diff --git a/LTA_openwebtext_dualt/logs/lta_owt_classic_fullvocab_bert_c1024_len128_gbs512_4gpu_1m_save1k_20260521_210848.nohup.log b/LTA_openwebtext_dualt/logs/lta_owt_classic_fullvocab_bert_c1024_len128_gbs512_4gpu_1m_save1k_20260521_210848.nohup.log new file mode 100644 index 0000000000000000000000000000000000000000..4e6ec4a582f42f5a75084b2b086947de1cd7cee2 --- /dev/null +++ b/LTA_openwebtext_dualt/logs/lta_owt_classic_fullvocab_bert_c1024_len128_gbs512_4gpu_1m_save1k_20260521_210848.nohup.log @@ -0,0 +1,314 @@ +[launch] method=owt_classic_fullvocab_bert_c1024_len128 host=di-20260411014000-djqhq time=2026-05-21T21:08:48+00:00 +[launch] run_name=lta_owt_classic_fullvocab_bert_c1024_len128_gbs512_4gpu_1m_save1k_20260521_210848 +[launch] save_dir=runs/lta_owt_classic_fullvocab_bert_c1024_len128_gbs512_4gpu_1m_save1k_20260521_210848 +[launch] log_file=logs/lta_owt_classic_fullvocab_bert_c1024_len128_gbs512_4gpu_1m_save1k_20260521_210848.log +[launch] data_path=/e2e-data/evad-tech-vla/wanghan58/data/small_benchmarks/langflow_2604_11748/openwebtext +[launch] split=train_minus_100k text_column=text +[launch] tokenizer=/e2e-data/evad-tech-vla/wanghan58/workspace/imagenet_handoff_20260327/nlp_dts_light/assets/distilbert-base-uncased/tokenizer.json +[launch] max_len=128 wrap=stream buffer=200 +[launch] world_size=4 per_gpu_batch=64 global_batch=512 grad_accum≈2 +[launch] classic recipe: lr=3e-4 constant_warmup warmup=2500 wd=0 beta2=0.999 dropout=0.1 bf16 mask=0.1->1.0 C=1024 +[launch] save_every=1000 latest_every=1000 total_steps=1000000 + +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +NCCL version 2.25.1+cuda12.8 +{ + "device": "cuda:0", + "rank": 0, + "world_size": 4, + "samples": "wrapped_stream", + "vocab_size": 30522, + "tokenizer_vocab_size": 30522, + "save_dir": "runs/lta_owt_classic_fullvocab_bert_c1024_len128_gbs512_4gpu_1m_save1k_20260521_210848", + "max_len": 128, + "effective_model_max_len": 128, + "batch_size": 64, + "grad_accum": 2, + "effective_batch_size": 512, + "global_batch_size": 512, + "lr_schedule": "constant_warmup", + "optimizer": "adamw", + "epochs": 0.0, + "steps_per_epoch": 0, + "total_steps": 1000000, + "warmup_steps": 2500, + "warmup_epochs": -1.0, + "min_lr": 6e-05, + "weight_decay": 0.0, + "output_weight_decay": -1.0, + "adamw_param_groups": "nanogpt", + "adam_beta1": 0.9, + "adam_beta2": 0.999, + "adam_eps": 1e-08, + "muon_impl": "legacy", + "muon_momentum": 0.95, + "muon_ns_steps": 5, + "muon_update_scale": 1.0, + "muon_nesterov": false, + "muon_width_scale": false, + "muon_grouping": "", + "muon_param_count": 0, + "muon_adam_param_count": 0, + "muon_param_names": [], + "muon_adam_param_names": [], + "muon_effective_nesterov": false, + "muon_effective_width_scale": false, + "muon_effective_weight_decay": 0.0, + "muon_adam_fallback_nesterov": false, + "muon_adam_fallback_weight_decay": 0.0, + "ema_decay": 0.0, + "ema_start_step": 0, + "model_type": "ddit", + "ddit_mlp_type": "gelu", + "elf_num_time_tokens": 4, + "elf_num_model_mode_tokens": 0, + "qk_norm": true, + "output_bias": false, + "output_init_std": -1.0, + "norm_type": "rmsnorm", + "target_loss": "hard_ce", + "linear_soft_target_power": 1.0, + "linear_soft_target_min_conf": 0.0, + "linear_soft_target_max_conf": 1.0, + "t_sampling_mode": "uniform", + "t_sampling_power": 1.0, + "t_sampling_eps": 0.0001, + "t_sampling_logit_mean": -1.5, + "t_sampling_logit_std": 0.8, + "t_sampling_gumbel_loc": 2.2, + "t_sampling_gumbel_scale": 0.8, + "dual_t": true, + "corrupt_t_mode": "same", + "corrupt_min_t": 0.0, + "corrupt_max_t": 1.0, + "prefix_block_prob": 0.0, + "prefix_block_len": 128, + "block_ar_two_stream": false, + "block_ar_block_len": 128, + "mask_ratio_floor_schedule": "none", + "dirichlet_endpoint_mode": "categorical_dual_t", + "dirichlet_semantic_t_mode": "same", + "dirichlet_semantic_t_value": 0.0, + "dirichlet_semantic_t_curve": "linear", + "dirichlet_semantic_t_power": 1.0, + "dirichlet_support_t_curve": "linear", + "dirichlet_support_t_power": 1.0, + "endpoint_sequence_random_prob_alpha": 0.0, + "categorical_wrong_from_full_vocab": true, + "categorical_wrong_from_batch_valid_tokens": false, + "categorical_wrong_basin_token_ids": "", + "categorical_wrong_basin_prob": 0.0, + "categorical_wrong_unigram_prob": 0.0, + "categorical_wrong_uniform_prob": 0.0, + "categorical_wrong_prob_floor": 0.0, + "categorical_gold_prob_floor": 0.0, + "categorical_gold_prob_ceil": 1.0, + "categorical_wrong_corpus_unigram_path": "", + "categorical_wrong_corpus_unigram_alpha": 1.0, + "categorical_wrong_basin_shared_prob": 0.0, + "categorical_wrong_unigram_shared_prob": 0.0, + "mask_mixture_original_prob": 0.0, + "mask_mixture_lowk_prob": 0.0, + "mask_mixture_lowcorrupt_prob": 0.0, + "mask_mixture_block_prob": 0.0, + "mask_mixture_all_prob": 0.0, + "mask_mixture_lowk_clean_tokens": "1,2,4,8,16,32,64", + "mask_mixture_lowcorrupt_tokens": "1,2,4,8,16,32,64", + "mask_mixture_block_tokens": "64,128", + "simplex_bridge_sampler": "dirichlet", + "logistic_normal_sigma_min": 0.18, + "logistic_normal_sigma_max": 2.2, + "logistic_normal_tau_min": 0.65, + "logistic_normal_tau_max": 1.15, + "torch_compile": false, + "compile_mode": "max-autotune", + "state_format": "prob", + "meanflow_weight": 0.0, + "rollout_train_prob": 0.0, + "rollout_train_steps": 1, + "rollout_train_steps_min": -1, + "rollout_train_infer_steps": 64, + "rollout_train_time_mode": "fixed_steps", + "rollout_train_s_dist": "uniform", + "rollout_train_s_min_frac": 0.0, + "rollout_train_s_max_frac": 0.125, + "rollout_train_s_beta_alpha": 2.0, + "rollout_train_s_beta_beta": 6.0, + "rollout_train_temp": 1.0, + "rollout_train_max_gamma": 1.0, + "rollout_train_rule": "flowmap", + "rollout_train_corrupt_only": true, + "rollout_train_samplewise": false, + "rollout_train_compute_always": false, + "rollout_train_keep_grad": false, + "rollout_train_sync_t": false, + "rollout_train_state_mix_mode": "final", + "rollout_train_state_mix_alpha": 0.5, + "bridge_noise_init": "logistic_normal", + "noise_sigma": -1.0, + "allow_tf32": true, + "activation_checkpointing": false, + "activation_checkpoint_interval": 1, + "activation_checkpoint_scope": "block", + "ddp_static_graph": false, + "ddp_gradient_as_bucket_view": true, + "blocking_data_transfer": false, + "dataloader_prefetch_factor": 2, + "full_train_stats": false, + "tokenized_hf": false, + "tokenized_pad_token": "pad", + "elf_conditional_hf": false, + "record_pad_truncate": false, + "record_add_eos": false, + "record_add_special_tokens": false, + "record_pad_token": "pad", + "record_shuffle_buffer": 10000, + "wrap": true, + "wrap_mode": "stream", + "wrap_record_buffer_size": 200, + "owt_cached_chunks": false, + "owt_chunk_cache_dir": "", + "owt_chunk_cache_rebuild": false, + "owt_chunk_cache_write_batch": 4096, + "owt_exact_repeat_per_chunk": 0, + "online_chunk_shuffle": false, + "online_chunk_shuffle_buffer": 10000, + "openwebtext_split": "train_minus_100k", + "detokenizer": "auto", + "resolved_detokenizer": null, + "num_workers": 0, + "latest_every": 1000, + "resume_path": "" +} +step=100 micro_steps=200 elapsed=27.9s lr=1.212000e-05 loss=10.1894 loss_recon=10.1894 loss_meanflow=0.0000 mean_model_t=0.5027 mean_corrupt_t=0.5027 