles 2.429e+01 4.195e+01 5.214e+01 6.348e+01 1.962e+02, threshold=1.043e+02, percent-clipped=5.0
2023-12-20 17:54:40,051 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.1.feed_forward2.out_whiten, num_groups=1, num_channels=384, metric=9.22 vs. limit=9.265
2023-12-20 17:54:57,249 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.0.conv_module1.balancer2.prob, batch_count=4840.0, ans=0.273125
2023-12-20 17:54:58,020 INFO [train.py:886] (2/4) Epoch 14, batch 50, loss[loss=0.03772, audio_tagging_loss=0.03772, over 25000.00 frames. ], tot_loss[loss=0.04195, audio_tagging_loss=0.04195, over 1123574.72 frames. ], batch size: 100, lr: 2.29e-02, grad_scale: 32.0
2023-12-20 17:55:22,498 INFO [train.py:886] (2/4) Epoch 15, batch 0, loss[loss=0.04001, audio_tagging_loss=0.04001, over 25000.00 frames. ], tot_loss[loss=0.04001, audio_tagging_loss=0.04001, over 25000.00 frames. ], batch size: 100, lr: 2.21e-02, grad_scale: 32.0
2023-12-20 17:55:22,498 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 17:55:43,372 INFO [train.py:917] (2/4) Epoch 15, validation: loss=0.04452, audio_tagging_loss=0.04452, over 3737520.00 frames. 
2023-12-20 17:55:43,373 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 17:55:44,603 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.3.nonlin_attention.balancer.min_positive, batch_count=4853.333333333333, ans=0.20146666666666668
2023-12-20 17:55:44,875 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.2.feed_forward3.out_whiten.whitening_limit, batch_count=4853.333333333333, ans=9.32
2023-12-20 17:55:47,837 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.1.feed_forward2.out_whiten, num_groups=1, num_channels=384, metric=36.73 vs. limit=9.32
2023-12-20 17:55:49,722 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.0.conv_module1.balancer1.max_abs, batch_count=4853.333333333333, ans=8.033333333333333
2023-12-20 17:55:49,892 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.0.feed_forward2.out_whiten, num_groups=1, num_channels=256, metric=27.66 vs. limit=9.32
2023-12-20 17:55:58,425 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.0.self_attn2.whiten, num_groups=1, num_channels=384, metric=12.65 vs. limit=11.19
2023-12-20 17:56:01,385 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.1.self_attn2.whiten, num_groups=1, num_channels=384, metric=20.60 vs. limit=11.19
2023-12-20 17:56:16,480 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.0.feed_forward2.out_whiten, num_groups=1, num_channels=512, metric=27.61 vs. limit=9.395
2023-12-20 17:56:18,276 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.1.conv_module1.whiten, num_groups=1, num_channels=256, metric=10.38 vs. limit=9.395
2023-12-20 17:56:23,249 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.0.layers.1.conv_skip_rate, batch_count=5053.333333333333, ans=0.045611111111111116
2023-12-20 17:56:35,386 INFO [train.py:886] (2/4) Epoch 15, batch 50, loss[loss=0.04041, audio_tagging_loss=0.04041, over 25000.00 frames. ], tot_loss[loss=0.04165, audio_tagging_loss=0.04165, over 1115568.63 frames. ], batch size: 100, lr: 2.21e-02, grad_scale: 32.0
2023-12-20 17:57:00,247 INFO [train.py:886] (2/4) Epoch 16, batch 0, loss[loss=0.0395, audio_tagging_loss=0.0395, over 25000.00 frames. ], tot_loss[loss=0.0395, audio_tagging_loss=0.0395, over 25000.00 frames. ], batch size: 100, lr: 2.14e-02, grad_scale: 32.0
2023-12-20 17:57:00,248 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 17:57:12,226 INFO [zipformer.py:1858] (2/4) name=encoder.encoders.5.encoder.layers.1.self_attn_weights, attn_weights_entropy = tensor([2.6515, 2.9810, 2.7109, 3.1295], device='cuda:2')
2023-12-20 17:57:21,257 INFO [train.py:917] (2/4) Epoch 16, validation: loss=0.04383, audio_tagging_loss=0.04383, over 3737520.00 frames. 
2023-12-20 17:57:21,257 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 17:57:22,530 INFO [scaling.py:1118] (2/4) WithLoss: name=encoder.encoders.5.encoder.layers.0.self_attn_weights, loss-sum=1.040e+02
2023-12-20 17:57:26,145 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.1.conv_module2.balancer2.prob, batch_count=5200.0, ans=0.25625
2023-12-20 17:57:27,290 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.1.feed_forward3.out_whiten, num_groups=1, num_channels=384, metric=30.31 vs. limit=9.45
2023-12-20 17:57:27,437 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.2.whiten, num_groups=1, num_channels=384, metric=4.57 vs. limit=6.08
2023-12-20 17:57:31,593 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.3.feed_forward2.out_whiten, num_groups=1, num_channels=512, metric=13.12 vs. limit=9.475
2023-12-20 17:57:38,836 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.1.feed_forward3.out_whiten, num_groups=1, num_channels=384, metric=10.70 vs. limit=9.475
2023-12-20 17:57:44,198 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.1.feed_forward3.out_whiten, num_groups=1, num_channels=384, metric=10.46 vs. limit=9.5
2023-12-20 17:57:49,876 WARNING [optim.py:484] (2/4) Clipping_scale=2.0, grad-norm quartiles 2.797e+01 3.933e+01 4.813e+01 5.766e+01 2.623e+02, threshold=9.626e+01, percent-clipped=4.0
2023-12-20 17:57:56,677 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.3.bypass.scale_min, batch_count=5400.0, ans=0.7110000000000001
2023-12-20 17:58:12,090 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.2.balancer1.prob, batch_count=5466.666666666667, ans=0.24375000000000002
2023-12-20 17:58:13,998 INFO [train.py:886] (2/4) Epoch 16, batch 50, loss[loss=0.04058, audio_tagging_loss=0.04058, over 25000.00 frames. ], tot_loss[loss=0.04029, audio_tagging_loss=0.04029, over 1124080.10 frames. ], batch size: 100, lr: 2.14e-02, grad_scale: 32.0
2023-12-20 17:58:14,088 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.0.layers.0.feed_forward2.hidden_balancer.prob, batch_count=5533.333333333333, ans=0.24062499999999998
2023-12-20 17:58:38,072 INFO [train.py:886] (2/4) Epoch 17, batch 0, loss[loss=0.04294, audio_tagging_loss=0.04294, over 24156.00 frames. ], tot_loss[loss=0.04294, audio_tagging_loss=0.04294, over 24156.00 frames. ], batch size: 100, lr: 2.07e-02, grad_scale: 32.0
2023-12-20 17:58:38,072 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 17:58:46,300 INFO [zipformer.py:1858] (2/4) name=encoder.encoders.5.encoder.layers.1.self_attn_weights, attn_weights_entropy = tensor([2.6665, 3.0104, 2.7217, 2.8777], device='cuda:2')
2023-12-20 17:58:59,163 INFO [train.py:917] (2/4) Epoch 17, validation: loss=0.04362, audio_tagging_loss=0.04362, over 3737520.00 frames. 
2023-12-20 17:58:59,164 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 17:59:00,765 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.0.feed_forward2.out_whiten, num_groups=1, num_channels=512, metric=22.74 vs. limit=9.58
2023-12-20 17:59:03,698 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.1.conv_module1.whiten, num_groups=1, num_channels=256, metric=9.83 vs. limit=9.58
2023-12-20 17:59:12,953 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.3.conv_module1.whiten, num_groups=1, num_channels=512, metric=9.71 vs. limit=9.605
2023-12-20 17:59:13,898 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.1.feed_forward1.out_whiten, num_groups=1, num_channels=384, metric=26.48 vs. limit=9.605
2023-12-20 17:59:16,705 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.2.bypass.skip_rate, batch_count=5613.333333333333, ans=0.07
2023-12-20 17:59:17,770 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.5.encoder.layers.1.bypass_mid.scale_min, batch_count=5613.333333333333, ans=0.7035333333333333
2023-12-20 17:59:18,813 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.1.whiten, num_groups=1, num_channels=256, metric=6.46 vs. limit=6.272
2023-12-20 17:59:32,321 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.1.encoder.layers.0.conv_skip_rate, batch_count=5746.666666666667, ans=0.042722222222222224
2023-12-20 17:59:37,551 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.2.self_attn2.whiten, num_groups=1, num_channels=384, metric=26.72 vs. limit=11.809999999999999
2023-12-20 17:59:38,760 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.1.self_attn1.whiten, num_groups=1, num_channels=384, metric=12.71 vs. limit=11.809999999999999
2023-12-20 17:59:43,436 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.0.feed_forward2.out_whiten, num_groups=1, num_channels=256, metric=18.49 vs. limit=9.68
2023-12-20 17:59:47,521 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.3.whiten, num_groups=1, num_channels=512, metric=6.48 vs. limit=6.325333333333333
2023-12-20 17:59:49,926 INFO [train.py:886] (2/4) Epoch 17, batch 50, loss[loss=0.03614, audio_tagging_loss=0.03614, over 25000.00 frames. ], tot_loss[loss=0.03988, audio_tagging_loss=0.03988, over 1120588.04 frames. ], batch size: 100, lr: 2.07e-02, grad_scale: 32.0
2023-12-20 18:00:14,311 INFO [train.py:886] (2/4) Epoch 18, batch 0, loss[loss=0.03981, audio_tagging_loss=0.03981, over 24110.00 frames. ], tot_loss[loss=0.03981, audio_tagging_loss=0.03981, over 24110.00 frames. ], batch size: 100, lr: 2.01e-02, grad_scale: 32.0
2023-12-20 18:00:14,312 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 18:00:35,060 INFO [train.py:917] (2/4) Epoch 18, validation: loss=0.04342, audio_tagging_loss=0.04342, over 3737520.00 frames. 
