[2024-03-06 17:57:16,416] INFO: Will use torch.nn.parallel.DistributedDataParallel() and 4 gpus [2024-03-06 17:57:16,420] INFO: NVIDIA GeForce GTX 1080 Ti [2024-03-06 17:57:16,421] INFO: NVIDIA GeForce GTX 1080 Ti [2024-03-06 17:57:16,421] INFO: NVIDIA GeForce GTX 1080 Ti [2024-03-06 17:57:16,421] INFO: NVIDIA GeForce GTX 1080 Ti [2024-03-06 17:57:24,342] INFO: using attention_type=efficient [2024-03-06 17:57:24,347] INFO: using attention_type=efficient [2024-03-06 17:57:24,352] INFO: using attention_type=efficient [2024-03-06 17:57:24,357] INFO: using attention_type=efficient [2024-03-06 17:57:24,362] INFO: using attention_type=efficient [2024-03-06 17:57:24,367] INFO: using attention_type=efficient [2024-03-06 17:57:28,462] INFO: DistributedDataParallel( (module): MLPF( (nn0): Sequential( (0): Linear(in_features=42, out_features=256, bias=True) (1): ELU(alpha=1.0) (2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (3): Dropout(p=0.3, inplace=False) (4): Linear(in_features=256, out_features=256, bias=True) ) (conv_id): ModuleList( (0-2): 3 x SelfAttentionLayer( (mha): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True) ) (norm0): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (seq): Sequential( (0): Linear(in_features=256, out_features=256, bias=True) (1): ELU(alpha=1.0) (2): Linear(in_features=256, out_features=256, bias=True) (3): ELU(alpha=1.0) ) (dropout): Dropout(p=0.3, inplace=False) ) ) (conv_reg): ModuleList( (0-2): 3 x SelfAttentionLayer( (mha): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True) ) (norm0): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (seq): Sequential( (0): Linear(in_features=256, out_features=256, bias=True) (1): ELU(alpha=1.0) (2): Linear(in_features=256, out_features=256, bias=True) (3): ELU(alpha=1.0) ) (dropout): Dropout(p=0.3, inplace=False) ) ) (nn_id): Sequential( (0): Linear(in_features=810, out_features=256, bias=True) (1): ELU(alpha=1.0) (2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (3): Dropout(p=0.3, inplace=False) (4): Linear(in_features=256, out_features=9, bias=True) ) (nn_pt): RegressionOutput( (nn): Sequential( (0): Linear(in_features=819, out_features=256, bias=True) (1): ELU(alpha=1.0) (2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (3): Dropout(p=0.3, inplace=False) (4): Linear(in_features=256, out_features=2, bias=True) ) ) (nn_eta): RegressionOutput( (nn): Sequential( (0): Linear(in_features=819, out_features=256, bias=True) (1): ELU(alpha=1.0) (2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (3): Dropout(p=0.3, inplace=False) (4): Linear(in_features=256, out_features=2, bias=True) ) ) (nn_sin_phi): RegressionOutput( (nn): Sequential( (0): Linear(in_features=819, out_features=256, bias=True) (1): ELU(alpha=1.0) (2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (3): Dropout(p=0.3, inplace=False) (4): Linear(in_features=256, out_features=2, bias