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.5095 corrupt_frac=0.5502 acc_corrupt=0.3348 loss_corrupt=10.1894 wrong_frac=0.4970 init_acc_corrupt=0.4695 acc_corrupt_t_0p0_0p2=0.0430 corrupt_frac_t_0p0_0p2=0.1946 acc_corrupt_t_0p2_0p4=0.1796 corrupt_frac_t_0p2_0p4=0.2020 acc_corrupt_t_0p4_0p6=0.3307 corrupt_frac_t_0p4_0p6=0.2000 acc_corrupt_t_0p6_0p8=0.4709 corrupt_frac_t_0p6_0p8=0.2013 acc_corrupt_t_0p8_1p0=0.6390 corrupt_frac_t_0p8_1p0=0.2022 out_w_norm=0.7980 out_g_norm=1.3291 loss_all=9.8176 init_gold_top10=0.4959 init_gold_top100=0.5019 +step=200 micro_steps=400 elapsed=26.2s lr=2.412000e-05 loss=9.0439 loss_recon=9.0439 loss_meanflow=0.0000 mean_model_t=0.4997 mean_corrupt_t=0.4997 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.0702 corrupt_frac=0.5498 acc_corrupt=0.0568 loss_corrupt=9.0439 wrong_frac=0.5017 init_acc_corrupt=0.4631 acc_corrupt_t_0p0_0p2=0.0439 corrupt_frac_t_0p0_0p2=0.1972 acc_corrupt_t_0p2_0p4=0.0446 corrupt_frac_t_0p2_0p4=0.2062 acc_corrupt_t_0p4_0p6=0.0509 corrupt_frac_t_0p4_0p6=0.1989 acc_corrupt_t_0p6_0p8=0.0652 corrupt_frac_t_0p6_0p8=0.2031 acc_corrupt_t_0p8_1p0=0.0802 corrupt_frac_t_0p8_1p0=0.1946 out_w_norm=5.7669 out_g_norm=2.0557 loss_all=8.4468 init_gold_top10=0.4659 init_gold_top100=0.4721 +step=300 micro_steps=600 elapsed=26.3s lr=3.612000e-05 loss=7.3528 loss_recon=7.3528 loss_meanflow=0.0000 mean_model_t=0.5001 mean_corrupt_t=0.5001 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.0974 corrupt_frac=0.5514 acc_corrupt=0.0778 loss_corrupt=7.3528 wrong_frac=0.5013 init_acc_corrupt=0.4640 acc_corrupt_t_0p0_0p2=0.0446 corrupt_frac_t_0p0_0p2=0.2019 acc_corrupt_t_0p2_0p4=0.0583 corrupt_frac_t_0p2_0p4=0.2027 acc_corrupt_t_0p4_0p6=0.0787 corrupt_frac_t_0p4_0p6=0.1970 acc_corrupt_t_0p6_0p8=0.0961 corrupt_frac_t_0p6_0p8=0.1973 acc_corrupt_t_0p8_1p0=0.1121 corrupt_frac_t_0p8_1p0=0.2011 out_w_norm=10.9492 out_g_norm=1.7034 loss_all=6.4804 init_gold_top10=0.4904 init_gold_top100=0.4971 +step=400 micro_steps=800 elapsed=26.0s lr=4.812000e-05 loss=5.4356 loss_recon=5.4356 loss_meanflow=0.0000 mean_model_t=0.5018 mean_corrupt_t=0.5018 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.4468 corrupt_frac=0.5493 acc_corrupt=0.3110 loss_corrupt=5.4356 wrong_frac=0.4987 init_acc_corrupt=0.4668 acc_corrupt_t_0p0_0p2=0.0606 corrupt_frac_t_0p0_0p2=0.1986 acc_corrupt_t_0p2_0p4=0.1763 corrupt_frac_t_0p2_0p4=0.1984 acc_corrupt_t_0p4_0p6=0.3117 corrupt_frac_t_0p4_0p6=0.2021 acc_corrupt_t_0p6_0p8=0.4427 corrupt_frac_t_0p6_0p8=0.1987 acc_corrupt_t_0p8_1p0=0.5592 corrupt_frac_t_0p8_1p0=0.2022 out_w_norm=14.5533 out_g_norm=0.9948 loss_all=2.9261 init_gold_top10=0.4184 init_gold_top100=0.4258 +step=500 micro_steps=1000 elapsed=26.2s lr=6.012000e-05 loss=4.1952 loss_recon=4.1952 loss_meanflow=0.0000 mean_model_t=0.4985 mean_corrupt_t=0.4985 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.6710 corrupt_frac=0.5479 acc_corrupt=0.4653 loss_corrupt=4.1952 wrong_frac=0.5030 init_acc_corrupt=0.4620 acc_corrupt_t_0p0_0p2=0.0811 corrupt_frac_t_0p0_0p2=0.2024 acc_corrupt_t_0p2_0p4=0.2622 corrupt_frac_t_0p2_0p4=0.2050 acc_corrupt_t_0p4_0p6=0.4820 corrupt_frac_t_0p4_0p6=0.1918 acc_corrupt_t_0p6_0p8=0.6662 corrupt_frac_t_0p6_0p8=0.2025 acc_corrupt_t_0p8_1p0=0.8461 corrupt_frac_t_0p8_1p0=0.1983 out_w_norm=17.7456 out_g_norm=1.1356 loss_all=2.5941 init_gold_top10=0.4624 init_gold_top100=0.4679 +step=600 micro_steps=1200 elapsed=26.1s lr=7.212000e-05 loss=3.7614 loss_recon=3.7614 loss_meanflow=0.0000 mean_model_t=0.5040 mean_corrupt_t=0.5040 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7141 corrupt_frac=0.5480 acc_corrupt=0.5094 loss_corrupt=3.7614 wrong_frac=0.4954 init_acc_corrupt=0.4705 acc_corrupt_t_0p0_0p2=0.1100 corrupt_frac_t_0p0_0p2=0.1969 acc_corrupt_t_0p2_0p4=0.2950 corrupt_frac_t_0p2_0p4=0.1962 acc_corrupt_t_0p4_0p6=0.5283 corrupt_frac_t_0p4_0p6=0.2033 acc_corrupt_t_0p6_0p8=0.7076 corrupt_frac_t_0p6_0p8=0.1974 acc_corrupt_t_0p8_1p0=0.8861 corrupt_frac_t_0p8_1p0=0.2063 out_w_norm=19.4983 out_g_norm=1.2365 loss_all=2.4266 init_gold_top10=0.4421 init_gold_top100=0.4477 +step=700 micro_steps=1400 elapsed=26.0s lr=8.412000e-05 loss=3.6560 loss_recon=3.6560 loss_meanflow=0.0000 mean_model_t=0.4971 mean_corrupt_t=0.4971 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7201 corrupt_frac=0.5493 acc_corrupt=0.5139 loss_corrupt=3.6560 wrong_frac=0.5021 init_acc_corrupt=0.4630 acc_corrupt_t_0p0_0p2=0.1196 corrupt_frac_t_0p0_0p2=0.2027 acc_corrupt_t_0p2_0p4=0.3038 corrupt_frac_t_0p2_0p4=0.2006 acc_corrupt_t_0p4_0p6=0.5393 corrupt_frac_t_0p4_0p6=0.1985 acc_corrupt_t_0p6_0p8=0.7181 corrupt_frac_t_0p6_0p8=0.2000 acc_corrupt_t_0p8_1p0=0.8982 corrupt_frac_t_0p8_1p0=0.1982 out_w_norm=20.6247 out_g_norm=1.3302 loss_all=2.0692 init_gold_top10=0.4858 init_gold_top100=0.4913 +step=800 micro_steps=1600 elapsed=26.2s lr=9.612000e-05 loss=3.5647 loss_recon=3.5647 loss_meanflow=0.0000 mean_model_t=0.4964 mean_corrupt_t=0.4964 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7244 corrupt_frac=0.5482 acc_corrupt=0.5196 loss_corrupt=3.5647 wrong_frac=0.5055 init_acc_corrupt=0.4593 acc_corrupt_t_0p0_0p2=0.1277 corrupt_frac_t_0p0_0p2=0.2099 acc_corrupt_t_0p2_0p4=0.3143 corrupt_frac_t_0p2_0p4=0.1992 acc_corrupt_t_0p4_0p6=0.5501 corrupt_frac_t_0p4_0p6=0.1915 acc_corrupt_t_0p6_0p8=0.7274 corrupt_frac_t_0p6_0p8=0.2040 acc_corrupt_t_0p8_1p0=0.9031 corrupt_frac_t_0p8_1p0=0.1954 out_w_norm=21.6470 out_g_norm=1.2451 loss_all=1.5754 init_gold_top10=0.5482 init_gold_top100=0.5541 +step=900 micro_steps=1800 elapsed=26.0s lr=1.081200e-04 loss=3.4854 loss_recon=3.4854 loss_meanflow=0.0000 mean_model_t=0.4998 mean_corrupt_t=0.4998 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7267 corrupt_frac=0.5508 acc_corrupt=0.5268 loss_corrupt=3.4854 wrong_frac=0.5019 init_acc_corrupt=0.4639 acc_corrupt_t_0p0_0p2=0.1318 corrupt_frac_t_0p0_0p2=0.2006 acc_corrupt_t_0p2_0p4=0.3217 corrupt_frac_t_0p2_0p4=0.1993 acc_corrupt_t_0p4_0p6=0.5526 corrupt_frac_t_0p4_0p6=0.2042 acc_corrupt_t_0p6_0p8=0.7327 corrupt_frac_t_0p6_0p8=0.2012 acc_corrupt_t_0p8_1p0=0.9041 corrupt_frac_t_0p8_1p0=0.1947 out_w_norm=22.6073 out_g_norm=1.2714 loss_all=1.8967 init_gold_top10=0.5364 init_gold_top100=0.5399 +step=1000 micro_steps=2000 elapsed=26.1s lr=1.201200e-04 loss=3.4234 loss_recon=3.4234 loss_meanflow=0.0000 mean_model_t=0.5014 mean_corrupt_t=0.5014 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7302 corrupt_frac=0.5471 acc_corrupt=0.5304 loss_corrupt=3.4234 wrong_frac=0.5012 init_acc_corrupt=0.4644 acc_corrupt_t_0p0_0p2=0.1352 corrupt_frac_t_0p0_0p2=0.1999 acc_corrupt_t_0p2_0p4=0.3268 corrupt_frac_t_0p2_0p4=0.2022 acc_corrupt_t_0p4_0p6=0.5556 corrupt_frac_t_0p4_0p6=0.2028 acc_corrupt_t_0p6_0p8=0.7342 corrupt_frac_t_0p6_0p8=0.1971 acc_corrupt_t_0p8_1p0=0.9083 corrupt_frac_t_0p8_1p0=0.1980 out_w_norm=23.4711 out_g_norm=1.3686 loss_all=2.0603 init_gold_top10=0.5096 init_gold_top100=0.5152 +step=1100 micro_steps=2200 elapsed=29.2s lr=1.321200e-04 loss=3.3425 loss_recon=3.3425 loss_meanflow=0.0000 mean_model_t=0.4965 mean_corrupt_t=0.4965 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7310 corrupt_frac=0.5484 acc_corrupt=0.5358 loss_corrupt=3.3425 wrong_frac=0.5011 init_acc_corrupt=0.4645 acc_corrupt_t_0p0_0p2=0.1420 corrupt_frac_t_0p0_0p2=0.2009 