2023-12-20 18:00:35,060 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 18:00:45,816 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.1.encoder.layers.0.feed_forward1.out_proj.dropout_p, batch_count=5960.0, ans=0.2404
2023-12-20 18:00:47,260 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.2.feed_forward3.out_whiten, num_groups=1, num_channels=384, metric=12.34 vs. limit=9.735
2023-12-20 18:00:58,725 WARNING [optim.py:484] (2/4) Clipping_scale=2.0, grad-norm quartiles 3.069e+01 3.667e+01 4.319e+01 5.687e+01 1.553e+02, threshold=8.639e+01, percent-clipped=3.0
2023-12-20 18:00:59,201 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.1.self_attn2.whiten, num_groups=1, num_channels=384, metric=17.41 vs. limit=12.02
2023-12-20 18:01:01,041 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.3.balancer2.prob, batch_count=6026.666666666667, ans=0.21750000000000003
2023-12-20 18:01:14,386 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.0.self_attn2.whiten, num_groups=1, num_channels=256, metric=19.23 vs. limit=12.07
2023-12-20 18:01:18,073 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.0.layers.0.balancer1.prob, batch_count=6160.0, ans=0.21125
2023-12-20 18:01:25,357 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.0.feed_forward1.out_whiten, num_groups=1, num_channels=384, metric=10.56 vs. limit=9.835
2023-12-20 18:01:25,750 INFO [train.py:886] (2/4) Epoch 18, batch 50, loss[loss=0.03439, audio_tagging_loss=0.03439, over 25000.00 frames. ], tot_loss[loss=0.03833, audio_tagging_loss=0.03833, over 1127202.33 frames. ], batch size: 100, lr: 2.01e-02, grad_scale: 32.0
2023-12-20 18:01:50,821 INFO [train.py:886] (2/4) Epoch 19, batch 0, loss[loss=0.03398, audio_tagging_loss=0.03398, over 25000.00 frames. ], tot_loss[loss=0.03398, audio_tagging_loss=0.03398, over 25000.00 frames. ], batch size: 100, lr: 1.96e-02, grad_scale: 32.0
2023-12-20 18:01:50,821 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 18:02:11,829 INFO [train.py:917] (2/4) Epoch 19, validation: loss=0.04287, audio_tagging_loss=0.04287, over 3737520.00 frames. 
2023-12-20 18:02:11,830 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 18:02:12,102 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.2.conv_module1.balancer2.prob, batch_count=6240.0, ans=0.20750000000000002
2023-12-20 18:02:22,842 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.3.self_attn1.whiten, num_groups=1, num_channels=512, metric=13.62 vs. limit=12.23
2023-12-20 18:02:28,552 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.0.conv_module1.balancer2.prob, batch_count=6306.666666666667, ans=0.20437499999999997
2023-12-20 18:02:43,598 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.0.feed_forward1.out_whiten, num_groups=1, num_channels=384, metric=11.72 vs. limit=9.915
2023-12-20 18:02:45,741 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.0.feed_forward2.hidden_balancer.prob, batch_count=6440.0, ans=0.198125
2023-12-20 18:02:45,782 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.out_combiner.scale_min, batch_count=6440.0, ans=0.6746000000000001
2023-12-20 18:02:48,718 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.1.conv_module2.balancer1.min_positive, batch_count=6440.0, ans=0.029875000000000002
2023-12-20 18:02:52,823 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.0.conv_module2.whiten, num_groups=1, num_channels=256, metric=9.61 vs. limit=9.94
2023-12-20 18:02:55,708 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.0.feed_forward2.out_whiten, num_groups=1, num_channels=256, metric=15.61 vs. limit=9.94
2023-12-20 18:03:00,160 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder_embed.conv.2.prob, batch_count=6506.666666666667, ans=0.195
2023-12-20 18:03:00,494 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.2.conv_module1.whiten, num_groups=1, num_channels=384, metric=10.95 vs. limit=9.94
2023-12-20 18:03:02,036 INFO [train.py:886] (2/4) Epoch 19, batch 50, loss[loss=0.03557, audio_tagging_loss=0.03557, over 25000.00 frames. ], tot_loss[loss=0.0378, audio_tagging_loss=0.0378, over 1123929.37 frames. ], batch size: 100, lr: 1.96e-02, grad_scale: 32.0
2023-12-20 18:03:26,294 INFO [train.py:886] (2/4) Epoch 20, batch 0, loss[loss=0.03504, audio_tagging_loss=0.03504, over 25000.00 frames. ], tot_loss[loss=0.03504, audio_tagging_loss=0.03504, over 25000.00 frames. ], batch size: 100, lr: 1.91e-02, grad_scale: 32.0
2023-12-20 18:03:26,295 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 18:03:47,095 INFO [train.py:917] (2/4) Epoch 20, validation: loss=0.0429, audio_tagging_loss=0.0429, over 3737520.00 frames. 
2023-12-20 18:03:47,095 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 18:03:48,714 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.1.encoder.layers.1.feed_forward2.out_whiten, num_groups=1, num_channels=256, metric=10.90 vs. limit=9.97
2023-12-20 18:03:58,683 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.0.layers.0.conv_module2.balancer2.prob, batch_count=6653.333333333333, ans=0.188125
2023-12-20 18:04:06,504 WARNING [optim.py:484] (2/4) Clipping_scale=2.0, grad-norm quartiles 2.851e+01 3.799e+01 4.551e+01 5.624e+01 1.513e+02, threshold=9.102e+01, percent-clipped=5.0
2023-12-20 18:04:15,815 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.2.self_attn1.whiten, num_groups=1, num_channels=384, metric=22.55 vs. limit=12.54
2023-12-20 18:04:28,964 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.3.nonlin_attention.whiten1, num_groups=1, num_channels=384, metric=6.81 vs. limit=6.713333333333333
2023-12-20 18:04:37,012 INFO [train.py:886] (2/4) Epoch 20, batch 50, loss[loss=0.03413, audio_tagging_loss=0.03413, over 25000.00 frames. ], tot_loss[loss=0.03747, audio_tagging_loss=0.03747, over 1118978.42 frames. ], batch size: 100, lr: 1.91e-02, grad_scale: 32.0
2023-12-20 18:04:59,859 INFO [train.py:886] (2/4) Epoch 21, batch 0, loss[loss=0.04612, audio_tagging_loss=0.04612, over 20094.00 frames. ], tot_loss[loss=0.04612, audio_tagging_loss=0.04612, over 20094.00 frames. ], batch size: 106, lr: 1.86e-02, grad_scale: 32.0
2023-12-20 18:04:59,860 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 18:05:20,818 INFO [train.py:917] (2/4) Epoch 21, validation: loss=0.0427, audio_tagging_loss=0.0427, over 3737520.00 frames. 
2023-12-20 18:05:20,819 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 18:05:32,814 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.1.encoder.layers.0.bypass.scale_min, batch_count=7000.0, ans=0.655
2023-12-20 18:05:48,317 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.2.feed_forward2.out_whiten, num_groups=1, num_channels=384, metric=22.49 vs. limit=10.15
2023-12-20 18:05:48,428 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.1.self_attn2.whiten, num_groups=1, num_channels=512, metric=12.66 vs. limit=12.8
2023-12-20 18:05:50,194 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.5.encoder.layers.1.nonlin_attention.balancer.min_positive, batch_count=7066.666666666667, ans=0.17933333333333334
2023-12-20 18:05:54,798 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.0.conv_module2.balancer2.min_abs, batch_count=7133.333333333333, ans=0.307
2023-12-20 18:05:54,891 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.1.self_attn_weights.pos_emb_skip_rate, batch_count=7133.333333333333, ans=0.0
2023-12-20 18:06:03,488 INFO [scaling.py:1118] (2/4) WithLoss: name=encoder.encoders.2.encoder.layers.2.self_attn_weights, loss-sum=3.507e+01
2023-12-20 18:06:10,789 INFO [train.py:886] (2/4) Epoch 21, batch 50, loss[loss=0.03124, audio_tagging_loss=0.03124, over 25000.00 frames. ], tot_loss[loss=0.03702, audio_tagging_loss=0.03702, over 1110930.85 frames. ], batch size: 100, lr: 1.86e-02, grad_scale: 32.0
2023-12-20 18:06:29,315 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.1.self_attn1.whiten, num_groups=1, num_channels=384, metric=13.78 vs. limit=12.96
2023-12-20 18:06:34,956 INFO [train.py:886] (2/4) Epoch 22, batch 0, loss[loss=0.03299, audio_tagging_loss=0.03299, over 25000.00 frames. ], tot_loss[loss=0.03299, audio_tagging_loss=0.03299, over 25000.00 frames. ], batch size: 100, lr: 1.82e-02, grad_scale: 32.0
2023-12-20 18:06:34,957 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 18:06:49,008 INFO [zipformer.py:1858] (2/4) name=encoder.encoders.2.encoder.layers.0.self_attn_weights, attn_weights_entropy = tensor([1.5037, 1.4958, 1.3105, 1.2949], device='cuda:2')
2023-12-20 18:06:55,944 INFO [train.py:917] (2/4) Epoch 22, validation: loss=0.04259, audio_tagging_loss=0.04259, over 3737520.00 frames. 
2023-12-20 18:06:55,945 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 18:06:59,859 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.2.feed_forward2.out_whiten, num_groups=1, num_channels=512, metric=10.48 vs. limit=10.23
2023-12-20 18:07:04,513 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.5.encoder.layers.1.feed_forward1.out_whiten.whitening_limit, batch_count=7280.0, ans=10.23
2023-12-20 18:07:06,514 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.1.feed_forward3.out_whiten, num_groups=1, num_channels=384, metric=12.59 vs. limit=10.254999999999999
2023-12-20 18:07:10,812 WARNING [optim.py:484] (2/4) Clipping_scale=2.0, grad-norm quartiles 2.833e+01 3.757e+01 4.513e+01 5.428e+01 2.125e+02, threshold=9.026e+01, percent-clipped=5.0
2023-12-20 18:07:12,287 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.0.feed_forward1.out_whiten, num_groups=1, num_channels=512, metric=10.49 vs. limit=10.254999999999999
2023-12-20 18:07:16,699 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.1.encoder.layers.0.feed_forward2.hidden_balancer.prob, batch_count=7413.333333333333, ans=0.15250000000000002
2023-12-20 18:07:21,835 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.3.feed_forward3.out_whiten, num_groups=1, num_channels=512, metric=12.56 vs. limit=10.28
2023-12-20 18:07:27,749 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.3.ff2_skip_rate, batch_count=7480.0, ans=0.009243478260869565
2023-12-20 18:07:28,589 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.1.encoder.layers.1.feed_forward1.hidden_balancer.prob, batch_count=7480.0, ans=0.14937499999999998
2023-12-20 18:07:39,941 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.0.layers.0.conv_skip_rate, batch_count=7546.666666666667, ans=0.035222222222222224
2023-12-20 18:07:44,531 INFO [train.py:886] (2/4) Epoch 22, batch 50, loss[loss=0.03313, audio_tagging_loss=0.03313, over 25000.00 frames. ], tot_loss[loss=0.03545, audio_tagging_loss=0.03545, over 1119124.88 frames. ], batch size: 100, lr: 1.81e-02, grad_scale: 32.0
2023-12-20 18:08:02,962 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.2.self_attn2.whiten, num_groups=1, num_channels=384, metric=21.18 vs. limit=13.219999999999999
2023-12-20 18:08:08,666 INFO [train.py:886] (2/4) Epoch 23, batch 0, loss[loss=0.04406, audio_tagging_loss=0.04406, over 21057.00 frames. ], tot_loss[loss=0.04406, audio_tagging_loss=0.04406, over 21057.00 frames. ], batch size: 106, lr: 1.77e-02, grad_scale: 32.0
2023-12-20 18:08:08,667 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 18:08:30,057 INFO [train.py:917] (2/4) Epoch 23, validation: loss=0.04291, audio_tagging_loss=0.04291, over 3737520.00 frames. 