=True) ) ) (nn_cos_phi): RegressionOutput( (nn): Sequential( (0): Linear(in_features=819, out_features=256, bias=True) (1): ELU(alpha=1.0) (2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (3): Dropout(p=0.3, inplace=False) (4): Linear(in_features=256, out_features=2, bias=True) ) ) (nn_energy): RegressionOutput( (nn): Sequential( (0): Linear(in_features=819, out_features=256, bias=True) (1): ELU(alpha=1.0) (2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (3): Dropout(p=0.3, inplace=False) (4): Linear(in_features=256, out_features=2, bias=True) ) ) (nn_charge): Sequential( (0): Linear(in_features=819, out_features=256, bias=True) (1): ELU(alpha=1.0) (2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (3): Dropout(p=0.3, inplace=False) (4): Linear(in_features=256, out_features=3, bias=True) ) (nn_probX): Sequential( (0): Linear(in_features=819, out_features=256, bias=True) (1): ELU(alpha=1.0) (2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (3): Dropout(p=0.3, inplace=False) (4): Linear(in_features=256, out_features=1, bias=True) ) ) ) [2024-03-06 17:57:28,464] INFO: Trainable parameters: 4139031 [2024-03-06 17:57:28,464] INFO: Non-trainable parameters: 0 [2024-03-06 17:57:28,464] INFO: Total parameters: 4139031 [2024-03-06 17:57:28,472] INFO: Modules Trainable params Non-tranable params Trainable Parameters Non-tranable Parameters module.nn0.0.weight NaN NaN 10752.0 - module.nn0.0.bias NaN NaN 256.0 - module.nn0.2.weight NaN NaN 256.0 - module.nn0.2.bias NaN NaN 256.0 - module.nn0.4.weight NaN NaN 65536.0 - module.nn0.4.bias NaN NaN 256.0 - module.conv_id.0.mha.in_proj_weight NaN NaN 196608.0 - module.conv_id.0.mha.in_proj_bias NaN NaN 768.0 - module.conv_id.0.mha.out_proj.weight NaN NaN 65536.0 - module.conv_id.0.mha.out_proj.bias NaN NaN 256.0 - module.conv_id.0.norm0.weight NaN NaN 256.0 - module.conv_id.0.norm0.bias NaN NaN 256.0 - module.conv_id.0.norm1.weight NaN NaN 256.0 - module.conv_id.0.norm1.bias NaN NaN 256.0 - module.conv_id.0.seq.0.weight NaN NaN 65536.0 - module.conv_id.0.seq.0.bias NaN NaN 256.0 - module.conv_id.0.seq.2.weight NaN NaN 65536.0 - module.conv_id.0.seq.2.bias NaN NaN 256.0 - module.conv_id.1.mha.in_proj_weight NaN NaN 196608.0 - module.conv_id.1.mha.in_proj_bias NaN NaN 768.0 - module.conv_id.1.mha.out_proj.weight NaN NaN 65536.0 - module.conv_id.1.mha.out_proj.bias NaN NaN 256.0 - module.conv_id.1.norm0.weight NaN NaN 256.0 - module.conv_id.1.norm0.bias NaN NaN 256.0 - module.conv_id.1.norm1.weight NaN NaN 256.0 - module.conv_id.1.norm1.bias NaN NaN 256.0 - module.conv_id.1.seq.0.weight NaN NaN 65536.0 - module.conv_id.1.seq.0.bias NaN NaN 256.0 - module.conv_id.1.seq.2.weight NaN NaN 65536.0 - module.conv_id.1.seq.2.bias NaN NaN 256.0 - module.conv_id.2.mha.in_proj_weight NaN NaN 196608.0 - module.conv_id.2.mha.in_proj_bias NaN NaN 768.0 - module.conv_id.2.mha.out_proj.weight NaN NaN 65536.0 - module.conv_id.2.mha.out_proj.bias NaN NaN 256.0 - module.conv_id.2.norm0.weight NaN NaN 256.0 - module.conv_id.2.norm0.bias NaN NaN 256.0 - module.conv_id.2.norm1.weight NaN NaN 256.0 - module.conv_id.2.norm1.bias NaN NaN 256.0 - module.conv_id.2.seq.0.weight NaN NaN 65536.0 - module.conv_id.2.seq.0.bias NaN NaN 256.0 - module.conv_id.2.seq.2.weight NaN NaN 65536.0 - module.conv_id.2.seq.2.bias NaN NaN 256.0 - module.conv_reg.0.mha.in_proj_weight NaN NaN 196608.0 - module.conv_reg.0.mha.in_proj_bias NaN NaN 768.0 - module.conv_reg.0.mha.out_proj.weight NaN NaN 65536.0 - module.conv_reg.0.mha.out_proj.bias NaN NaN 256.0 - module.conv_reg.0.norm0.weight NaN NaN 256.0 - module.conv_reg.0.norm0.bias NaN NaN 256.0 - module.conv_reg.0.norm1.weight NaN NaN 256.0 - module.conv_reg.0.norm1.bias NaN NaN 256.0 - module.conv_reg.0.seq.0.weight NaN NaN 65536.0 - module.conv_reg.0.seq.0.bias NaN NaN 256.0 - module.conv_reg.0.seq.2.weight NaN NaN 65536.0 - module.conv_reg.0.seq.2.bias NaN NaN 256.0 - module.conv_reg.1.mha.in_proj_weight NaN NaN 196608.0 - module.conv_reg.1.mha.in_proj_bias NaN NaN 768.0 - module.conv_reg.1.mha.out_proj.weight NaN NaN 65536.0 - module.conv_reg.1.mha.out_proj.bias NaN NaN 256.0 - module.conv_reg.1.norm0.weight NaN NaN 256.0 - module.conv_reg.1.norm0.bias NaN NaN 256.0 - module.conv_reg.1.norm1.weight NaN NaN 256.0 - module.conv_reg.1.norm1.bias NaN NaN 256.0 - module.conv_reg.1.seq.0.weight NaN NaN 65536.0 - module.conv_reg.1.seq.0.bias NaN NaN 256.0 - module.conv_reg.1.seq.2.weight NaN NaN 65536.0 - module.conv_reg.1.seq.2.bias NaN NaN 256.0 - module.conv_reg.2.mha.in_proj_weight NaN NaN 196608.0 - module.conv_reg.2.mha.in_proj_bias NaN NaN 768.0 - module.conv_reg.2.mha.out_proj.weight NaN NaN 65536.0 - module.conv_reg.2.mha.out_proj.bias NaN NaN 256.0 - module.conv_reg.2.norm0.weight NaN NaN 256.0 - module.conv_reg.2.norm0.bias NaN NaN 256.0 - module.conv_reg.2.norm1.weight NaN NaN 256.0 - module.conv_reg.2.norm1.bias NaN NaN 256.0 - module.conv_reg.2.seq.0.weight NaN NaN 65536.0 - module.conv_reg.2.seq.0.bias NaN NaN 256.0 - module.conv_reg.2.seq.2.weight NaN NaN 65536.0 - module.conv_reg.2.seq.2.bias NaN NaN 256.0 - module.nn_id.0.weight NaN NaN 207360.0 - module.nn_id.0.bias NaN NaN 256.0 - module.nn_id.2.weight NaN NaN 256.0 - module.nn_id.2.bias NaN NaN 256.0 - module.nn_id.4.weight NaN NaN 2304.0 - module.nn_id.4.bias NaN NaN 9.0 - module.nn_pt.nn.0.weight NaN NaN 209664.0 - module.nn_pt.nn.0.bias NaN NaN 256.0 - module.nn_pt.nn.2.weight NaN NaN 256.0 - module.nn_pt.nn.2.bias NaN NaN 256.0 - module.nn_pt.nn.4.weight NaN NaN 512.0 - module.nn_pt.nn.4.bias NaN NaN 2.0 - module.nn_eta.nn.0.weight NaN NaN 209664.0 - module.nn_eta.nn.0.bias NaN NaN 256.0 - module.nn_eta.nn.2.weight NaN NaN 256.0 - module.nn_eta.nn.2.bias NaN NaN 256.0 - module.nn_eta.nn.4.weight NaN NaN 512.0 - module.nn_eta.nn.4.bias NaN NaN 2.0 - module.nn_sin_phi.nn.0.weight NaN NaN 209664.0 - module.nn_sin_phi.nn.0.bias NaN NaN 256.0 - module.nn_sin_phi.nn.2.weight NaN NaN 256.0 - module.nn_sin_phi.nn.2.bias NaN NaN 256.0 - module.nn_sin_phi.nn.4.weight NaN NaN 512.0 - module.nn_sin_phi.nn.4.bias NaN NaN 2.0 - module.nn_cos_phi.nn.0.weight NaN NaN 209664.0 - module.nn_cos_phi.nn.0.bias NaN NaN 256.0 - module.nn_cos_phi.nn.2.weight NaN NaN 256.0 - module.nn_cos_phi.nn.2.bias NaN NaN 256.0 - module.nn_cos_phi.nn.4.weight NaN NaN 512.0 - module.nn_cos_phi.nn.4.bias NaN NaN 2.0 - module.nn_energy.nn.0.weight NaN NaN 209664.0 - module.nn_energy.nn.0.bias NaN NaN 256.0 - module.nn_energy.nn.2.weight NaN NaN 256.0 - module.nn_energy.nn.2.bias NaN NaN 256.0 - module.nn_energy.nn.4.weight NaN NaN 512.0 - module.nn_energy.nn.4.bias NaN NaN 2.0 - module.nn_charge.0.weight NaN NaN 209664.0 - module.nn_charge.0.bias NaN NaN 256.0 - module.nn_charge.2.weight NaN NaN 256.0 - module.nn_charge.2.bias NaN NaN 256.0 - module.nn_charge.4.weight NaN NaN 768.0 - module.nn_charge.4.bias NaN NaN 3.0 - module.nn_probX.0.weight NaN NaN 209664.0 - module.nn_probX.0.bias NaN NaN 256.0 - module.nn_probX.2.weight NaN NaN 256.0 - module.nn_probX.2.bias NaN NaN 256.0 - module.nn_probX.4.weight NaN NaN 256.0 - module.nn_probX.4.bias NaN NaN 1.0 - [2024-03-06 17:57:28,516] INFO: Creating experiment dir /pfvol/experiments/MLPF_cms_Transformer_MET_True_pyg-cms-ttbar_20240306_175715_567808 [2024-03-06 17:57:28,516] INFO: Model directory /pfvol/experiments/MLPF_cms_Transformer_MET_True_pyg-cms-ttbar_20240306_175715_567808 [2024-03-06 17:57:29,192] INFO: train_dataset: cms_pf_ttbar, 80000 [2024-03-06 17:57:29,239] INFO: valid_dataset: cms_pf_ttbar, 20000 [2024-03-06 17:57:29,460] INFO: Initiating epoch #1 train run on device rank=0 [2024-03-06 22:01:21,664] INFO: Initiating epoch #1 valid run on device rank=0 [2024-03-06 22:16:14,411] INFO: Rank 0: epoch=1 / 30 train_loss=86.4766 valid_loss=81.6310 stale=0 time=258.75m eta=7503.7m [2024-03-06 22:16:14,434] INFO: Initiating epoch #2 train run on device rank=0 [2024-03-07 02:20:23,183] INFO: Initiating epoch #2 valid run on device rank=0 [2024-03-07 02:31:22,498] INFO: Rank 0: epoch=2 / 30 train_loss=79.8290 valid_loss=79.4440 stale=0 time=255.13m eta=7194.4m [2024-03-07 02:31:24,030] INFO: Initiating epoch #3 train run on device rank=0 [2024-03-07 06:36:52,422] INFO: Initiating epoch #3 valid run on device rank=0 [2024-03-07 06:50:21,835] INFO: Rank 0: epoch=3 / 30 train_loss=78.9524 valid_loss=79.9011 stale=1 time=258.96m eta=6955.9m [2024-03-07 06:50:22,940] INFO: Initiating epoch #4 train run on device rank=0 [2024-03-07 10:54:06,921] INFO: Initiating epoch #4 valid run on device rank=0 [2024-03-07 11:05:03,790] INFO: Rank 0: epoch=4 / 30 train_loss=78.5508 valid_loss=79.3318 stale=0 time=254.68m eta=6679.2m [2024-03-07 11:05:04,355] INFO: Initiating epoch #5 train run on device rank=0