acc_corrupt_t_0p2_0p4=0.3309 corrupt_frac_t_0p2_0p4=0.1997 acc_corrupt_t_0p4_0p6=0.5606 corrupt_frac_t_0p4_0p6=0.2036 acc_corrupt_t_0p6_0p8=0.7387 corrupt_frac_t_0p6_0p8=0.1959 acc_corrupt_t_0p8_1p0=0.9121 corrupt_frac_t_0p8_1p0=0.1999 out_w_norm=24.4231 out_g_norm=1.2185 loss_all=1.8322 init_gold_top10=0.5140 init_gold_top100=0.5221 +step=1200 micro_steps=2400 elapsed=48.7s lr=1.441200e-04 loss=3.3217 loss_recon=3.3217 loss_meanflow=0.0000 mean_model_t=0.4985 mean_corrupt_t=0.4985 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7318 corrupt_frac=0.5498 acc_corrupt=0.5383 loss_corrupt=3.3217 wrong_frac=0.5014 init_acc_corrupt=0.4645 acc_corrupt_t_0p0_0p2=0.1439 corrupt_frac_t_0p0_0p2=0.1983 acc_corrupt_t_0p2_0p4=0.3324 corrupt_frac_t_0p2_0p4=0.2015 acc_corrupt_t_0p4_0p6=0.5635 corrupt_frac_t_0p4_0p6=0.2040 acc_corrupt_t_0p6_0p8=0.7423 corrupt_frac_t_0p6_0p8=0.1980 acc_corrupt_t_0p8_1p0=0.9126 corrupt_frac_t_0p8_1p0=0.1982 out_w_norm=25.4662 out_g_norm=1.2274 loss_all=1.8508 init_gold_top10=0.5417 init_gold_top100=0.5468 +step=1300 micro_steps=2600 elapsed=54.0s lr=1.561200e-04 loss=3.2811 loss_recon=3.2811 loss_meanflow=0.0000 mean_model_t=0.4995 mean_corrupt_t=0.4995 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7333 corrupt_frac=0.5477 acc_corrupt=0.5418 loss_corrupt=3.2811 wrong_frac=0.4991 init_acc_corrupt=0.4655 acc_corrupt_t_0p0_0p2=0.1513 corrupt_frac_t_0p0_0p2=0.1968 acc_corrupt_t_0p2_0p4=0.3365 corrupt_frac_t_0p2_0p4=0.2057 acc_corrupt_t_0p4_0p6=0.5669 corrupt_frac_t_0p4_0p6=0.1979 acc_corrupt_t_0p6_0p8=0.7433 corrupt_frac_t_0p6_0p8=0.1993 acc_corrupt_t_0p8_1p0=0.9112 corrupt_frac_t_0p8_1p0=0.2002 out_w_norm=26.6149 out_g_norm=1.2098 loss_all=1.6774 init_gold_top10=0.5032 init_gold_top100=0.5111 +step=1400 micro_steps=2800 elapsed=54.1s lr=1.681200e-04 loss=3.2043 loss_recon=3.2043 loss_meanflow=0.0000 mean_model_t=0.5036 mean_corrupt_t=0.5036 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7349 corrupt_frac=0.5531 acc_corrupt=0.5494 loss_corrupt=3.2043 wrong_frac=0.4962 init_acc_corrupt=0.4692 acc_corrupt_t_0p0_0p2=0.1516 corrupt_frac_t_0p0_0p2=0.2006 acc_corrupt_t_0p2_0p4=0.3455 corrupt_frac_t_0p2_0p4=0.1970 acc_corrupt_t_0p4_0p6=0.5709 corrupt_frac_t_0p4_0p6=0.1945 acc_corrupt_t_0p6_0p8=0.7480 corrupt_frac_t_0p6_0p8=0.1975 acc_corrupt_t_0p8_1p0=0.9133 corrupt_frac_t_0p8_1p0=0.2104 out_w_norm=27.9984 out_g_norm=1.1194 loss_all=3.9955 init_gold_top10=0.4396 init_gold_top100=0.4481 +step=1500 micro_steps=3000 elapsed=53.8s lr=1.801200e-04 loss=3.2401 loss_recon=3.2401 loss_meanflow=0.0000 mean_model_t=0.4988 mean_corrupt_t=0.4988 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7295 corrupt_frac=0.5546 acc_corrupt=0.5420 loss_corrupt=3.2401 wrong_frac=0.5035 init_acc_corrupt=0.4611 acc_corrupt_t_0p0_0p2=0.1519 corrupt_frac_t_0p0_0p2=0.1992 acc_corrupt_t_0p2_0p4=0.3425 corrupt_frac_t_0p2_0p4=0.2057 acc_corrupt_t_0p4_0p6=0.5707 corrupt_frac_t_0p4_0p6=0.2032 acc_corrupt_t_0p6_0p8=0.7471 corrupt_frac_t_0p6_0p8=0.1960 acc_corrupt_t_0p8_1p0=0.9134 corrupt_frac_t_0p8_1p0=0.1959 out_w_norm=29.4380 out_g_norm=1.1009 loss_all=1.4288 init_gold_top10=0.4928 init_gold_top100=0.4986 +step=1600 micro_steps=3200 elapsed=53.9s lr=1.921200e-04 loss=3.2119 loss_recon=3.2119 loss_meanflow=0.0000 mean_model_t=0.4985 mean_corrupt_t=0.4985 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7335 corrupt_frac=0.5496 acc_corrupt=0.5472 loss_corrupt=3.2119 wrong_frac=0.5008 init_acc_corrupt=0.4643 acc_corrupt_t_0p0_0p2=0.1565 corrupt_frac_t_0p0_0p2=0.2044 acc_corrupt_t_0p2_0p4=0.3460 corrupt_frac_t_0p2_0p4=0.1971 acc_corrupt_t_0p4_0p6=0.5709 corrupt_frac_t_0p4_0p6=0.1976 acc_corrupt_t_0p6_0p8=0.7498 corrupt_frac_t_0p6_0p8=0.1971 acc_corrupt_t_0p8_1p0=0.9147 corrupt_frac_t_0p8_1p0=0.2038 out_w_norm=31.0092 out_g_norm=0.9983 loss_all=1.7192 init_gold_top10=0.4415 init_gold_top100=0.4484 +step=1700 micro_steps=3400 elapsed=53.8s lr=2.041200e-04 loss=3.1607 loss_recon=3.1607 loss_meanflow=0.0000 mean_model_t=0.4986 mean_corrupt_t=0.4986 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7359 corrupt_frac=0.5486 acc_corrupt=0.5500 loss_corrupt=3.1607 wrong_frac=0.5012 init_acc_corrupt=0.4640 acc_corrupt_t_0p0_0p2=0.1616 corrupt_frac_t_0p0_0p2=0.1974 acc_corrupt_t_0p2_0p4=0.3490 corrupt_frac_t_0p2_0p4=0.2045 acc_corrupt_t_0p4_0p6=0.5760 corrupt_frac_t_0p4_0p6=0.2005 acc_corrupt_t_0p6_0p8=0.7517 corrupt_frac_t_0p6_0p8=0.2015 acc_corrupt_t_0p8_1p0=0.9168 corrupt_frac_t_0p8_1p0=0.1960 out_w_norm=32.8361 out_g_norm=0.9289 loss_all=2.1389 init_gold_top10=0.4580 init_gold_top100=0.4660 +step=1800 micro_steps=3600 elapsed=53.6s lr=2.161200e-04 loss=3.1017 loss_recon=3.1017 loss_meanflow=0.0000 mean_model_t=0.5037 mean_corrupt_t=0.5037 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7388 corrupt_frac=0.5490 acc_corrupt=0.5575 loss_corrupt=3.1017 wrong_frac=0.4951 init_acc_corrupt=0.4714 acc_corrupt_t_0p0_0p2=0.1633 corrupt_frac_t_0p0_0p2=0.1896 acc_corrupt_t_0p2_0p4=0.3501 corrupt_frac_t_0p2_0p4=0.2002 acc_corrupt_t_0p4_0p6=0.5770 corrupt_frac_t_0p4_0p6=0.2027 acc_corrupt_t_0p6_0p8=0.7526 corrupt_frac_t_0p6_0p8=0.2063 acc_corrupt_t_0p8_1p0=0.9155 corrupt_frac_t_0p8_1p0=0.2013 out_w_norm=34.8032 out_g_norm=0.8999 loss_all=1.6906 init_gold_top10=0.5238 init_gold_top100=0.5278 +step=1900 micro_steps=3800 elapsed=53.7s lr=2.281200e-04 loss=3.0891 loss_recon=3.0891 loss_meanflow=0.0000 mean_model_t=0.5000 mean_corrupt_t=0.5000 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7371 corrupt_frac=0.5502 acc_corrupt=0.5555 loss_corrupt=3.0891 wrong_frac=0.4995 init_acc_corrupt=0.4660 acc_corrupt_t_0p0_0p2=0.1646 corrupt_frac_t_0p0_0p2=0.2033 acc_corrupt_t_0p2_0p4=0.3580 corrupt_frac_t_0p2_0p4=0.1979 acc_corrupt_t_0p4_0p6=0.5824 corrupt_frac_t_0p4_0p6=0.1957 acc_corrupt_t_0p6_0p8=0.7560 corrupt_frac_t_0p6_0p8=0.2020 acc_corrupt_t_0p8_1p0=0.9174 corrupt_frac_t_0p8_1p0=0.2011 out_w_norm=36.9222 out_g_norm=0.8265 loss_all=1.8573 init_gold_top10=0.4884 init_gold_top100=0.4953 +step=2000 micro_steps=4000 elapsed=53.6s lr=2.401200e-04 loss=3.0672 loss_recon=3.0672 loss_meanflow=0.0000 mean_model_t=0.5005 mean_corrupt_t=0.5005 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7370 corrupt_frac=0.5511 acc_corrupt=0.5570 loss_corrupt=3.0672 wrong_frac=0.5001 init_acc_corrupt=0.4655 acc_corrupt_t_0p0_0p2=0.1679 corrupt_frac_t_0p0_0p2=0.2005 acc_corrupt_t_0p2_0p4=0.3623 corrupt_frac_t_0p2_0p4=0.1994 acc_corrupt_t_0p4_0p6=0.5851 corrupt_frac_t_0p4_0p6=0.2021 acc_corrupt_t_0p6_0p8=0.7555 corrupt_frac_t_0p6_0p8=0.1989 acc_corrupt_t_0p8_1p0=0.9168 corrupt_frac_t_0p8_1p0=0.1991 out_w_norm=39.2313 out_g_norm=0.8197 loss_all=1.9690 init_gold_top10=0.4329 init_gold_top100=0.4414 +step=2100 micro_steps=4200 elapsed=68.7s lr=2.521200e-04 loss=3.0611 loss_recon=3.0611 loss_meanflow=0.0000 mean_model_t=0.4980 mean_corrupt_t=0.4980 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7379 corrupt_frac=0.5491 acc_corrupt=0.5571 loss_corrupt=3.0611 wrong_frac=0.5032 init_acc_corrupt=0.4622 acc_corrupt_t_0p0_0p2=0.1710 corrupt_frac_t_0p0_0p2=0.2042 acc_corrupt_t_0p2_0p4=0.3637 corrupt_frac_t_0p2_0p4=0.1972 acc_corrupt_t_0p4_0p6=0.5893 corrupt_frac_t_0p4_0p6=0.2018 acc_corrupt_t_0p6_0p8=0.7568 corrupt_frac_t_0p6_0p8=0.2030 acc_corrupt_t_0p8_1p0=0.9182 