2023-12-20 18:08:30,058 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 18:08:30,520 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.2.feed_forward2.out_whiten, num_groups=1, num_channels=512, metric=10.48 vs. limit=10.36
2023-12-20 18:08:31,247 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.3.conv_module1.balancer1.prob, batch_count=7626.666666666667, ans=0.14250000000000002
2023-12-20 18:08:31,493 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.2.feed_forward2.out_whiten, num_groups=1, num_channels=512, metric=12.59 vs. limit=10.36
2023-12-20 18:08:34,307 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.3.feed_forward3.out_whiten, num_groups=1, num_channels=512, metric=11.42 vs. limit=10.36
2023-12-20 18:08:45,488 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.2.conv_module1.balancer1.prob, batch_count=7693.333333333333, ans=0.13937500000000003
2023-12-20 18:08:46,739 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.1.feed_forward3.out_whiten, num_groups=1, num_channels=512, metric=11.46 vs. limit=10.385
2023-12-20 18:08:56,472 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.1.encoder.layers.1.conv_module2.whiten, num_groups=1, num_channels=256, metric=8.30 vs. limit=10.41
2023-12-20 18:09:17,955 INFO [train.py:886] (2/4) Epoch 23, batch 50, loss[loss=0.03305, audio_tagging_loss=0.03305, over 25000.00 frames. ], tot_loss[loss=0.03516, audio_tagging_loss=0.03516, over 1116803.91 frames. ], batch size: 100, lr: 1.77e-02, grad_scale: 32.0
2023-12-20 18:09:40,310 INFO [train.py:886] (2/4) Epoch 24, batch 0, loss[loss=0.04133, audio_tagging_loss=0.04133, over 21728.00 frames. ], tot_loss[loss=0.04133, audio_tagging_loss=0.04133, over 21728.00 frames. ], batch size: 106, lr: 1.73e-02, grad_scale: 32.0
2023-12-20 18:09:40,310 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 18:10:01,276 INFO [train.py:917] (2/4) Epoch 24, validation: loss=0.04248, audio_tagging_loss=0.04248, over 3737520.00 frames. 
2023-12-20 18:10:01,277 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 18:10:06,163 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.1.nonlin_attention.balancer.min_positive, batch_count=7973.333333333333, ans=0.17026666666666668
2023-12-20 18:10:12,545 WARNING [optim.py:484] (2/4) Clipping_scale=2.0, grad-norm quartiles 2.742e+01 3.651e+01 4.128e+01 4.777e+01 1.617e+02, threshold=8.255e+01, percent-clipped=1.0
2023-12-20 18:10:14,625 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.1.feed_forward2.hidden_balancer.prob, batch_count=8040.0, ans=0.125
2023-12-20 18:10:16,748 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.1.feed_forward1.out_whiten.whitening_limit, batch_count=8040.0, ans=10.515
2023-12-20 18:10:19,399 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.2.balancer2.prob, batch_count=8040.0, ans=0.125
2023-12-20 18:10:38,625 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.1.feed_forward1.out_proj.dropout_p, batch_count=8173.333333333333, ans=0.21826666666666666
2023-12-20 18:10:43,356 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.1.feed_forward1.out_proj.dropout_p, batch_count=8240.0, ans=0.21760000000000002
2023-12-20 18:10:49,639 INFO [train.py:886] (2/4) Epoch 24, batch 50, loss[loss=0.03189, audio_tagging_loss=0.03189, over 25000.00 frames. ], tot_loss[loss=0.03405, audio_tagging_loss=0.03405, over 1119075.99 frames. ], batch size: 100, lr: 1.73e-02, grad_scale: 32.0
2023-12-20 18:11:13,612 INFO [train.py:886] (2/4) Epoch 25, batch 0, loss[loss=0.0338, audio_tagging_loss=0.0338, over 25000.00 frames. ], tot_loss[loss=0.0338, audio_tagging_loss=0.0338, over 25000.00 frames. ], batch size: 100, lr: 1.70e-02, grad_scale: 32.0
2023-12-20 18:11:13,613 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 18:11:34,705 INFO [train.py:917] (2/4) Epoch 25, validation: loss=0.04257, audio_tagging_loss=0.04257, over 3737520.00 frames. 
2023-12-20 18:11:34,705 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 18:11:37,640 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.3.feed_forward2.out_whiten, num_groups=1, num_channels=512, metric=10.79 vs. limit=10.620000000000001
2023-12-20 18:11:47,148 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.0.conv_module2.whiten, num_groups=1, num_channels=384, metric=9.00 vs. limit=10.645
2023-12-20 18:11:55,445 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.0.feed_forward2.out_whiten, num_groups=1, num_channels=512, metric=10.39 vs. limit=10.67
2023-12-20 18:12:00,768 INFO [scaling.py:1118] (2/4) WithLoss: name=encoder.encoders.3.encoder.layers.3.self_attn_weights, loss-sum=6.559e+00
2023-12-20 18:12:04,796 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.1.whiten, num_groups=1, num_channels=384, metric=5.31 vs. limit=7.4079999999999995
2023-12-20 18:12:06,183 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.1.encoder.layers.0.bypass_mid.scale_min, batch_count=8520.0, ans=0.6018
2023-12-20 18:12:10,415 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.1.feed_forward1.out_proj.dropout_p, batch_count=8520.0, ans=0.2148
2023-12-20 18:12:10,751 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.0.feed_forward3.out_whiten, num_groups=1, num_channels=512, metric=11.24 vs. limit=10.695
2023-12-20 18:12:11,706 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.0.self_attn2.whiten, num_groups=1, num_channels=512, metric=13.88 vs. limit=13.89
2023-12-20 18:12:22,266 INFO [train.py:886] (2/4) Epoch 25, batch 50, loss[loss=0.03228, audio_tagging_loss=0.03228, over 25000.00 frames. ], tot_loss[loss=0.03326, audio_tagging_loss=0.03326, over 1123209.09 frames. ], batch size: 100, lr: 1.70e-02, grad_scale: 32.0
2023-12-20 18:12:45,044 INFO [train.py:886] (2/4) Epoch 26, batch 0, loss[loss=0.04138, audio_tagging_loss=0.04138, over 20177.00 frames. ], tot_loss[loss=0.04138, audio_tagging_loss=0.04138, over 20177.00 frames. ], batch size: 106, lr: 1.66e-02, grad_scale: 32.0
2023-12-20 18:12:45,044 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 18:13:05,884 INFO [train.py:917] (2/4) Epoch 26, validation: loss=0.04241, audio_tagging_loss=0.04241, over 3737520.00 frames. 
2023-12-20 18:13:05,885 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 18:13:11,871 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.0.feed_forward1.out_whiten, num_groups=1, num_channels=256, metric=22.46 vs. limit=10.75
2023-12-20 18:13:12,404 WARNING [optim.py:484] (2/4) Clipping_scale=2.0, grad-norm quartiles 3.047e+01 3.673e+01 4.044e+01 4.675e+01 8.607e+01, threshold=8.088e+01, percent-clipped=1.0
2023-12-20 18:13:12,696 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.2.bypass_mid.scale_min, batch_count=8666.666666666666, ans=0.5966666666666667
2023-12-20 18:13:15,421 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.1.feed_forward1.out_proj.dropout_p, batch_count=8733.333333333334, ans=0.21266666666666667
2023-12-20 18:13:16,350 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.1.feed_forward1.out_proj.dropout_p, batch_count=8733.333333333334, ans=0.21266666666666667
2023-12-20 18:13:16,536 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.5.encoder.layers.0.feed_forward1.out_whiten.whitening_limit, batch_count=8733.333333333334, ans=10.775
2023-12-20 18:13:20,915 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.0.layers.1.bypass_mid.scale_min, batch_count=8733.333333333334, ans=0.5943333333333334
2023-12-20 18:13:23,778 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.1.encoder.layers.1.feed_forward1.out_proj.dropout_p, batch_count=8800.0, ans=0.212
2023-12-20 18:13:31,751 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.1.encoder.layers.0.conv_module2.whiten, num_groups=1, num_channels=256, metric=9.13 vs. limit=10.8
2023-12-20 18:13:41,604 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.1.encoder.layers.1.self_attn1.whiten, num_groups=1, num_channels=256, metric=13.00 vs. limit=14.15
2023-12-20 18:13:44,877 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.0.feed_forward1.hidden_balancer.prob, batch_count=8933.333333333334, ans=0.125
2023-12-20 18:13:47,740 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.1.feed_forward3.out_whiten, num_groups=1, num_channels=256, metric=27.82 vs. limit=10.85
2023-12-20 18:13:52,997 INFO [train.py:886] (2/4) Epoch 26, batch 50, loss[loss=0.02956, audio_tagging_loss=0.02956, over 25000.00 frames. ], tot_loss[loss=0.03224, audio_tagging_loss=0.03224, over 1119526.98 frames. ], batch size: 100, lr: 1.66e-02, grad_scale: 32.0
2023-12-20 18:14:18,299 INFO [train.py:886] (2/4) Epoch 27, batch 0, loss[loss=0.0303, audio_tagging_loss=0.0303, over 25000.00 frames. ], tot_loss[loss=0.0303, audio_tagging_loss=0.0303, over 25000.00 frames. ], batch size: 100, lr: 1.63e-02, grad_scale: 32.0
2023-12-20 18:14:18,300 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 18:14:31,139 INFO [zipformer.py:1858] (2/4) name=encoder.encoders.4.encoder.layers.2.self_attn_weights, attn_weights_entropy = tensor([2.3577, 1.7678, 2.1146, 2.2565], device='cuda:2')
2023-12-20 18:14:37,636 INFO [zipformer.py:1858] (2/4) name=encoder.encoders.5.encoder.layers.1.self_attn_weights, attn_weights_entropy = tensor([2.5915, 3.0487, 2.5976, 2.5353], device='cuda:2')
2023-12-20 18:14:39,325 INFO [train.py:917] (2/4) Epoch 27, validation: loss=0.04294, audio_tagging_loss=0.04294, over 3737520.00 frames. 
