NOTE: Training on 20000000 tokens
buffer_size: 244, buffer_size_in_tokens: 250000
coreset_ratio: 0.5, effective_sae_batch_size: 1024
[transformers] `torch_dtype` is deprecated! Use `dtype` instead!

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Model parameters before truncation: 2,614,341,888
Model parameters after truncation: 1,134,888,192
len trainer configs: 12

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Calculating norm factor: 101it [00:03, 25.46it/s]                       
Average mean squared norm: 8256.0
Norm factor: 91.0

============================================================
CORESET CONFIGURATION
============================================================
Coreset Ratio: 0.5
Coreset Method: random
Main Buffer Size: 249856
Proxy Buffer Size: 124928
============================================================


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Model parameters before truncation: 2,614,341,888
Model parameters after truncation: 1,134,888,192
/sfs/weka/scratch/euh7ys/data-sae/dictionary_learning/dictionary_learning/dictionary.py:361: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  state_dict = t.load(path)
{'l2_loss': 47.58, 'l1_loss': 1032.08, 'l0': 380.148388671875, 'frac_variance_explained': 0.49599609375, 'cossim': 0.90185546875, 'l2_ratio': 1.10875, 'relative_reconstruction_bias': 1.404140625, 'frac_alive': 1.0, 'hyperparameters': {'n_inputs': 200, 'context_length': 1024}}
/sfs/weka/scratch/euh7ys/data-sae/dictionary_learning/dictionary_learning/trainers/matryoshka_batch_top_k.py:124: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  state_dict = t.load(path)
{'l2_loss': 43.72, 'l1_loss': 775.42, 'l0': 204.0459228515625, 'frac_variance_explained': 0.67376953125, 'cossim': 0.87337890625, 'l2_ratio': 0.9259765625, 'relative_reconstruction_bias': 1.08, 'frac_alive': 1.0, 'hyperparameters': {'n_inputs': 200, 'context_length': 1024}}
{'l2_loss': 47.01375, 'l1_loss': 511.5, 'l0': 113.0058154296875, 'frac_variance_explained': 0.6194140625, 'cossim': 0.852265625, 'l2_ratio': 0.90916015625, 'relative_reconstruction_bias': 1.0912109375, 'frac_alive': 1.0, 'hyperparameters': {'n_inputs': 200, 'context_length': 1024}}
/sfs/weka/scratch/euh7ys/data-sae/dictionary_learning/dictionary_learning/trainers/batch_top_k.py:82: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  state_dict = t.load(path)
{'l2_loss': 44.1825, 'l1_loss': 740.56, 'l0': 281.0050732421875, 'frac_variance_explained': 0.61173828125, 'cossim': 0.8912890625, 'l2_ratio': 0.99958984375, 'relative_reconstruction_bias': 1.2373828125, 'frac_alive': 1.0, 'hyperparameters': {'n_inputs': 200, 'context_length': 1024}}
{'l2_loss': 47.59125, 'l1_loss': 596.38, 'l0': 151.1995703125, 'frac_variance_explained': 0.57576171875, 'cossim': 0.8666015625, 'l2_ratio': 0.97955078125, 'relative_reconstruction_bias': 1.2273046875, 'frac_alive': 0.9999847412109375, 'hyperparameters': {'n_inputs': 200, 'context_length': 1024}}
{'l2_loss': 48.755, 'l1_loss': 752.3, 'l0': 193.736484375, 'frac_variance_explained': 0.49390625, 'cossim': 0.87962890625, 'l2_ratio': 1.0437890625, 'relative_reconstruction_bias': 1.3559765625, 'frac_alive': 0.9998779296875, 'hyperparameters': {'n_inputs': 200, 'context_length': 1024}}
{'l2_loss': 42.8575, 'l1_loss': 573.62, 'l0': 210.2701318359375, 'frac_variance_explained': 0.68017578125, 'cossim': 0.87849609375, 'l2_ratio': 0.91634765625, 'relative_reconstruction_bias': 1.0659375, 'frac_alive': 1.0, 'hyperparameters': {'n_inputs': 200, 'context_length': 1024}}
{'l2_loss': 42.3025, 'l1_loss': 729.84, 'l0': 235.820771484375, 'frac_variance_explained': 0.68578125, 'cossim': 0.885078125, 'l2_ratio': 0.942265625, 'relative_reconstruction_bias': 1.096875, 'frac_alive': 1.0, 'hyperparameters': {'n_inputs': 200, 'context_length': 1024}}
{'l2_loss': 43.71875, 'l1_loss': 774.76, 'l0': 242.2191259765625, 'frac_variance_explained': 0.65115234375, 'cossim': 0.88484375, 'l2_ratio': 0.97599609375, 'relative_reconstruction_bias': 1.1720703125, 'frac_alive': 0.9999847412109375, 'hyperparameters': {'n_inputs': 200, 'context_length': 1024}}
{'l2_loss': 45.31125, 'l1_loss': 547.06, 'l0': 125.0025048828125, 'frac_variance_explained': 0.63720703125, 'cossim': 0.86404296875, 'l2_ratio': 0.9146875, 'relative_reconstruction_bias': 1.0946875, 'frac_alive': 0.99993896484375, 'hyperparameters': {'n_inputs': 200, 'context_length': 1024}}
{'l2_loss': 45.8725, 'l1_loss': 433.84, 'l0': 116.2332275390625, 'frac_variance_explained': 0.6315234375, 'cossim': 0.858984375, 'l2_ratio': 0.90171875, 'relative_reconstruction_bias': 1.07796875, 'frac_alive': 1.0, 'hyperparameters': {'n_inputs': 200, 'context_length': 1024}}
{'l2_loss': 47.98375, 'l1_loss': 598.54, 'l0': 173.24548828125, 'frac_variance_explained': 0.532109375, 'cossim': 0.8730859375, 'l2_ratio': 1.00138671875, 'relative_reconstruction_bias': 1.2932421875, 'frac_alive': 1.0, 'hyperparameters': {'n_inputs': 200, 'context_length': 1024}}
Total time: 3629.0230674743652