corrupt_frac_t_0p8_1p0=0.1937 out_w_norm=41.5586 out_g_norm=0.7339 loss_all=1.8915 init_gold_top10=0.4788 init_gold_top100=0.4841 +step=2200 micro_steps=4400 elapsed=32.9s lr=2.641200e-04 loss=3.0688 loss_recon=3.0688 loss_meanflow=0.0000 mean_model_t=0.4967 mean_corrupt_t=0.4967 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7366 corrupt_frac=0.5505 acc_corrupt=0.5559 loss_corrupt=3.0688 wrong_frac=0.5043 init_acc_corrupt=0.4601 acc_corrupt_t_0p0_0p2=0.1715 corrupt_frac_t_0p0_0p2=0.2001 acc_corrupt_t_0p2_0p4=0.3639 corrupt_frac_t_0p2_0p4=0.2087 acc_corrupt_t_0p4_0p6=0.5873 corrupt_frac_t_0p4_0p6=0.1955 acc_corrupt_t_0p6_0p8=0.7596 corrupt_frac_t_0p6_0p8=0.2023 acc_corrupt_t_0p8_1p0=0.9160 corrupt_frac_t_0p8_1p0=0.1934 out_w_norm=43.9405 out_g_norm=0.7025 loss_all=1.7571 init_gold_top10=0.5469 init_gold_top100=0.5521 +step=2300 micro_steps=4600 elapsed=26.1s lr=2.761200e-04 loss=3.0113 loss_recon=3.0113 loss_meanflow=0.0000 mean_model_t=0.5001 mean_corrupt_t=0.5001 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7390 corrupt_frac=0.5526 acc_corrupt=0.5627 loss_corrupt=3.0113 wrong_frac=0.4991 init_acc_corrupt=0.4662 acc_corrupt_t_0p0_0p2=0.1739 corrupt_frac_t_0p0_0p2=0.2003 acc_corrupt_t_0p2_0p4=0.3709 corrupt_frac_t_0p2_0p4=0.1975 acc_corrupt_t_0p4_0p6=0.5895 corrupt_frac_t_0p4_0p6=0.2009 acc_corrupt_t_0p6_0p8=0.7569 corrupt_frac_t_0p6_0p8=0.2024 acc_corrupt_t_0p8_1p0=0.9198 corrupt_frac_t_0p8_1p0=0.1989 out_w_norm=46.5383 out_g_norm=0.6520 loss_all=1.9084 init_gold_top10=0.4996 init_gold_top100=0.5072 +step=2400 micro_steps=4800 elapsed=35.1s lr=2.881200e-04 loss=2.9773 loss_recon=2.9773 loss_meanflow=0.0000 mean_model_t=0.5003 mean_corrupt_t=0.5003 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7410 corrupt_frac=0.5516 acc_corrupt=0.5646 loss_corrupt=2.9773 wrong_frac=0.5006 init_acc_corrupt=0.4653 acc_corrupt_t_0p0_0p2=0.1763 corrupt_frac_t_0p0_0p2=0.1999 acc_corrupt_t_0p2_0p4=0.3738 corrupt_frac_t_0p2_0p4=0.2024 acc_corrupt_t_0p4_0p6=0.5939 corrupt_frac_t_0p4_0p6=0.2011 acc_corrupt_t_0p6_0p8=0.7622 corrupt_frac_t_0p6_0p8=0.1956 acc_corrupt_t_0p8_1p0=0.9215 corrupt_frac_t_0p8_1p0=0.2009 out_w_norm=49.1546 out_g_norm=0.6275 loss_all=1.6140 init_gold_top10=0.4741 init_gold_top100=0.4807 +step=2500 micro_steps=5000 elapsed=54.5s lr=3.000000e-04 loss=2.9745 loss_recon=2.9745 loss_meanflow=0.0000 mean_model_t=0.4989 mean_corrupt_t=0.4989 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7435 corrupt_frac=0.5470 acc_corrupt=0.5658 loss_corrupt=2.9745 wrong_frac=0.5022 init_acc_corrupt=0.4627 acc_corrupt_t_0p0_0p2=0.1830 corrupt_frac_t_0p0_0p2=0.1996 acc_corrupt_t_0p2_0p4=0.3785 corrupt_frac_t_0p2_0p4=0.2072 acc_corrupt_t_0p4_0p6=0.5951 corrupt_frac_t_0p4_0p6=0.1991 acc_corrupt_t_0p6_0p8=0.7659 corrupt_frac_t_0p6_0p8=0.1948 acc_corrupt_t_0p8_1p0=0.9190 corrupt_frac_t_0p8_1p0=0.1993 out_w_norm=51.5968 out_g_norm=0.6205 loss_all=1.7186 init_gold_top10=0.4997 init_gold_top100=0.5039 +step=2600 micro_steps=5200 elapsed=54.4s lr=3.000000e-04 loss=2.9515 loss_recon=2.9515 loss_meanflow=0.0000 mean_model_t=0.4977 mean_corrupt_t=0.4977 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7435 corrupt_frac=0.5495 acc_corrupt=0.5670 loss_corrupt=2.9515 wrong_frac=0.5005 init_acc_corrupt=0.4645 acc_corrupt_t_0p0_0p2=0.1789 corrupt_frac_t_0p0_0p2=0.1995 acc_corrupt_t_0p2_0p4=0.3821 corrupt_frac_t_0p2_0p4=0.2020 acc_corrupt_t_0p4_0p6=0.5948 corrupt_frac_t_0p4_0p6=0.1997 acc_corrupt_t_0p6_0p8=0.7629 corrupt_frac_t_0p6_0p8=0.2024 acc_corrupt_t_0p8_1p0=0.9212 corrupt_frac_t_0p8_1p0=0.1964 out_w_norm=53.8608 out_g_norm=0.5627 loss_all=1.6114 init_gold_top10=0.5160 init_gold_top100=0.5224 +step=2700 micro_steps=5400 elapsed=54.5s lr=3.000000e-04 loss=2.9728 loss_recon=2.9728 loss_meanflow=0.0000 mean_model_t=0.4961 mean_corrupt_t=0.4961 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7433 corrupt_frac=0.5511 acc_corrupt=0.5675 loss_corrupt=2.9728 wrong_frac=0.5044 init_acc_corrupt=0.4602 acc_corrupt_t_0p0_0p2=0.1867 corrupt_frac_t_0p0_0p2=0.2106 acc_corrupt_t_0p2_0p4=0.3867 corrupt_frac_t_0p2_0p4=0.1997 acc_corrupt_t_0p4_0p6=0.6037 corrupt_frac_t_0p4_0p6=0.1973 acc_corrupt_t_0p6_0p8=0.7684 corrupt_frac_t_0p6_0p8=0.1969 acc_corrupt_t_0p8_1p0=0.9238 corrupt_frac_t_0p8_1p0=0.1954 out_w_norm=55.9954 out_g_norm=0.5629 loss_all=1.8268 init_gold_top10=0.4990 init_gold_top100=0.5059 +step=2800 micro_steps=5600 elapsed=54.5s lr=3.000000e-04 loss=2.9480 loss_recon=2.9480 loss_meanflow=0.0000 mean_model_t=0.4970 mean_corrupt_t=0.4970 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7439 corrupt_frac=0.5487 acc_corrupt=0.5658 loss_corrupt=2.9480 wrong_frac=0.5039 init_acc_corrupt=0.4612 acc_corrupt_t_0p0_0p2=0.1815 corrupt_frac_t_0p0_0p2=0.2045 acc_corrupt_t_0p2_0p4=0.3757 corrupt_frac_t_0p2_0p4=0.1969 acc_corrupt_t_0p4_0p6=0.5983 corrupt_frac_t_0p4_0p6=0.2031 acc_corrupt_t_0p6_0p8=0.7668 corrupt_frac_t_0p6_0p8=0.2052 acc_corrupt_t_0p8_1p0=0.9239 corrupt_frac_t_0p8_1p0=0.1904 out_w_norm=57.9976 out_g_norm=0.5528 loss_all=1.5330 init_gold_top10=0.4832 init_gold_top100=0.4885 +step=2900 micro_steps=5800 elapsed=88.8s lr=3.000000e-04 loss=2.8851 loss_recon=2.8851 loss_meanflow=0.0000 mean_model_t=0.4982 mean_corrupt_t=0.4982 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7483 corrupt_frac=0.5498 acc_corrupt=0.5736 loss_corrupt=2.8851 wrong_frac=0.5004 init_acc_corrupt=0.4652 acc_corrupt_t_0p0_0p2=0.1864 corrupt_frac_t_0p0_0p2=0.1997 acc_corrupt_t_0p2_0p4=0.3812 corrupt_frac_t_0p2_0p4=0.1975 acc_corrupt_t_0p4_0p6=0.6019 corrupt_frac_t_0p4_0p6=0.2033 acc_corrupt_t_0p6_0p8=0.7706 corrupt_frac_t_0p6_0p8=0.1986 acc_corrupt_t_0p8_1p0=0.9240 corrupt_frac_t_0p8_1p0=0.2009 out_w_norm=59.8185 out_g_norm=0.5086 loss_all=1.7147 init_gold_top10=0.5530 init_gold_top100=0.5568 +step=3000 micro_steps=6000 elapsed=142.4s lr=3.000000e-04 loss=2.8396 loss_recon=2.8396 loss_meanflow=0.0000 mean_model_t=0.5022 mean_corrupt_t=0.5022 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7521 corrupt_frac=0.5490 acc_corrupt=0.5786 loss_corrupt=2.8396 wrong_frac=0.4958 init_acc_corrupt=0.4699 acc_corrupt_t_0p0_0p2=0.1883 corrupt_frac_t_0p0_0p2=0.1995 acc_corrupt_t_0p2_0p4=0.3837 corrupt_frac_t_0p2_0p4=0.1960 acc_corrupt_t_0p4_0p6=0.6078 corrupt_frac_t_0p4_0p6=0.2003 acc_corrupt_t_0p6_0p8=0.7717 corrupt_frac_t_0p6_0p8=0.1961 acc_corrupt_t_0p8_1p0=0.9262 corrupt_frac_t_0p8_1p0=0.2081 out_w_norm=61.4983 out_g_norm=0.4789 loss_all=1.5678 init_gold_top10=0.4593 init_gold_top100=0.4673 +step=3100 micro_steps=6200 elapsed=143.4s lr=3.000000e-04 loss=2.8451 loss_recon=2.8451 loss_meanflow=0.0000 mean_model_t=0.5002 mean_corrupt_t=0.5002 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7514 corrupt_frac=0.5499 acc_corrupt=0.5766 loss_corrupt=2.8451 wrong_frac=0.4998 init_acc_corrupt=0.4660 acc_corrupt_t_0p0_0p2=0.1879 corrupt_frac_t_0p0_0p2=0.2049 acc_corrupt_t_0p2_0p4=0.3894 corrupt_frac_t_0p2_0p4=0.1938 acc_corrupt_t_0p4_0p6=0.6065 corrupt_frac_t_0p4_0p6=0.1965 acc_corrupt_t_0p6_0p8=0.7732 corrupt_frac_t_0p6_0p8=0.2048 acc_corrupt_t_0p8_1p0=0.9256 corrupt_frac_t_0p8_1p0=0.2000 out_w_norm=63.1038 out_g_norm=0.4776 loss_all=1.5394 init_gold_top10=0.4952 init_gold_top100=0.5001 +step=3200 micro_steps=6400 elapsed=106.0s lr=3.000000e-04 