2023-12-20 18:14:39,326 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 18:14:44,243 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.1.feed_forward3.out_whiten, num_groups=1, num_channels=256, metric=27.24 vs. limit=10.879999999999999
2023-12-20 18:14:46,294 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.2.feed_forward1.out_whiten, num_groups=1, num_channels=512, metric=11.02 vs. limit=10.879999999999999
2023-12-20 18:14:49,979 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.1.self_attn1.whiten, num_groups=1, num_channels=256, metric=17.70 vs. limit=14.309999999999999
2023-12-20 18:14:51,858 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.1.feed_forward1.out_whiten, num_groups=1, num_channels=256, metric=31.65 vs. limit=10.905
2023-12-20 18:15:09,339 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.5.encoder.layers.1.bypass_mid.scale_min, batch_count=9213.333333333334, ans=0.5775333333333333
2023-12-20 18:15:26,761 INFO [train.py:886] (2/4) Epoch 27, batch 50, loss[loss=0.02842, audio_tagging_loss=0.02842, over 25000.00 frames. ], tot_loss[loss=0.03164, audio_tagging_loss=0.03164, over 1117606.62 frames. ], batch size: 100, lr: 1.63e-02, grad_scale: 32.0
2023-12-20 18:15:48,260 INFO [train.py:886] (2/4) Epoch 28, batch 0, loss[loss=0.0301, audio_tagging_loss=0.0301, over 25000.00 frames. ], tot_loss[loss=0.0301, audio_tagging_loss=0.0301, over 25000.00 frames. ], batch size: 100, lr: 1.60e-02, grad_scale: 32.0
2023-12-20 18:15:48,261 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 18:16:09,705 INFO [train.py:917] (2/4) Epoch 28, validation: loss=0.04282, audio_tagging_loss=0.04282, over 3737520.00 frames. 
2023-12-20 18:16:09,705 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 18:16:12,511 WARNING [optim.py:484] (2/4) Clipping_scale=2.0, grad-norm quartiles 3.131e+01 3.970e+01 4.630e+01 5.343e+01 9.281e+01, threshold=9.260e+01, percent-clipped=1.0
2023-12-20 18:16:24,833 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.1.feed_forward1.out_proj.dropout_p, batch_count=9426.666666666666, ans=0.20573333333333332
2023-12-20 18:16:32,050 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.1.encoder.layers.1.conv_skip_rate, batch_count=9493.333333333334, ans=0.027111111111111114
2023-12-20 18:16:32,078 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.1.conv_module2.balancer2.prob, batch_count=9493.333333333334, ans=0.125
2023-12-20 18:16:38,516 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.0.layers.1.ff2_skip_rate, batch_count=9560.0, ans=0.008791304347826087
2023-12-20 18:16:54,790 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.0.nonlin_attention.whiten1, num_groups=1, num_channels=288, metric=7.45 vs. limit=7.406666666666666
2023-12-20 18:16:56,950 INFO [train.py:886] (2/4) Epoch 28, batch 50, loss[loss=0.02563, audio_tagging_loss=0.02563, over 25000.00 frames. ], tot_loss[loss=0.03101, audio_tagging_loss=0.03101, over 1120311.90 frames. ], batch size: 100, lr: 1.60e-02, grad_scale: 32.0
2023-12-20 18:17:19,793 INFO [train.py:886] (2/4) Epoch 29, batch 0, loss[loss=0.03977, audio_tagging_loss=0.03977, over 20634.00 frames. ], tot_loss[loss=0.03977, audio_tagging_loss=0.03977, over 20634.00 frames. ], batch size: 106, lr: 1.57e-02, grad_scale: 32.0
2023-12-20 18:17:19,793 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 18:17:30,680 INFO [zipformer.py:1858] (2/4) name=encoder.encoders.4.encoder.layers.1.self_attn_weights, attn_weights_entropy = tensor([2.5305, 2.3478, 2.4033, 2.3839], device='cuda:2')
2023-12-20 18:17:31,974 INFO [zipformer.py:1858] (2/4) name=encoder.encoders.3.encoder.layers.1.self_attn_weights, attn_weights_entropy = tensor([1.8318, 1.6548, 1.3662, 1.6184, 1.7442, 1.6820, 1.5322, 1.6898],
       device='cuda:2')
2023-12-20 18:17:40,753 INFO [train.py:917] (2/4) Epoch 29, validation: loss=0.04276, audio_tagging_loss=0.04276, over 3737520.00 frames. 
2023-12-20 18:17:40,754 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 18:17:42,826 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.1.conv_module2.whiten, num_groups=1, num_channels=512, metric=9.55 vs. limit=11.14
2023-12-20 18:18:06,856 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.1.feed_forward3.out_whiten, num_groups=1, num_channels=384, metric=11.34 vs. limit=11.19
2023-12-20 18:18:07,726 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.1.bypass_mid.scale_min, batch_count=9840.0, ans=0.5556000000000001
2023-12-20 18:18:11,856 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.0.layers.1.self_attn1.whiten, num_groups=1, num_channels=192, metric=12.93 vs. limit=14.93
2023-12-20 18:18:29,134 INFO [train.py:886] (2/4) Epoch 29, batch 50, loss[loss=0.02734, audio_tagging_loss=0.02734, over 25000.00 frames. ], tot_loss[loss=0.02999, audio_tagging_loss=0.02999, over 1118289.13 frames. ], batch size: 100, lr: 1.57e-02, grad_scale: 32.0
2023-12-20 18:18:29,998 WARNING [optim.py:484] (2/4) Clipping_scale=2.0, grad-norm quartiles 3.044e+01 4.177e+01 4.600e+01 5.564e+01 7.757e+01, threshold=9.200e+01, percent-clipped=0.0
2023-12-20 18:18:46,965 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.3.bypass.skip_rate, batch_count=10053.333333333334, ans=0.09899494936611666
2023-12-20 18:18:51,720 INFO [train.py:886] (2/4) Epoch 30, batch 0, loss[loss=0.03279, audio_tagging_loss=0.03279, over 24101.00 frames. ], tot_loss[loss=0.03279, audio_tagging_loss=0.03279, over 24101.00 frames. ], batch size: 100, lr: 1.54e-02, grad_scale: 32.0
2023-12-20 18:18:51,721 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 18:18:59,713 INFO [zipformer.py:1858] (2/4) name=encoder.encoders.5.encoder.layers.0.self_attn_weights, attn_weights_entropy = tensor([2.8432, 2.2270, 2.3182, 2.7913], device='cuda:2')
2023-12-20 18:19:01,818 INFO [zipformer.py:1858] (2/4) name=encoder.encoders.5.encoder.layers.0.self_attn_weights, attn_weights_entropy = tensor([2.7919, 2.4071, 2.4041, 2.8809], device='cuda:2')
2023-12-20 18:19:12,593 INFO [train.py:917] (2/4) Epoch 30, validation: loss=0.04346, audio_tagging_loss=0.04346, over 3737520.00 frames. 
2023-12-20 18:19:12,593 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 18:19:28,293 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.0.conv_module2.balancer1.prob, batch_count=10120.0, ans=0.125
2023-12-20 18:19:29,133 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.0.feed_forward1.hidden_balancer.prob, batch_count=10120.0, ans=0.125
2023-12-20 18:19:31,143 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.2.conv_module1.balancer2.prob, batch_count=10186.666666666666, ans=0.125
2023-12-20 18:19:40,991 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.1.feed_forward2.out_whiten, num_groups=1, num_channels=384, metric=11.10 vs. limit=11.345
2023-12-20 18:19:54,857 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.2.nonlin_attention.whiten2, num_groups=1, num_channels=384, metric=5.89 vs. limit=10.16
2023-12-20 18:19:57,519 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.0.self_attn1.whiten, num_groups=1, num_channels=256, metric=14.23 vs. limit=15.24
2023-12-20 18:19:59,939 INFO [train.py:886] (2/4) Epoch 30, batch 50, loss[loss=0.02758, audio_tagging_loss=0.02758, over 25000.00 frames. ], tot_loss[loss=0.02922, audio_tagging_loss=0.02922, over 1121142.49 frames. ], batch size: 100, lr: 1.54e-02, grad_scale: 32.0
2023-12-20 18:20:22,405 INFO [train.py:886] (2/4) Epoch 31, batch 0, loss[loss=0.02425, audio_tagging_loss=0.02425, over 25000.00 frames. ], tot_loss[loss=0.02425, audio_tagging_loss=0.02425, over 25000.00 frames. ], batch size: 100, lr: 1.52e-02, grad_scale: 32.0
2023-12-20 18:20:22,406 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 18:20:32,842 INFO [zipformer.py:1858] (2/4) name=encoder.encoders.3.encoder.layers.1.self_attn_weights, attn_weights_entropy = tensor([2.1955, 1.7944, 1.6471, 1.8199, 1.8954, 1.8125, 1.6294, 1.8020],
       device='cuda:2')
2023-12-20 18:20:33,457 INFO [zipformer.py:1858] (2/4) name=encoder.encoders.1.encoder.layers.1.self_attn_weights, attn_weights_entropy = tensor([2.4324, 2.3833, 2.5484, 2.2773], device='cuda:2')
2023-12-20 18:20:43,502 INFO [train.py:917] (2/4) Epoch 31, validation: loss=0.04363, audio_tagging_loss=0.04363, over 3737520.00 frames. 