loss=2.8674 loss_recon=2.8674 loss_meanflow=0.0000 mean_model_t=0.4972 mean_corrupt_t=0.4972 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7503 corrupt_frac=0.5497 acc_corrupt=0.5746 loss_corrupt=2.8674 wrong_frac=0.5027 init_acc_corrupt=0.4632 acc_corrupt_t_0p0_0p2=0.1882 corrupt_frac_t_0p0_0p2=0.2000 acc_corrupt_t_0p2_0p4=0.3906 corrupt_frac_t_0p2_0p4=0.2021 acc_corrupt_t_0p4_0p6=0.6060 corrupt_frac_t_0p4_0p6=0.1996 acc_corrupt_t_0p6_0p8=0.7722 corrupt_frac_t_0p6_0p8=0.2044 acc_corrupt_t_0p8_1p0=0.9241 corrupt_frac_t_0p8_1p0=0.1939 out_w_norm=64.6166 out_g_norm=0.4943 loss_all=1.8347 init_gold_top10=0.4020 init_gold_top100=0.4100 +step=3300 micro_steps=6600 elapsed=133.2s lr=3.000000e-04 loss=2.8554 loss_recon=2.8554 loss_meanflow=0.0000 mean_model_t=0.4960 mean_corrupt_t=0.4960 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7513 corrupt_frac=0.5503 acc_corrupt=0.5748 loss_corrupt=2.8554 wrong_frac=0.5043 init_acc_corrupt=0.4605 acc_corrupt_t_0p0_0p2=0.1915 corrupt_frac_t_0p0_0p2=0.2030 acc_corrupt_t_0p2_0p4=0.3921 corrupt_frac_t_0p2_0p4=0.1994 acc_corrupt_t_0p4_0p6=0.6073 corrupt_frac_t_0p4_0p6=0.2046 acc_corrupt_t_0p6_0p8=0.7741 corrupt_frac_t_0p6_0p8=0.2002 acc_corrupt_t_0p8_1p0=0.9260 corrupt_frac_t_0p8_1p0=0.1928 out_w_norm=66.1030 out_g_norm=0.4682 loss_all=1.6975 init_gold_top10=0.4975 init_gold_top100=0.5035 +step=3400 micro_steps=6800 elapsed=141.6s lr=3.000000e-04 loss=2.8104 loss_recon=2.8104 loss_meanflow=0.0000 mean_model_t=0.4976 mean_corrupt_t=0.4976 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7540 corrupt_frac=0.5496 acc_corrupt=0.5795 loss_corrupt=2.8104 wrong_frac=0.5011 init_acc_corrupt=0.4646 acc_corrupt_t_0p0_0p2=0.1931 corrupt_frac_t_0p0_0p2=0.2020 acc_corrupt_t_0p2_0p4=0.3896 corrupt_frac_t_0p2_0p4=0.1979 acc_corrupt_t_0p4_0p6=0.6106 corrupt_frac_t_0p4_0p6=0.2020 acc_corrupt_t_0p6_0p8=0.7777 corrupt_frac_t_0p6_0p8=0.1969 acc_corrupt_t_0p8_1p0=0.9291 corrupt_frac_t_0p8_1p0=0.2012 out_w_norm=67.5430 out_g_norm=0.4392 loss_all=1.8342 init_gold_top10=0.4421 init_gold_top100=0.4481 +step=3500 micro_steps=7000 elapsed=141.5s lr=3.000000e-04 loss=2.7707 loss_recon=2.7707 loss_meanflow=0.0000 mean_model_t=0.5018 mean_corrupt_t=0.5018 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7578 corrupt_frac=0.5515 acc_corrupt=0.5863 loss_corrupt=2.7707 wrong_frac=0.4950 init_acc_corrupt=0.4710 acc_corrupt_t_0p0_0p2=0.1961 corrupt_frac_t_0p0_0p2=0.1957 acc_corrupt_t_0p2_0p4=0.4010 corrupt_frac_t_0p2_0p4=0.1988 acc_corrupt_t_0p4_0p6=0.6110 corrupt_frac_t_0p4_0p6=0.1981 acc_corrupt_t_0p6_0p8=0.7773 corrupt_frac_t_0p6_0p8=0.2032 acc_corrupt_t_0p8_1p0=0.9271 corrupt_frac_t_0p8_1p0=0.2041 out_w_norm=68.8999 out_g_norm=0.4358 loss_all=1.4099 init_gold_top10=0.5393 init_gold_top100=0.5437 +step=3600 micro_steps=7200 elapsed=141.7s lr=3.000000e-04 loss=2.7722 loss_recon=2.7722 loss_meanflow=0.0000 mean_model_t=0.5013 mean_corrupt_t=0.5013 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7570 corrupt_frac=0.5518 acc_corrupt=0.5835 loss_corrupt=2.7722 wrong_frac=0.5004 init_acc_corrupt=0.4653 acc_corrupt_t_0p0_0p2=0.1953 corrupt_frac_t_0p0_0p2=0.2016 acc_corrupt_t_0p2_0p4=0.3982 corrupt_frac_t_0p2_0p4=0.1974 acc_corrupt_t_0p4_0p6=0.6148 corrupt_frac_t_0p4_0p6=0.1974 acc_corrupt_t_0p6_0p8=0.7800 corrupt_frac_t_0p6_0p8=0.2035 acc_corrupt_t_0p8_1p0=0.9271 corrupt_frac_t_0p8_1p0=0.2000 out_w_norm=70.2097 out_g_norm=0.4226 loss_all=1.2853 init_gold_top10=0.5373 init_gold_top100=0.5413 +step=3700 micro_steps=7400 elapsed=139.9s lr=3.000000e-04 loss=2.7673 loss_recon=2.7673 loss_meanflow=0.0000 mean_model_t=0.4992 mean_corrupt_t=0.4992 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7586 corrupt_frac=0.5473 acc_corrupt=0.5827 loss_corrupt=2.7673 wrong_frac=0.5016 init_acc_corrupt=0.4635 acc_corrupt_t_0p0_0p2=0.1960 corrupt_frac_t_0p0_0p2=0.2044 acc_corrupt_t_0p2_0p4=0.3986 corrupt_frac_t_0p2_0p4=0.2003 acc_corrupt_t_0p4_0p6=0.6163 corrupt_frac_t_0p4_0p6=0.1931 acc_corrupt_t_0p6_0p8=0.7808 corrupt_frac_t_0p6_0p8=0.2009 acc_corrupt_t_0p8_1p0=0.9285 corrupt_frac_t_0p8_1p0=0.2013 out_w_norm=71.4773 out_g_norm=0.4186 loss_all=1.0451 init_gold_top10=0.6195 init_gold_top100=0.6235 +step=3800 micro_steps=7600 elapsed=105.5s lr=3.000000e-04 loss=2.7257 loss_recon=2.7257 loss_meanflow=0.0000 mean_model_t=0.5016 mean_corrupt_t=0.5016 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7625 corrupt_frac=0.5472 acc_corrupt=0.5883 loss_corrupt=2.7257 wrong_frac=0.4983 init_acc_corrupt=0.4674 acc_corrupt_t_0p0_0p2=0.2014 corrupt_frac_t_0p0_0p2=0.1956 acc_corrupt_t_0p2_0p4=0.4019 corrupt_frac_t_0p2_0p4=0.1975 acc_corrupt_t_0p4_0p6=0.6169 corrupt_frac_t_0p4_0p6=0.2056 acc_corrupt_t_0p6_0p8=0.7809 corrupt_frac_t_0p6_0p8=0.2038 acc_corrupt_t_0p8_1p0=0.9294 corrupt_frac_t_0p8_1p0=0.1975 out_w_norm=72.7085 out_g_norm=0.4078 loss_all=1.2059 init_gold_top10=0.5569 init_gold_top100=0.5600 +step=3900 micro_steps=7800 elapsed=84.9s lr=3.000000e-04 loss=2.7906 loss_recon=2.7906 loss_meanflow=0.0000 mean_model_t=0.4945 mean_corrupt_t=0.4945 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7575 corrupt_frac=0.5488 acc_corrupt=0.5809 loss_corrupt=2.7906 wrong_frac=0.5053 init_acc_corrupt=0.4592 acc_corrupt_t_0p0_0p2=0.1982 corrupt_frac_t_0p0_0p2=0.2029 acc_corrupt_t_0p2_0p4=0.4018 corrupt_frac_t_0p2_0p4=0.2072 acc_corrupt_t_0p4_0p6=0.6173 corrupt_frac_t_0p4_0p6=0.2037 acc_corrupt_t_0p6_0p8=0.7834 corrupt_frac_t_0p6_0p8=0.1898 acc_corrupt_t_0p8_1p0=0.9315 corrupt_frac_t_0p8_1p0=0.1964 out_w_norm=73.9089 out_g_norm=0.4109 loss_all=1.5959 init_gold_top10=0.4664 init_gold_top100=0.4734 +step=4000 micro_steps=8000 elapsed=84.9s lr=3.000000e-04 loss=2.7181 loss_recon=2.7181 loss_meanflow=0.0000 mean_model_t=0.4993 mean_corrupt_t=0.4993 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7624 corrupt_frac=0.5478 acc_corrupt=0.5888 loss_corrupt=2.7181 wrong_frac=0.4989 init_acc_corrupt=0.4668 acc_corrupt_t_0p0_0p2=0.2026 corrupt_frac_t_0p0_0p2=0.2010 acc_corrupt_t_0p2_0p4=0.4048 corrupt_frac_t_0p2_0p4=0.1995 acc_corrupt_t_0p4_0p6=0.6199 corrupt_frac_t_0p4_0p6=0.1990 acc_corrupt_t_0p6_0p8=0.7861 corrupt_frac_t_0p6_0p8=0.1982 acc_corrupt_t_0p8_1p0=0.9301 corrupt_frac_t_0p8_1p0=0.2022 out_w_norm=75.1357 out_g_norm=0.4040 loss_all=1.7201 init_gold_top10=0.4731 init_gold_top100=0.4773 +step=4100 micro_steps=8200 elapsed=102.2s lr=3.000000e-04 loss=2.7296 loss_recon=2.7296 loss_meanflow=0.0000 mean_model_t=0.5002 mean_corrupt_t=0.5002 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7637 corrupt_frac=0.5463 acc_corrupt=0.5888 loss_corrupt=2.7296 wrong_frac=0.4997 init_acc_corrupt=0.4655 acc_corrupt_t_0p0_0p2=0.2033 corrupt_frac_t_0p0_0p2=0.1992 acc_corrupt_t_0p2_0p4=0.4030 corrupt_frac_t_0p2_0p4=0.1994 acc_corrupt_t_0p4_0p6=0.6188 corrupt_frac_t_0p4_0p6=0.2016 acc_corrupt_t_0p6_0p8=0.7843 corrupt_frac_t_0p6_0p8=0.1976 acc_corrupt_t_0p8_1p0=0.9308 corrupt_frac_t_0p8_1p0=0.2022 out_w_norm=76.3335 out_g_norm=0.3953 loss_all=1.4530 init_gold_top10=0.4691 init_gold_top100=0.4778 +step=4200 micro_steps=8400 elapsed=141.8s lr=3.000000e-04 loss=2.7023 loss_recon=2.7023 loss_meanflow=0.0000 mean_model_t=0.4988 mean_corrupt_t=0.4988 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7640 corrupt_frac=0.5484 acc_corrupt=0.5907 loss_corrupt=2.7023 wrong_frac=0.4995 init_acc_corrupt=0.4662 acc_corrupt_t_0p0_0p2=0.2040 corrupt_frac_t_0p0_0p2=0.2027 acc_corrupt_t_0p2_0p4=0.4075 corrupt_frac_t_0p2_0p4=0.1962 acc_corrupt_t_0p4_0p6=0.6238 corrupt_frac_t_0p4_0p6=0.1981 acc_corrupt_t_0p6_0p8=0.7854 corrupt_frac_t_0p6_0p8=0.1998 acc_corrupt_t_0p8_1p0=0.9300 corrupt_frac_t_0p8_1p0=0.2031 out_w_norm=77.4800 out_g_norm=0.3858 loss_all=1.4264 init_gold_top10=0.5095 init_gold_top100=0.5144 +step=4300 micro_steps=8600 elapsed=141.7s lr=3.000000e-04 loss=2.7109 loss_recon=2.7109 loss_meanflow=0.0000 mean_model_t=0.4992 mean_corrupt_t=0.4992 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7626 corrupt_frac=0.5522 acc_corrupt=0.5901 loss_corrupt=2.7109 wrong_frac=0.5000 init_acc_corrupt=0.4650 acc_corrupt_t_0p0_0p2=0.2036 corrupt_frac_t_0p0_0p2=0.1971 acc_corrupt_t_0p2_0p4=0.4054 corrupt_frac_t_0p2_0p4=0.2078 acc_corrupt_t_0p4_0p6=0.6238 corrupt_frac_t_0p4_0p6=0.1996 acc_corrupt_t_0p6_0p8=0.7902 corrupt_frac_t_0p6_0p8=0.1959 acc_corrupt_t_0p8_1p0=0.9340 corrupt_frac_t_0p8_1p0=0.1996 out_w_norm=78.5911 out_g_norm=0.3783 loss_all=1.7441 init_gold_top10=0.4525 init_gold_top100=0.4581 +step=4400 micro_steps=8800 elapsed=94.2s lr=3.000000e-04 loss=2.7028 loss_recon=2.7028 loss_meanflow=0.0000 mean_model_t=0.4997 mean_corrupt_t=0.4997 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7645 corrupt_frac=0.5490 acc_corrupt=0.5909 loss_corrupt=2.7028 wrong_frac=0.5012 init_acc_corrupt=0.4639 acc_corrupt_t_0p0_0p2=0.2046 corrupt_frac_t_0p0_0p2=0.2000 acc_corrupt_t_0p2_0p4=0.4071 corrupt_frac_t_0p2_0p4=0.2022 acc_corrupt_t_0p4_0p6=0.6263 corrupt_frac_t_0p4_0p6=0.2020 acc_corrupt_t_0p6_0p8=0.7901 corrupt_frac_t_0p6_0p8=0.1955 acc_corrupt_t_0p8_1p0=0.9323 corrupt_frac_t_0p8_1p0=0.2002 out_w_norm=79.7061 out_g_norm=0.3882 loss_all=1.3517 init_gold_top10=0.5281 init_gold_top100=0.5359 +step=4500 micro_steps=9000 elapsed=64.8s lr=3.000000e-04 loss=2.6717 loss_recon=2.6717 loss_meanflow=0.0000 mean_model_t=0.5024 mean_corrupt_t=0.5024 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7658 corrupt_frac=0.5514 acc_corrupt=0.5949 loss_corrupt=2.6717 wrong_frac=0.4975 init_acc_corrupt=0.4685 acc_corrupt_t_0p0_0p2=0.2041 corrupt_frac_t_0p0_0p2=0.1965 acc_corrupt_t_0p2_0p4=0.4082 corrupt_frac_t_0p2_0p4=0.1980 acc_corrupt_t_0p4_0p6=0.6244 corrupt_frac_t_0p4_0p6=0.2015 acc_corrupt_t_0p6_0p8=0.7913 corrupt_frac_t_0p6_0p8=0.2051 acc_corrupt_t_0p8_1p0=0.9344 corrupt_frac_t_0p8_1p0=0.1989 out_w_norm=80.8162 out_g_norm=0.3767 loss_all=1.5768 init_gold_top10=0.5377 init_gold_top100=0.5429 +step=4600 micro_steps=9200 elapsed=77.8s lr=3.000000e-04 loss=2.6563 loss_recon=2.6563 loss_meanflow=0.0000 mean_model_t=0.5031 mean_corrupt_t=0.5031 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7654 corrupt_frac=0.5562 acc_corrupt=0.5966 loss_corrupt=2.6563 wrong_frac=0.4960 init_acc_corrupt=0.4705 acc_corrupt_t_0p0_0p2=0.2051 corrupt_frac_t_0p0_0p2=0.1945 acc_corrupt_t_0p2_0p4=0.4066 corrupt_frac_t_0p2_0p4=0.1977 acc_corrupt_t_0p4_0p6=0.6289 corrupt_frac_t_0p4_0p6=0.2045 acc_corrupt_t_0p6_0p8=0.7922 corrupt_frac_t_0p6_0p8=0.2020 acc_corrupt_t_0p8_1p0=0.9322 corrupt_frac_t_0p8_1p0=0.2013 out_w_norm=81.9024 out_g_norm=0.3625 loss_all=1.4308 init_gold_top10=0.5378 init_gold_top100=0.5414 +step=4700 micro_steps=9400 elapsed=78.0s lr=3.000000e-04 loss=2.6482 loss_recon=2.6482 loss_meanflow=0.0000 mean_model_t=0.5055 mean_corrupt_t=0.5055 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7671 corrupt_frac=0.5514 acc_corrupt=0.5965 loss_corrupt=2.6482 wrong_frac=0.4958 init_acc_corrupt=0.4699 acc_corrupt_t_0p0_0p2=0.2032 corrupt_frac_t_0p0_0p2=0.1988 acc_corrupt_t_0p2_0p4=0.4108 corrupt_frac_t_0p2_0p4=0.2009 acc_corrupt_t_0p4_0p6=0.6271 corrupt_frac_t_0p4_0p6=0.1859 acc_corrupt_t_0p6_0p8=0.7914 corrupt_frac_t_0p6_0p8=0.2078 acc_corrupt_t_0p8_1p0=0.9318 corrupt_frac_t_0p8_1p0=0.2066 out_w_norm=82.9731 out_g_norm=0.3705 loss_all=2.1329 init_gold_top10=0.4231 init_gold_top100=0.4311 +step=4800 micro_steps=9600 elapsed=77.1s lr=3.000000e-04 loss=2.6286 loss_recon=2.6286 loss_meanflow=0.0000 mean_model_t=0.5032 mean_corrupt_t=0.5032 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7688 corrupt_frac=0.5507 acc_corrupt=0.5991 loss_corrupt=2.6286 wrong_frac=0.4950 init_acc_corrupt=0.4706 acc_corrupt_t_0p0_0p2=0.2065 corrupt_frac_t_0p0_0p2=0.1943 acc_corrupt_t_0p2_0p4=0.4122 corrupt_frac_t_0p2_0p4=0.2024 acc_corrupt_t_0p4_0p6=0.6300 corrupt_frac_t_0p4_0p6=0.1920 acc_corrupt_t_0p6_0p8=0.7907 corrupt_frac_t_0p6_0p8=0.2048 acc_corrupt_t_0p8_1p0=0.9330 corrupt_frac_t_0p8_1p0=0.2065 out_w_norm=84.0492 out_g_norm=0.3625 loss_all=1.5810 init_gold_top10=0.4979 init_gold_top100=0.5017 +step=4900 micro_steps=9800 elapsed=77.1s lr=3.000000e-04 loss=2.6254 loss_recon=2.6254 loss_meanflow=0.0000 mean_model_t=0.5045 mean_corrupt_t=0.5045 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7721 corrupt_frac=0.5455 acc_corrupt=0.6001 loss_corrupt=2.6254 wrong_frac=0.4954 init_acc_corrupt=0.4702 acc_corrupt_t_0p0_0p2=0.2115 corrupt_frac_t_0p0_0p2=0.1955 acc_corrupt_t_0p2_0p4=0.4122 corrupt_frac_t_0p2_0p4=0.1960 acc_corrupt_t_0p4_0p6=0.6302 corrupt_frac_t_0p4_0p6=0.2028 acc_corrupt_t_0p6_0p8=0.7929 corrupt_frac_t_0p6_0p8=0.2025 acc_corrupt_t_0p8_1p0=0.9328 corrupt_frac_t_0p8_1p0=0.2033 out_w_norm=85.1279 out_g_norm=0.3655 loss_all=1.4656 init_gold_top10=0.4917 init_gold_top100=0.5032 +step=5000 micro_steps=10000 elapsed=77.1s lr=3.000000e-04 loss=2.6382 loss_recon=2.6382 loss_meanflow=0.0000 mean_model_t=0.5020 mean_corrupt_t=0.5020 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7702 corrupt_frac=0.5474 acc_corrupt=0.5986 loss_corrupt=2.6382 wrong_frac=0.4979 init_acc_corrupt=0.4680 acc_corrupt_t_0p0_0p2=0.2086 corrupt_frac_t_0p0_0p2=0.1988 acc_corrupt_t_0p2_0p4=0.4134 corrupt_frac_t_0p2_0p4=0.1946 acc_corrupt_t_0p4_0p6=0.6311 corrupt_frac_t_0p4_0p6=0.1983 acc_corrupt_t_0p6_0p8=0.7911 corrupt_frac_t_0p6_0p8=0.2110 acc_corrupt_t_0p8_1p0=0.9355 corrupt_frac_t_0p8_1p0=0.1972 out_w_norm=86.1823 out_g_norm=0.3482 loss_all=1.6180 init_gold_top10=0.4480 init_gold_top100=0.4560 +step=5100 micro_steps=10200 elapsed=80.3s lr=3.000000e-04 loss=2.6559 loss_recon=2.6559 loss_meanflow=0.0000 mean_model_t=0.5024 mean_corrupt_t=0.5024 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7684 corrupt_frac=0.5505 acc_corrupt=0.5971 loss_corrupt=2.6559 wrong_frac=0.4981 init_acc_corrupt=0.4673 acc_corrupt_t_0p0_0p2=0.2060 corrupt_frac_t_0p0_0p2=0.2012 acc_corrupt_t_0p2_0p4=0.4109 corrupt_frac_t_0p2_0p4=0.1974 acc_corrupt_t_0p4_0p6=0.6336 corrupt_frac_t_0p4_0p6=0.1938 acc_corrupt_t_0p6_0p8=0.7916 corrupt_frac_t_0p6_0p8=0.2021 acc_corrupt_t_0p8_1p0=0.9333 corrupt_frac_t_0p8_1p0=0.2055 out_w_norm=87.1937 out_g_norm=0.3559 loss_all=1.6976 init_gold_top10=0.4520 init_gold_top100=0.4601 +step=5200 micro_steps=10400 elapsed=77.6s lr=3.000000e-04 loss=2.6487 loss_recon=2.6487 loss_meanflow=0.0000 mean_model_t=0.4991 mean_corrupt_t=0.4991 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7693 corrupt_frac=0.5477 acc_corrupt=0.5971 loss_corrupt=2.6487 wrong_frac=0.4994 init_acc_corrupt=0.4661 acc_corrupt_t_0p0_0p2=0.2103 corrupt_frac_t_0p0_0p2=0.1990 acc_corrupt_t_0p2_0p4=0.4174 corrupt_frac_t_0p2_0p4=0.2019 