2023-12-20 18:20:43,503 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 18:20:50,501 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.1.balancer2.prob, batch_count=10400.0, ans=0.125
2023-12-20 18:21:01,029 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.0.conv_module2.whiten, num_groups=1, num_channels=512, metric=8.58 vs. limit=11.425
2023-12-20 18:21:14,147 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.1.encoder.layers.1.feed_forward1.out_proj.dropout_p, batch_count=10600.0, ans=0.194
2023-12-20 18:21:17,286 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.0.feed_forward1.out_whiten, num_groups=1, num_channels=256, metric=13.01 vs. limit=11.475
2023-12-20 18:21:19,805 INFO [scaling.py:1118] (2/4) WithLoss: name=encoder.encoders.3.encoder.layers.1.self_attn_weights, loss-sum=4.177e-01
2023-12-20 18:21:20,785 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.1.feed_forward2.hidden_balancer.prob, batch_count=10600.0, ans=0.125
2023-12-20 18:21:29,122 WARNING [optim.py:484] (2/4) Clipping_scale=2.0, grad-norm quartiles 3.385e+01 4.278e+01 4.904e+01 5.799e+01 1.168e+02, threshold=9.808e+01, percent-clipped=2.0
2023-12-20 18:21:30,263 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.5.encoder.layers.0.conv_module2.balancer1.max_abs, batch_count=10666.666666666666, ans=10.0
2023-12-20 18:21:31,839 INFO [train.py:886] (2/4) Epoch 31, batch 50, loss[loss=0.02422, audio_tagging_loss=0.02422, over 25000.00 frames. ], tot_loss[loss=0.02842, audio_tagging_loss=0.02842, over 1117091.33 frames. ], batch size: 100, lr: 1.51e-02, grad_scale: 32.0
2023-12-20 18:21:32,102 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.5.encoder.layers.1.bypass.scale_min, batch_count=10733.333333333334, ans=0.5243333333333333
2023-12-20 18:21:54,500 INFO [train.py:886] (2/4) Epoch 32, batch 0, loss[loss=0.03717, audio_tagging_loss=0.03717, over 21316.00 frames. ], tot_loss[loss=0.03717, audio_tagging_loss=0.03717, over 21316.00 frames. ], batch size: 106, lr: 1.49e-02, grad_scale: 32.0
2023-12-20 18:21:54,500 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 18:22:10,447 INFO [zipformer.py:1858] (2/4) name=encoder.encoders.1.encoder.layers.1.self_attn_weights, attn_weights_entropy = tensor([2.5819, 2.4984, 2.6485, 2.4092], device='cuda:2')
2023-12-20 18:22:15,977 INFO [train.py:917] (2/4) Epoch 32, validation: loss=0.04494, audio_tagging_loss=0.04494, over 3737520.00 frames. 
2023-12-20 18:22:15,977 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 18:22:16,155 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.0.balancer2.prob, batch_count=10746.666666666666, ans=0.125
2023-12-20 18:22:20,156 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.1.self_attn2.whiten, num_groups=1, num_channels=512, metric=15.20 vs. limit=15.56
2023-12-20 18:22:20,817 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.1.conv_skip_rate, batch_count=10746.666666666666, ans=0.021888888888888892
2023-12-20 18:22:24,643 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.5.encoder.layers.1.conv_module1.balancer2.prob, batch_count=10813.333333333334, ans=0.125
2023-12-20 18:22:25,558 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.5.encoder.layers.1.conv_module2.balancer2.prob, batch_count=10813.333333333334, ans=0.125
2023-12-20 18:22:37,831 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.0.feed_forward3.out_whiten, num_groups=1, num_channels=512, metric=10.69 vs. limit=11.58
2023-12-20 18:22:38,400 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.1.encoder.layers.1.self_attn_weights.pos_emb_skip_rate, batch_count=10880.0, ans=0.0
2023-12-20 18:22:40,803 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.1.feed_forward1.out_proj.dropout_p, batch_count=10880.0, ans=0.19119999999999998
2023-12-20 18:22:46,330 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder_embed.convnext.out_balancer.prob, batch_count=10946.666666666666, ans=0.125
2023-12-20 18:22:56,758 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.1.conv_module1.balancer2.prob, batch_count=11013.333333333334, ans=0.125
2023-12-20 18:22:58,424 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.1.conv_skip_rate, batch_count=11013.333333333334, ans=0.020777777777777773
2023-12-20 18:23:02,816 INFO [train.py:886] (2/4) Epoch 32, batch 50, loss[loss=0.02819, audio_tagging_loss=0.02819, over 25000.00 frames. ], tot_loss[loss=0.02718, audio_tagging_loss=0.02718, over 1121418.55 frames. ], batch size: 100, lr: 1.49e-02, grad_scale: 32.0
2023-12-20 18:23:23,589 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.0.layers.0.feed_forward1.out_proj.dropout_p, batch_count=11093.333333333334, ans=0.18906666666666666
2023-12-20 18:23:25,183 INFO [train.py:886] (2/4) Epoch 33, batch 0, loss[loss=0.03206, audio_tagging_loss=0.03206, over 21614.00 frames. ], tot_loss[loss=0.03206, audio_tagging_loss=0.03206, over 21614.00 frames. ], batch size: 106, lr: 1.47e-02, grad_scale: 32.0
2023-12-20 18:23:25,184 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 18:23:46,120 INFO [train.py:917] (2/4) Epoch 33, validation: loss=0.0459, audio_tagging_loss=0.0459, over 3737520.00 frames. 
2023-12-20 18:23:46,121 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 18:23:49,136 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.0.balancer1.prob, batch_count=11093.333333333334, ans=0.125
2023-12-20 18:23:59,793 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.3.feed_forward1.out_whiten, num_groups=1, num_channels=512, metric=10.62 vs. limit=11.684999999999999
2023-12-20 18:24:20,377 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.0.layers.0.conv_module2.balancer2.prob, batch_count=11293.333333333334, ans=0.125
2023-12-20 18:24:25,132 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.5.encoder.layers.0.feed_forward1.out_proj.dropout_p, batch_count=11360.0, ans=0.18639999999999998
2023-12-20 18:24:25,944 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.1.conv_skip_rate, batch_count=11360.0, ans=0.019333333333333338
2023-12-20 18:24:26,653 WARNING [optim.py:484] (2/4) Clipping_scale=2.0, grad-norm quartiles 3.339e+01 4.449e+01 5.027e+01 5.967e+01 1.050e+02, threshold=1.005e+02, percent-clipped=1.0
2023-12-20 18:24:32,312 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.2.conv_module2.balancer1.prob, batch_count=11426.666666666666, ans=0.125
2023-12-20 18:24:33,022 INFO [train.py:886] (2/4) Epoch 33, batch 50, loss[loss=0.02471, audio_tagging_loss=0.02471, over 25000.00 frames. ], tot_loss[loss=0.02623, audio_tagging_loss=0.02623, over 1116458.53 frames. ], batch size: 100, lr: 1.47e-02, grad_scale: 32.0
2023-12-20 18:24:54,841 INFO [train.py:886] (2/4) Epoch 34, batch 0, loss[loss=0.02526, audio_tagging_loss=0.02526, over 25000.00 frames. ], tot_loss[loss=0.02526, audio_tagging_loss=0.02526, over 25000.00 frames. ], batch size: 100, lr: 1.44e-02, grad_scale: 32.0
2023-12-20 18:24:54,841 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 18:25:16,061 INFO [train.py:917] (2/4) Epoch 34, validation: loss=0.0463, audio_tagging_loss=0.0463, over 3737520.00 frames. 
2023-12-20 18:25:16,062 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 18:25:29,233 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.3.attention_skip_rate, batch_count=11506.666666666666, ans=0.018722222222222223
2023-12-20 18:25:31,978 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.2.feed_forward1.out_proj.dropout_p, batch_count=11506.666666666666, ans=0.18493333333333334
2023-12-20 18:25:51,741 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.0.layers.0.ff3_skip_rate, batch_count=11640.0, ans=0.00833913043478261
2023-12-20 18:25:56,859 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.1.encoder.layers.1.conv_module1.whiten, num_groups=1, num_channels=256, metric=7.25 vs. limit=11.89
2023-12-20 18:26:02,681 INFO [train.py:886] (2/4) Epoch 34, batch 50, loss[loss=0.02265, audio_tagging_loss=0.02265, over 25000.00 frames. ], tot_loss[loss=0.02531, audio_tagging_loss=0.02531, over 1120059.27 frames. ], batch size: 100, lr: 1.44e-02, grad_scale: 32.0
2023-12-20 18:26:24,394 INFO [train.py:886] (2/4) Epoch 35, batch 0, loss[loss=0.02218, audio_tagging_loss=0.02218, over 25000.00 frames. ], tot_loss[loss=0.02218, audio_tagging_loss=0.02218, over 25000.00 frames. ], batch size: 100, lr: 1.42e-02, grad_scale: 32.0
2023-12-20 18:26:24,395 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 18:26:43,536 INFO [zipformer.py:1858] (2/4) name=encoder.encoders.4.encoder.layers.1.self_attn_weights, attn_weights_entropy = tensor([3.0184, 2.5182, 2.4109, 2.8270], device='cuda:2')
2023-12-20 18:26:45,180 INFO [train.py:917] (2/4) Epoch 35, validation: loss=0.04736, audio_tagging_loss=0.04736, over 3737520.00 frames. 
2023-12-20 18:26:45,181 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 18:27:13,873 INFO [scaling.py:1118] (2/4) WithLoss: name=encoder.encoders.4.encoder.layers.2.self_attn_weights, loss-sum=3.961e-01
2023-12-20 18:27:20,383 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.1.encoder.layers.0.feed_forward2.out_whiten, num_groups=1, num_channels=256, metric=11.55 vs. limit=11.995000000000001
2023-12-20 18:27:23,466 WARNING [optim.py:484] (2/4) Clipping_scale=2.0, grad-norm quartiles 3.764e+01 4.533e+01 5.198e+01 5.955e+01 1.043e+02, threshold=1.040e+02, percent-clipped=1.0
2023-12-20 18:27:23,861 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.0.feed_forward2.out_whiten, num_groups=1, num_channels=256, metric=11.03 vs. limit=12.02
2023-12-20 18:27:32,979 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.1.conv_module2.balancer1.prob, batch_count=12120.0, ans=0.125
2023-12-20 18:27:33,754 INFO [train.py:886] (2/4) Epoch 35, batch 50, loss[loss=0.02357, audio_tagging_loss=0.02357, over 25000.00 frames. ], tot_loss[loss=0.02389, audio_tagging_loss=0.02389, over 1124135.09 frames. ], batch size: 100, lr: 1.42e-02, grad_scale: 32.0
2023-12-20 18:27:55,038 INFO [train.py:886] (2/4) Epoch 36, batch 0, loss[loss=0.03018, audio_tagging_loss=0.03018, over 20513.00 frames. ], tot_loss[loss=0.03018, audio_tagging_loss=0.03018, over 20513.00 frames. ], batch size: 106, lr: 1.40e-02, grad_scale: 32.0
2023-12-20 18:27:55,038 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 18:28:11,987 INFO [zipformer.py:1858] (2/4) name=encoder.encoders.0.layers.0.self_attn_weights, attn_weights_entropy = tensor([5.0909, 4.4174, 4.4397, 4.3308], device='cuda:2')
2023-12-20 18:28:16,073 INFO [train.py:917] (2/4) Epoch 36, validation: loss=0.04841, audio_tagging_loss=0.04841, over 3737520.00 frames. 