acc_corrupt_t_0p4_0p6=0.6272 corrupt_frac_t_0p4_0p6=0.1969 acc_corrupt_t_0p6_0p8=0.7947 corrupt_frac_t_0p6_0p8=0.2042 acc_corrupt_t_0p8_1p0=0.9353 corrupt_frac_t_0p8_1p0=0.1981 out_w_norm=88.2098 out_g_norm=0.3481 loss_all=1.4577 init_gold_top10=0.4928 init_gold_top100=0.4997 +step=5300 micro_steps=10600 elapsed=77.8s lr=3.000000e-04 loss=2.6715 loss_recon=2.6715 loss_meanflow=0.0000 mean_model_t=0.4986 mean_corrupt_t=0.4986 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7662 corrupt_frac=0.5540 acc_corrupt=0.5956 loss_corrupt=2.6715 wrong_frac=0.5008 init_acc_corrupt=0.4646 acc_corrupt_t_0p0_0p2=0.2109 corrupt_frac_t_0p0_0p2=0.2009 acc_corrupt_t_0p2_0p4=0.4132 corrupt_frac_t_0p2_0p4=0.2024 acc_corrupt_t_0p4_0p6=0.6311 corrupt_frac_t_0p4_0p6=0.1982 acc_corrupt_t_0p6_0p8=0.7920 corrupt_frac_t_0p6_0p8=0.1980 acc_corrupt_t_0p8_1p0=0.9360 corrupt_frac_t_0p8_1p0=0.2005 out_w_norm=89.2217 out_g_norm=0.3460 loss_all=1.5222 init_gold_top10=0.4999 init_gold_top100=0.5079 +step=5400 micro_steps=10800 elapsed=77.8s lr=3.000000e-04 loss=2.5854 loss_recon=2.5854 loss_meanflow=0.0000 mean_model_t=0.5026 mean_corrupt_t=0.5026 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7734 corrupt_frac=0.5492 acc_corrupt=0.6051 loss_corrupt=2.5854 wrong_frac=0.4958 init_acc_corrupt=0.4703 acc_corrupt_t_0p0_0p2=0.2138 corrupt_frac_t_0p0_0p2=0.1967 acc_corrupt_t_0p2_0p4=0.4266 corrupt_frac_t_0p2_0p4=0.1955 acc_corrupt_t_0p4_0p6=0.6333 corrupt_frac_t_0p4_0p6=0.2011 acc_corrupt_t_0p6_0p8=0.7976 corrupt_frac_t_0p6_0p8=0.2043 acc_corrupt_t_0p8_1p0=0.9355 corrupt_frac_t_0p8_1p0=0.2023 out_w_norm=90.2321 out_g_norm=0.3606 loss_all=1.3602 init_gold_top10=0.5247 init_gold_top100=0.5323 +step=5500 micro_steps=11000 elapsed=77.8s lr=3.000000e-04 loss=2.6204 loss_recon=2.6204 loss_meanflow=0.0000 mean_model_t=0.5012 mean_corrupt_t=0.5012 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7710 corrupt_frac=0.5477 acc_corrupt=0.5999 loss_corrupt=2.6204 wrong_frac=0.5004 init_acc_corrupt=0.4641 acc_corrupt_t_0p0_0p2=0.2151 corrupt_frac_t_0p0_0p2=0.2060 acc_corrupt_t_0p2_0p4=0.4198 corrupt_frac_t_0p2_0p4=0.1956 acc_corrupt_t_0p4_0p6=0.6366 corrupt_frac_t_0p4_0p6=0.1973 acc_corrupt_t_0p6_0p8=0.7961 corrupt_frac_t_0p6_0p8=0.1997 acc_corrupt_t_0p8_1p0=0.9378 corrupt_frac_t_0p8_1p0=0.2014 out_w_norm=91.2355 out_g_norm=0.3429 loss_all=1.4913 init_gold_top10=0.5058 init_gold_top100=0.5130 +step=5600 micro_steps=11200 elapsed=77.9s lr=3.000000e-04 loss=2.6201 loss_recon=2.6201 loss_meanflow=0.0000 mean_model_t=0.5017 mean_corrupt_t=0.5017 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7713 corrupt_frac=0.5480 acc_corrupt=0.6008 loss_corrupt=2.6201 wrong_frac=0.4979 init_acc_corrupt=0.4683 acc_corrupt_t_0p0_0p2=0.2159 corrupt_frac_t_0p0_0p2=0.1987 acc_corrupt_t_0p2_0p4=0.4150 corrupt_frac_t_0p2_0p4=0.1962 acc_corrupt_t_0p4_0p6=0.6334 corrupt_frac_t_0p4_0p6=0.1989 acc_corrupt_t_0p6_0p8=0.7932 corrupt_frac_t_0p6_0p8=0.2081 acc_corrupt_t_0p8_1p0=0.9358 corrupt_frac_t_0p8_1p0=0.1982 out_w_norm=92.2293 out_g_norm=0.3345 loss_all=1.4203 init_gold_top10=0.5139 init_gold_top100=0.5195 +step=5700 micro_steps=11400 elapsed=77.9s lr=3.000000e-04 loss=2.6312 loss_recon=2.6312 loss_meanflow=0.0000 mean_model_t=0.4974 mean_corrupt_t=0.4974 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7705 corrupt_frac=0.5472 acc_corrupt=0.5980 loss_corrupt=2.6312 wrong_frac=0.5029 init_acc_corrupt=0.4619 acc_corrupt_t_0p0_0p2=0.2142 corrupt_frac_t_0p0_0p2=0.2032 acc_corrupt_t_0p2_0p4=0.4188 corrupt_frac_t_0p2_0p4=0.2000 acc_corrupt_t_0p4_0p6=0.6361 corrupt_frac_t_0p4_0p6=0.2009 acc_corrupt_t_0p6_0p8=0.7978 corrupt_frac_t_0p6_0p8=0.1990 acc_corrupt_t_0p8_1p0=0.9352 corrupt_frac_t_0p8_1p0=0.1969 out_w_norm=93.2317 out_g_norm=0.3222 loss_all=1.5815 init_gold_top10=0.4613 init_gold_top100=0.4684 +step=5800 micro_steps=11600 elapsed=77.6s lr=3.000000e-04 loss=2.5988 loss_recon=2.5988 loss_meanflow=0.0000 mean_model_t=0.5006 mean_corrupt_t=0.5006 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7715 corrupt_frac=0.5473 acc_corrupt=0.6004 loss_corrupt=2.5988 wrong_frac=0.5004 init_acc_corrupt=0.4651 acc_corrupt_t_0p0_0p2=0.2155 corrupt_frac_t_0p0_0p2=0.1993 acc_corrupt_t_0p2_0p4=0.4197 corrupt_frac_t_0p2_0p4=0.2011 acc_corrupt_t_0p4_0p6=0.6363 corrupt_frac_t_0p4_0p6=0.1993 acc_corrupt_t_0p6_0p8=0.7953 corrupt_frac_t_0p6_0p8=0.2013 acc_corrupt_t_0p8_1p0=0.9352 corrupt_frac_t_0p8_1p0=0.1991 out_w_norm=94.2054 out_g_norm=0.3476 loss_all=1.1646 init_gold_top10=0.5594 init_gold_top100=0.5650 +step=5900 micro_steps=11800 elapsed=76.8s lr=3.000000e-04 loss=2.6073 loss_recon=2.6073 loss_meanflow=0.0000 mean_model_t=0.4973 mean_corrupt_t=0.4973 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7715 corrupt_frac=0.5469 acc_corrupt=0.5996 loss_corrupt=2.6073 wrong_frac=0.5017 init_acc_corrupt=0.4639 acc_corrupt_t_0p0_0p2=0.2166 corrupt_frac_t_0p0_0p2=0.2007 acc_corrupt_t_0p2_0p4=0.4228 corrupt_frac_t_0p2_0p4=0.2000 acc_corrupt_t_0p4_0p6=0.6335 corrupt_frac_t_0p4_0p6=0.2072 acc_corrupt_t_0p6_0p8=0.7968 corrupt_frac_t_0p6_0p8=0.1960 acc_corrupt_t_0p8_1p0=0.9386 corrupt_frac_t_0p8_1p0=0.1961 out_w_norm=95.1501 out_g_norm=0.3191 loss_all=1.7509 init_gold_top10=0.4825 init_gold_top100=0.4898 +step=6000 micro_steps=12000 elapsed=76.9s lr=3.000000e-04 loss=2.6029 loss_recon=2.6029 loss_meanflow=0.0000 mean_model_t=0.4993 mean_corrupt_t=0.4993 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7722 corrupt_frac=0.5476 acc_corrupt=0.6013 loss_corrupt=2.6029 wrong_frac=0.5002 init_acc_corrupt=0.4654 acc_corrupt_t_0p0_0p2=0.2155 corrupt_frac_t_0p0_0p2=0.2042 acc_corrupt_t_0p2_0p4=0.4211 corrupt_frac_t_0p2_0p4=0.1959 acc_corrupt_t_0p4_0p6=0.6368 corrupt_frac_t_0p4_0p6=0.1974 acc_corrupt_t_0p6_0p8=0.7971 corrupt_frac_t_0p6_0p8=0.1999 acc_corrupt_t_0p8_1p0=0.9368 corrupt_frac_t_0p8_1p0=0.2026 out_w_norm=96.0792 out_g_norm=0.3352 loss_all=1.5024 init_gold_top10=0.5151 init_gold_top100=0.5208 +step=6100 micro_steps=12200 elapsed=80.4s lr=3.000000e-04 loss=2.6185 loss_recon=2.6185 loss_meanflow=0.0000 mean_model_t=0.4984 mean_corrupt_t=0.4984 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7688 corrupt_frac=0.5546 acc_corrupt=0.5990 loss_corrupt=2.6185 wrong_frac=0.5007 init_acc_corrupt=0.4642 acc_corrupt_t_0p0_0p2=0.2158 corrupt_frac_t_0p0_0p2=0.1990 acc_corrupt_t_0p2_0p4=0.4191 corrupt_frac_t_0p2_0p4=0.2030 acc_corrupt_t_0p4_0p6=0.6341 corrupt_frac_t_0p4_0p6=0.2016 acc_corrupt_t_0p6_0p8=0.7960 corrupt_frac_t_0p6_0p8=0.1986 acc_corrupt_t_0p8_1p0=0.9359 corrupt_frac_t_0p8_1p0=0.1977 out_w_norm=97.0229 out_g_norm=0.3253 loss_all=1.5957 init_gold_top10=0.5362 init_gold_top100=0.5410 +step=6200 micro_steps=12400 elapsed=77.7s lr=3.000000e-04 loss=2.6121 loss_recon=2.6121 loss_meanflow=0.0000 mean_model_t=0.5030 mean_corrupt_t=0.5030 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7727 corrupt_frac=0.5483 acc_corrupt=0.6023 loss_corrupt=2.6121 wrong_frac=0.4967 init_acc_corrupt=0.4686 acc_corrupt_t_0p0_0p2=0.2132 corrupt_frac_t_0p0_0p2=0.1958 acc_corrupt_t_0p2_0p4=0.4179 corrupt_frac_t_0p2_0p4=0.2010 acc_corrupt_t_0p4_0p6=0.6361 corrupt_frac_t_0p4_0p6=0.1992 acc_corrupt_t_0p6_0p8=0.7954 corrupt_frac_t_0p6_0p8=0.2018 acc_corrupt_t_0p8_1p0=0.9358 corrupt_frac_t_0p8_1p0=0.2023 out_w_norm=97.9547 