2023-12-20 18:28:16,073 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 18:28:16,521 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.3.nonlin_attention.whiten1, num_groups=1, num_channels=384, metric=8.58 vs. limit=8.033333333333333
2023-12-20 18:28:19,839 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.0.layers.1.ff2_skip_rate, batch_count=12133.333333333334, ans=0.008231884057971015
2023-12-20 18:28:41,777 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.1.nonlin_attention.whiten1, num_groups=1, num_channels=384, metric=8.00 vs. limit=8.066666666666666
2023-12-20 18:29:03,192 INFO [train.py:886] (2/4) Epoch 36, batch 50, loss[loss=0.02135, audio_tagging_loss=0.02135, over 25000.00 frames. ], tot_loss[loss=0.02365, audio_tagging_loss=0.02365, over 1119716.98 frames. ], batch size: 100, lr: 1.40e-02, grad_scale: 32.0
2023-12-20 18:29:24,449 INFO [train.py:886] (2/4) Epoch 37, batch 0, loss[loss=0.02786, audio_tagging_loss=0.02786, over 20498.00 frames. ], tot_loss[loss=0.02786, audio_tagging_loss=0.02786, over 20498.00 frames. ], batch size: 106, lr: 1.38e-02, grad_scale: 32.0
2023-12-20 18:29:24,450 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 18:29:34,311 INFO [zipformer.py:1858] (2/4) name=encoder.encoders.0.layers.0.self_attn_weights, attn_weights_entropy = tensor([5.0687, 4.7948, 4.6310, 4.4593], device='cuda:2')
2023-12-20 18:29:45,679 INFO [train.py:917] (2/4) Epoch 37, validation: loss=0.04928, audio_tagging_loss=0.04928, over 3737520.00 frames. 
2023-12-20 18:29:45,680 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 18:29:46,850 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.0.bypass.scale_min, batch_count=12480.0, ans=0.4632
2023-12-20 18:29:49,254 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.0.layers.1.feed_forward2.hidden_balancer.prob, batch_count=12480.0, ans=0.125
2023-12-20 18:29:58,497 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.0.balancer1.prob, batch_count=12546.666666666666, ans=0.125
2023-12-20 18:30:00,383 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.1.ff3_skip_rate, batch_count=12546.666666666666, ans=0.008142028985507246
2023-12-20 18:30:02,179 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.3.feed_forward1.out_proj.dropout_p, batch_count=12546.666666666666, ans=0.17453333333333335
2023-12-20 18:30:12,054 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.1.feed_forward3.out_whiten, num_groups=1, num_channels=256, metric=22.46 vs. limit=12.23
2023-12-20 18:30:19,005 WARNING [optim.py:484] (2/4) Clipping_scale=2.0, grad-norm quartiles 3.554e+01 4.732e+01 5.545e+01 6.466e+01 1.044e+02, threshold=1.109e+02, percent-clipped=1.0
2023-12-20 18:30:26,726 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.1.balancer1.prob, batch_count=12746.666666666666, ans=0.125
2023-12-20 18:30:32,823 INFO [train.py:886] (2/4) Epoch 37, batch 50, loss[loss=0.0178, audio_tagging_loss=0.0178, over 25000.00 frames. ], tot_loss[loss=0.02257, audio_tagging_loss=0.02257, over 1116382.77 frames. ], batch size: 100, lr: 1.38e-02, grad_scale: 32.0
2023-12-20 18:30:55,794 INFO [train.py:886] (2/4) Epoch 38, batch 0, loss[loss=0.02753, audio_tagging_loss=0.02753, over 21206.00 frames. ], tot_loss[loss=0.02753, audio_tagging_loss=0.02753, over 21206.00 frames. ], batch size: 106, lr: 1.36e-02, grad_scale: 32.0
2023-12-20 18:30:55,794 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 18:31:16,994 INFO [train.py:917] (2/4) Epoch 38, validation: loss=0.04916, audio_tagging_loss=0.04916, over 3737520.00 frames. 
2023-12-20 18:31:16,994 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 18:31:20,710 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.5.encoder.layers.0.feed_forward3.hidden_balancer.prob, batch_count=12826.666666666666, ans=0.125
2023-12-20 18:31:21,618 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.5.encoder.layers.0.conv_module2.balancer2.prob, batch_count=12826.666666666666, ans=0.125
2023-12-20 18:31:25,298 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.3.conv_module1.balancer1.prob, batch_count=12826.666666666666, ans=0.125
2023-12-20 18:31:35,637 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.1.encoder.layers.0.conv_module2.balancer2.prob, batch_count=12960.0, ans=0.125
2023-12-20 18:31:39,235 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.2.bypass.skip_rate, batch_count=12960.0, ans=0.09899494936611666
2023-12-20 18:31:41,898 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.1.encoder.layers.0.bypass.skip_rate, batch_count=12960.0, ans=0.04949747468305833
2023-12-20 18:32:04,832 INFO [train.py:886] (2/4) Epoch 38, batch 50, loss[loss=0.01835, audio_tagging_loss=0.01835, over 25000.00 frames. ], tot_loss[loss=0.02232, audio_tagging_loss=0.02232, over 1109728.35 frames. ], batch size: 100, lr: 1.36e-02, grad_scale: 32.0
2023-12-20 18:32:26,418 INFO [train.py:886] (2/4) Epoch 39, batch 0, loss[loss=0.02121, audio_tagging_loss=0.02121, over 24125.00 frames. ], tot_loss[loss=0.02121, audio_tagging_loss=0.02121, over 24125.00 frames. ], batch size: 100, lr: 1.34e-02, grad_scale: 32.0
2023-12-20 18:32:26,419 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 18:32:46,472 INFO [zipformer.py:1858] (2/4) name=encoder.encoders.3.encoder.layers.1.self_attn_weights, attn_weights_entropy = tensor([2.6173, 1.9408, 1.7376, 2.2254, 2.0347, 2.0442, 1.8605, 2.0572],
       device='cuda:2')
2023-12-20 18:32:47,549 INFO [train.py:917] (2/4) Epoch 39, validation: loss=0.05058, audio_tagging_loss=0.05058, over 3737520.00 frames. 
2023-12-20 18:32:47,550 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 18:33:00,005 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.0.layers.1.nonlin_attention.whiten1.whitening_limit, batch_count=13240.0, ans=8.31
2023-12-20 18:33:01,723 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.1.whiten, num_groups=1, num_channels=384, metric=7.85 vs. limit=9.296
2023-12-20 18:33:17,347 WARNING [optim.py:484] (2/4) Clipping_scale=2.0, grad-norm quartiles 3.971e+01 5.139e+01 5.911e+01 6.986e+01 1.449e+02, threshold=1.182e+02, percent-clipped=3.0
2023-12-20 18:33:17,547 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.2.feed_forward2.hidden_balancer.prob, batch_count=13373.333333333334, ans=0.125
2023-12-20 18:33:20,932 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.5.encoder.layers.1.conv_skip_rate, batch_count=13373.333333333334, ans=0.010944444444444444
2023-12-20 18:33:22,595 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.0.layers.0.feed_forward2.hidden_balancer.prob, batch_count=13373.333333333334, ans=0.125
2023-12-20 18:33:29,650 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.0.layers.0.conv_module2.whiten, num_groups=1, num_channels=192, metric=5.23 vs. limit=12.54
2023-12-20 18:33:35,368 INFO [train.py:886] (2/4) Epoch 39, batch 50, loss[loss=0.0191, audio_tagging_loss=0.0191, over 25000.00 frames. ], tot_loss[loss=0.02097, audio_tagging_loss=0.02097, over 1117713.56 frames. ], batch size: 100, lr: 1.34e-02, grad_scale: 32.0
2023-12-20 18:33:57,923 INFO [train.py:886] (2/4) Epoch 40, batch 0, loss[loss=0.02404, audio_tagging_loss=0.02404, over 24092.00 frames. ], tot_loss[loss=0.02404, audio_tagging_loss=0.02404, over 24092.00 frames. ], batch size: 100, lr: 1.32e-02, grad_scale: 32.0
2023-12-20 18:33:57,924 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 18:34:19,043 INFO [train.py:917] (2/4) Epoch 40, validation: loss=0.05208, audio_tagging_loss=0.05208, over 3737520.00 frames. 
2023-12-20 18:34:19,043 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 18:34:21,783 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.1.feed_forward1.out_proj.dropout_p, batch_count=13520.0, ans=0.1648
2023-12-20 18:34:23,890 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.0.feed_forward1.out_whiten, num_groups=1, num_channels=512, metric=11.35 vs. limit=12.57
2023-12-20 18:34:41,804 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.0.layers.0.feed_forward3.out_whiten, num_groups=1, num_channels=192, metric=11.59 vs. limit=12.620000000000001
2023-12-20 18:34:58,456 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.1.bypass_mid.scale_min, batch_count=13786.666666666666, ans=0.4174666666666667
2023-12-20 18:35:06,527 INFO [train.py:886] (2/4) Epoch 40, batch 50, loss[loss=0.01745, audio_tagging_loss=0.01745, over 25000.00 frames. ], tot_loss[loss=0.01985, audio_tagging_loss=0.01985, over 1124183.14 frames. ], batch size: 100, lr: 1.32e-02, grad_scale: 32.0
2023-12-20 18:35:29,528 INFO [train.py:886] (2/4) Epoch 41, batch 0, loss[loss=0.02016, audio_tagging_loss=0.02016, over 25000.00 frames. ], tot_loss[loss=0.02016, audio_tagging_loss=0.02016, over 25000.00 frames. ], batch size: 100, lr: 1.30e-02, grad_scale: 32.0
2023-12-20 18:35:29,528 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 18:35:48,061 INFO [zipformer.py:1858] (2/4) name=encoder.encoders.1.encoder.layers.1.self_attn_weights, attn_weights_entropy = tensor([3.3372, 3.0105, 3.3948, 3.0879], device='cuda:2')
2023-12-20 18:35:50,405 INFO [train.py:917] (2/4) Epoch 41, validation: loss=0.05259, audio_tagging_loss=0.05259, over 3737520.00 frames. 
2023-12-20 18:35:50,406 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 18:36:02,278 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.0.layers.0.attention_skip_rate, batch_count=13933.333333333334, ans=0.008611111111111104
2023-12-20 18:36:04,368 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.0.feed_forward3.out_whiten, num_groups=1, num_channels=256, metric=12.66 vs. limit=12.725
2023-12-20 18:36:16,659 WARNING [optim.py:484] (2/4) Clipping_scale=2.0, grad-norm quartiles 3.775e+01 5.160e+01 5.694e+01 6.780e+01 1.124e+02, threshold=1.139e+02, percent-clipped=0.0
2023-12-20 18:36:23,512 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.0.feed_forward1.out_whiten, num_groups=1, num_channels=256, metric=13.41 vs. limit=12.775
2023-12-20 18:36:31,764 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.2.feed_forward1.out_proj.dropout_p, batch_count=14133.333333333334, ans=0.15866666666666665
2023-12-20 18:36:33,494 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.1.encoder.layers.1.balancer1.prob, batch_count=14133.333333333334, ans=0.125
2023-12-20 18:36:37,896 INFO [train.py:886] (2/4) Epoch 41, batch 50, loss[loss=0.01783, audio_tagging_loss=0.01783, over 25000.00 frames. ], tot_loss[loss=0.01954, audio_tagging_loss=0.01954, over 1118676.14 frames. ], batch size: 100, lr: 1.30e-02, grad_scale: 32.0
2023-12-20 18:37:00,638 INFO [train.py:886] (2/4) Epoch 42, batch 0, loss[loss=0.02661, audio_tagging_loss=0.02661, over 19785.00 frames. ], tot_loss[loss=0.02661, audio_tagging_loss=0.02661, over 19785.00 frames. ], batch size: 106, lr: 1.29e-02, grad_scale: 32.0
2023-12-20 18:37:00,639 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 18:37:21,716 INFO [train.py:917] (2/4) Epoch 42, validation: loss=0.0541, audio_tagging_loss=0.0541, over 3737520.00 frames. 
2023-12-20 18:37:21,717 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 18:37:27,217 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.2.ff2_skip_rate, batch_count=14213.333333333334, ans=0.007779710144927536
2023-12-20 18:37:34,736 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.0.nonlin_attention.balancer.prob, batch_count=14280.0, ans=0.125
2023-12-20 18:37:37,751 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.2.feed_forward3.out_whiten, num_groups=1, num_channels=384, metric=12.72 vs. limit=12.855
2023-12-20 18:38:07,484 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.1.self_attn_weights.whiten_keys, num_groups=4, num_channels=128, metric=4.59 vs. limit=5.172000000000001
2023-12-20 18:38:09,808 INFO [train.py:886] (2/4) Epoch 42, batch 50, loss[loss=0.0145, audio_tagging_loss=0.0145, over 25000.00 frames. ], tot_loss[loss=0.01847, audio_tagging_loss=0.01847, over 1113558.36 frames. ], batch size: 100, lr: 1.29e-02, grad_scale: 32.0
2023-12-20 18:38:32,308 INFO [train.py:886] (2/4) Epoch 43, batch 0, loss[loss=0.02318, audio_tagging_loss=0.02318, over 21325.00 frames. ], tot_loss[loss=0.02318, audio_tagging_loss=0.02318, over 21325.00 frames. ], batch size: 106, lr: 1.27e-02, grad_scale: 32.0
2023-12-20 18:38:32,309 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 18:38:40,419 INFO [zipformer.py:1858] (2/4) name=encoder.encoders.4.encoder.layers.0.self_attn_weights, attn_weights_entropy = tensor([3.4399, 2.6202, 2.8722, 2.6384], device='cuda:2')
2023-12-20 18:38:53,027 INFO [train.py:917] (2/4) Epoch 43, validation: loss=0.05602, audio_tagging_loss=0.05602, over 3737520.00 frames. 
2023-12-20 18:38:53,027 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 18:39:03,929 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.2.bypass_mid.scale_min, batch_count=14626.666666666666, ans=0.38806666666666667
2023-12-20 18:39:16,028 WARNING [optim.py:484] (2/4) Clipping_scale=2.0, grad-norm quartiles 4.316e+01 5.471e+01 6.063e+01 6.688e+01 1.130e+02, threshold=1.213e+02, percent-clipped=0.0
2023-12-20 18:39:19,925 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.1.encoder.layers.1.nonlin_attention.balancer.prob, batch_count=14693.333333333334, ans=0.125
2023-12-20 18:39:29,540 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.1.encoder.layers.1.ff2_skip_rate, batch_count=14760.0, ans=0.007660869565217391
2023-12-20 18:39:31,450 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.1.encoder.layers.0.conv_module2.balancer2.prob, batch_count=14826.666666666666, ans=0.125
2023-12-20 18:39:38,292 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.1.conv_module1.whiten, num_groups=1, num_channels=384, metric=11.23 vs. limit=13.059999999999999
2023-12-20 18:39:41,479 INFO [train.py:886] (2/4) Epoch 43, batch 50, loss[loss=0.01481, audio_tagging_loss=0.01481, over 25000.00 frames. ], tot_loss[loss=0.01771, audio_tagging_loss=0.01771, over 1120921.25 frames. ], batch size: 100, lr: 1.27e-02, grad_scale: 32.0
2023-12-20 18:40:04,353 INFO [train.py:886] (2/4) Epoch 44, batch 0, loss[loss=0.01558, audio_tagging_loss=0.01558, over 25000.00 frames. ], tot_loss[loss=0.01558, audio_tagging_loss=0.01558, over 25000.00 frames. ], batch size: 100, lr: 1.25e-02, grad_scale: 32.0
2023-12-20 18:40:04,354 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 18:40:25,321 INFO [train.py:917] (2/4) Epoch 44, validation: loss=0.05682, audio_tagging_loss=0.05682, over 3737520.00 frames. 
2023-12-20 18:40:25,322 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 18:40:40,813 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.0.layers.1.feed_forward1.out_proj.dropout_p, batch_count=14973.333333333334, ans=0.15026666666666666
2023-12-20 18:40:52,715 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.1.conv_module1.balancer2.prob, batch_count=15040.0, ans=0.125
2023-12-20 18:40:53,888 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.2.self_attn_weights.whiten_keys, num_groups=4, num_channels=128, metric=3.42 vs. limit=5.266
2023-12-20 18:40:57,316 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.0.conv_module2.balancer2.prob, batch_count=15106.666666666666, ans=0.125
2023-12-20 18:41:12,867 INFO [train.py:886] (2/4) Epoch 44, batch 50, loss[loss=0.0152, audio_tagging_loss=0.0152, over 25000.00 frames. ], tot_loss[loss=0.01714, audio_tagging_loss=0.01714, over 1121136.66 frames. ], batch size: 100, lr: 1.25e-02, grad_scale: 32.0
2023-12-20 18:41:35,895 INFO [train.py:886] (2/4) Epoch 45, batch 0, loss[loss=0.01509, audio_tagging_loss=0.01509, over 25000.00 frames. ], tot_loss[loss=0.01509, audio_tagging_loss=0.01509, over 25000.00 frames. ], batch size: 100, lr: 1.24e-02, grad_scale: 32.0
2023-12-20 18:41:35,896 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 18:41:56,899 INFO [train.py:917] (2/4) Epoch 45, validation: loss=0.05811, audio_tagging_loss=0.05811, over 3737520.00 frames. 
2023-12-20 18:41:56,900 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 18:42:15,037 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.0.layers.0.self_attn_weights.whiten_keys, num_groups=4, num_channels=128, metric=3.27 vs. limit=5.298
2023-12-20 18:42:15,214 WARNING [optim.py:484] (2/4) Clipping_scale=2.0, grad-norm quartiles 3.876e+01 5.082e+01 5.625e+01 6.615e+01 1.122e+02, threshold=1.125e+02, percent-clipped=0.0
2023-12-20 18:42:25,650 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.1.conv_module1.whiten, num_groups=1, num_channels=512, metric=8.90 vs. limit=13.295
2023-12-20 18:42:26,389 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.5.encoder.layers.0.feed_forward1.hidden_balancer.prob, batch_count=15453.333333333334, ans=0.125
2023-12-20 18:42:30,048 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.1.feed_forward1.out_proj.dropout_p, batch_count=15453.333333333334, ans=0.14546666666666666
2023-12-20 18:42:35,496 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.2.ff2_skip_rate, batch_count=15520.0, ans=0.007495652173913044
2023-12-20 18:42:38,420 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.1.feed_forward3.out_whiten, num_groups=1, num_channels=256, metric=16.23 vs. limit=13.32
2023-12-20 18:42:44,425 INFO [train.py:886] (2/4) Epoch 45, batch 50, loss[loss=0.0147, audio_tagging_loss=0.0147, over 25000.00 frames. ], tot_loss[loss=0.01575, audio_tagging_loss=0.01575, over 1124023.84 frames. ], batch size: 100, lr: 1.24e-02, grad_scale: 64.0
2023-12-20 18:43:02,004 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.2.self_attn_weights.pos_emb_skip_rate, batch_count=15600.0, ans=0.0
2023-12-20 18:43:02,321 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.1.encoder.layers.0.feed_forward3.out_whiten, num_groups=1, num_channels=256, metric=13.89 vs. limit=13.35
2023-12-20 18:43:06,816 INFO [train.py:886] (2/4) Epoch 46, batch 0, loss[loss=0.01788, audio_tagging_loss=0.01788, over 24133.00 frames. ], tot_loss[loss=0.01788, audio_tagging_loss=0.01788, over 24133.00 frames. ], batch size: 100, lr: 1.22e-02, grad_scale: 64.0
2023-12-20 18:43:06,816 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 18:43:18,177 INFO [zipformer.py:1858] (2/4) name=encoder.encoders.4.encoder.layers.0.self_attn_weights, attn_weights_entropy = tensor([3.5517, 2.9490, 2.9990, 2.8908], device='cuda:2')
2023-12-20 18:43:27,876 INFO [train.py:917] (2/4) Epoch 46, validation: loss=0.05956, audio_tagging_loss=0.05956, over 3737520.00 frames. 
2023-12-20 18:43:27,876 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 18:43:37,053 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.2.conv_module1.balancer1.max_abs, batch_count=15666.666666666666, ans=10.0
2023-12-20 18:43:38,794 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.0.layers.1.feed_forward2.hidden_balancer.prob, batch_count=15666.666666666666, ans=0.125
2023-12-20 18:43:40,748 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.3.bypass.scale_min, batch_count=15666.666666666666, ans=0.3516666666666667
2023-12-20 18:43:49,001 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.1.self_attn_weights.pos_emb_skip_rate, batch_count=15733.333333333334, ans=0.0
2023-12-20 18:43:53,751 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.2.ff3_skip_rate, batch_count=15733.333333333334, ans=0.007449275362318841
2023-12-20 18:43:58,315 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.0.layers.1.self_attn_weights.pos_emb_skip_rate, batch_count=15800.0, ans=0.0
2023-12-20 18:44:15,173 INFO [train.py:886] (2/4) Epoch 46, batch 50, loss[loss=0.01339, audio_tagging_loss=0.01339, over 25000.00 frames. ], tot_loss[loss=0.01494, audio_tagging_loss=0.01494, over 1123908.16 frames. ], batch size: 100, lr: 1.22e-02, grad_scale: 64.0
2023-12-20 18:44:38,147 INFO [train.py:886] (2/4) Epoch 47, batch 0, loss[loss=0.01721, audio_tagging_loss=0.01721, over 20990.00 frames. ], tot_loss[loss=0.01721, audio_tagging_loss=0.01721, over 20990.00 frames. ], batch size: 106, lr: 1.21e-02, grad_scale: 64.0
2023-12-20 18:44:38,148 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 18:44:48,564 INFO [zipformer.py:1858] (2/4) name=encoder.encoders.1.encoder.layers.1.self_attn_weights, attn_weights_entropy = tensor([3.4234, 3.0464, 3.5152, 3.1036], device='cuda:2')
2023-12-20 18:44:59,320 INFO [train.py:917] (2/4) Epoch 47, validation: loss=0.06125, audio_tagging_loss=0.06125, over 3737520.00 frames. 
2023-12-20 18:44:59,320 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 18:45:08,592 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.1.encoder.layers.0.bypass.scale_min, batch_count=16013.333333333334, ans=0.33953333333333335
2023-12-20 18:45:12,269 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.0.layers.1.bypass.skip_rate, batch_count=16013.333333333334, ans=0.035
2023-12-20 18:45:14,000 WARNING [optim.py:484] (2/4) Clipping_scale=2.0, grad-norm quartiles 4.428e+01 5.199e+01 5.973e+01 6.776e+01 1.435e+02, threshold=1.195e+02, percent-clipped=1.0
2023-12-20 18:45:26,524 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.2.encoder.layers.2.feed_forward3.out_whiten, num_groups=1, num_channels=384, metric=13.00 vs. limit=13.530000000000001
2023-12-20 18:45:46,336 INFO [train.py:886] (2/4) Epoch 47, batch 50, loss[loss=0.01396, audio_tagging_loss=0.01396, over 25000.00 frames. ], tot_loss[loss=0.01445, audio_tagging_loss=0.01445, over 1117903.36 frames. ], batch size: 100, lr: 1.21e-02, grad_scale: 64.0
2023-12-20 18:46:04,313 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.2.nonlin_attention.balancer.prob, batch_count=16293.333333333334, ans=0.125
2023-12-20 18:46:08,724 INFO [train.py:886] (2/4) Epoch 48, batch 0, loss[loss=0.01321, audio_tagging_loss=0.01321, over 24086.00 frames. ], tot_loss[loss=0.01321, audio_tagging_loss=0.01321, over 24086.00 frames. ], batch size: 100, lr: 1.20e-02, grad_scale: 64.0
2023-12-20 18:46:08,725 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 18:46:29,402 INFO [train.py:917] (2/4) Epoch 48, validation: loss=0.06238, audio_tagging_loss=0.06238, over 3737520.00 frames. 
2023-12-20 18:46:29,403 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 18:46:39,163 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.0.ff2_skip_rate, batch_count=16360.0, ans=0.00731304347826087
2023-12-20 18:46:46,676 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.5.encoder.layers.1.feed_forward3.out_whiten, num_groups=1, num_channels=256, metric=15.87 vs. limit=13.635
2023-12-20 18:46:47,680 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.0.feed_forward3.out_whiten, num_groups=1, num_channels=384, metric=13.49 vs. limit=13.635
2023-12-20 18:46:50,473 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.3.encoder.layers.2.whiten, num_groups=1, num_channels=512, metric=6.24 vs. limit=10.570666666666668
2023-12-20 18:46:53,941 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.1.conv_module1.balancer1.prob, batch_count=16426.666666666668, ans=0.125
2023-12-20 18:46:55,791 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.1.feed_forward3.hidden_balancer.prob, batch_count=16426.666666666668, ans=0.125
2023-12-20 18:47:10,567 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.1.self_attn_weights.pos_emb_skip_rate, batch_count=16560.0, ans=0.0
2023-12-20 18:47:14,260 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.2.feed_forward1.out_proj.dropout_p, batch_count=16560.0, ans=0.13440000000000002
2023-12-20 18:47:16,770 INFO [train.py:886] (2/4) Epoch 48, batch 50, loss[loss=0.01058, audio_tagging_loss=0.01058, over 25000.00 frames. ], tot_loss[loss=0.01382, audio_tagging_loss=0.01382, over 1127183.65 frames. ], batch size: 100, lr: 1.19e-02, grad_scale: 64.0
2023-12-20 18:47:37,825 INFO [train.py:886] (2/4) Epoch 49, batch 0, loss[loss=0.01802, audio_tagging_loss=0.01802, over 20499.00 frames. ], tot_loss[loss=0.01802, audio_tagging_loss=0.01802, over 20499.00 frames. ], batch size: 106, lr: 1.18e-02, grad_scale: 64.0
2023-12-20 18:47:37,825 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 18:47:58,811 INFO [train.py:917] (2/4) Epoch 49, validation: loss=0.06394, audio_tagging_loss=0.06394, over 3737520.00 frames. 
2023-12-20 18:47:58,811 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 18:48:09,451 WARNING [optim.py:484] (2/4) Clipping_scale=2.0, grad-norm quartiles 4.348e+01 5.324e+01 6.019e+01 6.956e+01 1.317e+02, threshold=1.204e+02, percent-clipped=1.0
2023-12-20 18:48:09,593 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.0.layers.0.conv_module2.balancer1.prob, batch_count=16706.666666666668, ans=0.125
2023-12-20 18:48:09,646 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.1.conv_module1.balancer1.prob, batch_count=16706.666666666668, ans=0.125
2023-12-20 18:48:24,651 INFO [scaling.py:1022] (2/4) Whitening: name=encoder.encoders.4.encoder.layers.2.conv_module2.whiten, num_groups=1, num_channels=384, metric=7.46 vs. limit=13.79
2023-12-20 18:48:28,615 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.2.encoder.layers.1.nonlin_attention.balancer.prob, batch_count=16840.0, ans=0.125
2023-12-20 18:48:41,463 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.5.encoder.layers.0.conv_skip_rate, batch_count=16906.666666666668, ans=0.0
2023-12-20 18:48:45,782 INFO [train.py:886] (2/4) Epoch 49, batch 50, loss[loss=0.01319, audio_tagging_loss=0.01319, over 25000.00 frames. ], tot_loss[loss=0.01358, audio_tagging_loss=0.01358, over 1116344.42 frames. ], batch size: 100, lr: 1.18e-02, grad_scale: 64.0
2023-12-20 18:49:07,494 INFO [train.py:886] (2/4) Epoch 50, batch 0, loss[loss=0.01496, audio_tagging_loss=0.01496, over 24131.00 frames. ], tot_loss[loss=0.01496, audio_tagging_loss=0.01496, over 24131.00 frames. ], batch size: 100, lr: 1.17e-02, grad_scale: 64.0
2023-12-20 18:49:07,495 INFO [train.py:909] (2/4) Computing validation loss
2023-12-20 18:49:17,438 INFO [zipformer.py:1858] (2/4) name=encoder.encoders.2.encoder.layers.2.self_attn_weights, attn_weights_entropy = tensor([3.1588, 2.8040, 2.7916, 2.7895], device='cuda:2')
2023-12-20 18:49:28,222 INFO [train.py:917] (2/4) Epoch 50, validation: loss=0.06678, audio_tagging_loss=0.06678, over 3737520.00 frames. 
2023-12-20 18:49:28,223 INFO [train.py:918] (2/4) Maximum memory allocated so far is 14828MB
2023-12-20 18:49:33,696 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.5.encoder.layers.1.self_attn_weights.pos_emb_skip_rate, batch_count=16986.666666666668, ans=0.0
2023-12-20 18:49:44,732 INFO [scaling.py:1118] (2/4) WithLoss: name=encoder.encoders.2.encoder.layers.2.self_attn_weights, loss-sum=1.049e-02
2023-12-20 18:49:59,090 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.4.encoder.layers.0.conv_module2.balancer1.prob, batch_count=17186.666666666668, ans=0.125
2023-12-20 18:50:06,286 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.1.encoder.layers.1.bypass.scale_min, batch_count=17253.333333333332, ans=0.29613333333333347
2023-12-20 18:50:07,229 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.3.encoder.layers.0.ff2_skip_rate, batch_count=17253.333333333332, ans=0.007118840579710146
2023-12-20 18:50:15,466 INFO [train.py:886] (2/4) Epoch 50, batch 50, loss[loss=0.01167, audio_tagging_loss=0.01167, over 25000.00 frames. ], tot_loss[loss=0.01317, audio_tagging_loss=0.01317, over 1119154.56 frames. ], batch size: 100, lr: 1.17e-02, grad_scale: 32.0
2023-12-20 18:50:15,698 INFO [scaling.py:213] (2/4) ScheduledFloat: name=encoder.encoders.5.encoder.layers.1.self_attn_weights.pos_emb_skip_rate, batch_count=17320.0, ans=0.0
2023-12-20 18:50:18,099 INFO [train.py:1099] (2/4) Done!