out_g_norm=0.3121 loss_all=1.8363 init_gold_top10=0.4435 init_gold_top100=0.4540 +step=6300 micro_steps=12600 elapsed=77.7s lr=3.000000e-04 loss=2.5863 loss_recon=2.5863 loss_meanflow=0.0000 mean_model_t=0.5018 mean_corrupt_t=0.5018 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7726 corrupt_frac=0.5512 acc_corrupt=0.6036 loss_corrupt=2.5863 wrong_frac=0.4980 init_acc_corrupt=0.4676 acc_corrupt_t_0p0_0p2=0.2149 corrupt_frac_t_0p0_0p2=0.1913 acc_corrupt_t_0p2_0p4=0.4205 corrupt_frac_t_0p2_0p4=0.2025 acc_corrupt_t_0p4_0p6=0.6347 corrupt_frac_t_0p4_0p6=0.2066 acc_corrupt_t_0p6_0p8=0.7974 corrupt_frac_t_0p6_0p8=0.2021 acc_corrupt_t_0p8_1p0=0.9367 corrupt_frac_t_0p8_1p0=0.1975 out_w_norm=98.9066 out_g_norm=0.3033 loss_all=1.5438 init_gold_top10=0.5072 init_gold_top100=0.5147 +step=6400 micro_steps=12800 elapsed=77.7s lr=3.000000e-04 loss=2.5753 loss_recon=2.5753 loss_meanflow=0.0000 mean_model_t=0.5032 mean_corrupt_t=0.5032 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7738 corrupt_frac=0.5488 acc_corrupt=0.6042 loss_corrupt=2.5753 wrong_frac=0.4974 init_acc_corrupt=0.4684 acc_corrupt_t_0p0_0p2=0.2154 corrupt_frac_t_0p0_0p2=0.1994 acc_corrupt_t_0p2_0p4=0.4236 corrupt_frac_t_0p2_0p4=0.1958 acc_corrupt_t_0p4_0p6=0.6387 corrupt_frac_t_0p4_0p6=0.2030 acc_corrupt_t_0p6_0p8=0.7975 corrupt_frac_t_0p6_0p8=0.2003 acc_corrupt_t_0p8_1p0=0.9372 corrupt_frac_t_0p8_1p0=0.2015 out_w_norm=99.8255 out_g_norm=0.3039 loss_all=1.2909 init_gold_top10=0.5096 init_gold_top100=0.5142 +step=6500 micro_steps=13000 elapsed=77.5s lr=3.000000e-04 loss=2.6387 loss_recon=2.6387 loss_meanflow=0.0000 mean_model_t=0.4962 mean_corrupt_t=0.4962 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7692 corrupt_frac=0.5491 acc_corrupt=0.5963 loss_corrupt=2.6387 wrong_frac=0.5052 init_acc_corrupt=0.4595 acc_corrupt_t_0p0_0p2=0.2150 corrupt_frac_t_0p0_0p2=0.2026 acc_corrupt_t_0p2_0p4=0.4134 corrupt_frac_t_0p2_0p4=0.2021 acc_corrupt_t_0p4_0p6=0.6372 corrupt_frac_t_0p4_0p6=0.2017 acc_corrupt_t_0p6_0p8=0.7975 corrupt_frac_t_0p6_0p8=0.2005 acc_corrupt_t_0p8_1p0=0.9361 corrupt_frac_t_0p8_1p0=0.1931 out_w_norm=100.7425 out_g_norm=0.3065 loss_all=1.7912 init_gold_top10=0.4068 init_gold_top100=0.4151 +step=6600 micro_steps=13200 elapsed=77.6s lr=3.000000e-04 loss=2.5887 loss_recon=2.5887 loss_meanflow=0.0000 mean_model_t=0.5022 mean_corrupt_t=0.5022 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7731 corrupt_frac=0.5500 acc_corrupt=0.6033 loss_corrupt=2.5887 wrong_frac=0.4979 init_acc_corrupt=0.4678 acc_corrupt_t_0p0_0p2=0.2135 corrupt_frac_t_0p0_0p2=0.1985 acc_corrupt_t_0p2_0p4=0.4208 corrupt_frac_t_0p2_0p4=0.1964 acc_corrupt_t_0p4_0p6=0.6389 corrupt_frac_t_0p4_0p6=0.2058 acc_corrupt_t_0p6_0p8=0.7974 corrupt_frac_t_0p6_0p8=0.1947 acc_corrupt_t_0p8_1p0=0.9363 corrupt_frac_t_0p8_1p0=0.2045 out_w_norm=101.6529 out_g_norm=0.3072 loss_all=1.5498 init_gold_top10=0.5361 init_gold_top100=0.5428 +step=6700 micro_steps=13400 elapsed=77.7s lr=3.000000e-04 loss=2.5940 loss_recon=2.5940 loss_meanflow=0.0000 mean_model_t=0.4999 mean_corrupt_t=0.4999 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7730 corrupt_frac=0.5492 acc_corrupt=0.6029 loss_corrupt=2.5940 wrong_frac=0.4995 init_acc_corrupt=0.4658 acc_corrupt_t_0p0_0p2=0.2165 corrupt_frac_t_0p0_0p2=0.2014 acc_corrupt_t_0p2_0p4=0.4174 corrupt_frac_t_0p2_0p4=0.1953 acc_corrupt_t_0p4_0p6=0.6382 corrupt_frac_t_0p4_0p6=0.1987 acc_corrupt_t_0p6_0p8=0.8001 corrupt_frac_t_0p6_0p8=0.2039 acc_corrupt_t_0p8_1p0=0.9361 corrupt_frac_t_0p8_1p0=0.2006 out_w_norm=102.5984 out_g_norm=0.2965 loss_all=1.3087 init_gold_top10=0.4875 init_gold_top100=0.4979 +step=6800 micro_steps=13600 elapsed=77.4s lr=3.000000e-04 loss=2.5866 loss_recon=2.5866 loss_meanflow=0.0000 mean_model_t=0.4974 mean_corrupt_t=0.4974 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7715 corrupt_frac=0.5532 acc_corrupt=0.6033 loss_corrupt=2.5866 wrong_frac=0.4999 init_acc_corrupt=0.4666 acc_corrupt_t_0p0_0p2=0.2179 corrupt_frac_t_0p0_0p2=0.2021 acc_corrupt_t_0p2_0p4=0.4267 corrupt_frac_t_0p2_0p4=0.1958 acc_corrupt_t_0p4_0p6=0.6364 corrupt_frac_t_0p4_0p6=0.2030 acc_corrupt_t_0p6_0p8=0.7980 corrupt_frac_t_0p6_0p8=0.1978 acc_corrupt_t_0p8_1p0=0.9371 corrupt_frac_t_0p8_1p0=0.2013 out_w_norm=103.5085 out_g_norm=0.2995 loss_all=1.9027 init_gold_top10=0.4583 init_gold_top100=0.4657 +step=6900 micro_steps=13800 elapsed=76.9s lr=3.000000e-04 loss=2.5575 loss_recon=2.5575 loss_meanflow=0.0000 mean_model_t=0.5006 mean_corrupt_t=0.5006 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7741 corrupt_frac=0.5524 acc_corrupt=0.6067 loss_corrupt=2.5575 wrong_frac=0.4964 init_acc_corrupt=0.4699 acc_corrupt_t_0p0_0p2=0.2190 corrupt_frac_t_0p0_0p2=0.2005 acc_corrupt_t_0p2_0p4=0.4218 corrupt_frac_t_0p2_0p4=0.1944 acc_corrupt_t_0p4_0p6=0.6429 corrupt_frac_t_0p4_0p6=0.1972 acc_corrupt_t_0p6_0p8=0.7996 corrupt_frac_t_0p6_0p8=0.2050 acc_corrupt_t_0p8_1p0=0.9371 corrupt_frac_t_0p8_1p0=0.2028 out_w_norm=104.4233 out_g_norm=0.2957 loss_all=1.5377 init_gold_top10=0.5061 init_gold_top100=0.5115 +step=7000 micro_steps=14000 elapsed=76.8s lr=3.000000e-04 loss=2.5914 loss_recon=2.5914 loss_meanflow=0.0000 mean_model_t=0.5005 mean_corrupt_t=0.5005 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7711 corrupt_frac=0.5522 acc_corrupt=0.6015 loss_corrupt=2.5914 wrong_frac=0.5008 init_acc_corrupt=0.4648 acc_corrupt_t_0p0_0p2=0.2147 corrupt_frac_t_0p0_0p2=0.2040 acc_corrupt_t_0p2_0p4=0.4238 corrupt_frac_t_0p2_0p4=0.2002 acc_corrupt_t_0p4_0p6=0.6403 corrupt_frac_t_0p4_0p6=0.1940 acc_corrupt_t_0p6_0p8=0.7971 corrupt_frac_t_0p6_0p8=0.1996 acc_corrupt_t_0p8_1p0=0.9375 corrupt_frac_t_0p8_1p0=0.2022 out_w_norm=105.3211 out_g_norm=0.2895 loss_all=1.2258 init_gold_top10=0.5607 init_gold_top100=0.5651 +step=7100 micro_steps=14200 elapsed=79.8s lr=3.000000e-04 loss=2.5540 loss_recon=2.5540 loss_meanflow=0.0000 mean_model_t=0.5010 mean_corrupt_t=0.5010 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7759 corrupt_frac=0.5476 acc_corrupt=0.6071 loss_corrupt=2.5540 wrong_frac=0.4977 init_acc_corrupt=0.4679 acc_corrupt_t_0p0_0p2=0.2209 corrupt_frac_t_0p0_0p2=0.1951 acc_corrupt_t_0p2_0p4=0.4264 corrupt_frac_t_0p2_0p4=0.2035 acc_corrupt_t_0p4_0p6=0.6422 corrupt_frac_t_0p4_0p6=0.1979 acc_corrupt_t_0p6_0p8=0.8014 corrupt_frac_t_0p6_0p8=0.2046 acc_corrupt_t_0p8_1p0=0.9357 corrupt_frac_t_0p8_1p0=0.1990 out_w_norm=106.2180 out_g_norm=0.2981 loss_all=1.2346 init_gold_top10=0.4574 init_gold_top100=0.4662 +step=7200 micro_steps=14400 elapsed=77.7s lr=3.000000e-04 loss=2.5626 loss_recon=2.5626 loss_meanflow=0.0000 mean_model_t=0.5028 mean_corrupt_t=0.5028 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7746 corrupt_frac=0.5504 acc_corrupt=0.6066 loss_corrupt=2.5626 wrong_frac=0.4952 init_acc_corrupt=0.4701 acc_corrupt_t_0p0_0p2=0.2169 corrupt_frac_t_0p0_0p2=0.1949 acc_corrupt_t_0p2_0p4=0.4226 corrupt_frac_t_0p2_0p4=0.2025 acc_corrupt_t_0p4_0p6=0.6396 corrupt_frac_t_0p4_0p6=0.1981 acc_corrupt_t_0p6_0p8=0.7989 corrupt_frac_t_0p6_0p8=0.1963 acc_corrupt_t_0p8_1p0=0.9380 corrupt_frac_t_0p8_1p0=0.2082 out_w_norm=107.1215 out_g_norm=0.2885 loss_all=1.3280 init_gold_top10=0.4818 init_gold_top100=0.4894 +step=7300 micro_steps=14600 elapsed=77.6s lr=3.000000e-04 loss=2.6049 loss_recon=2.6049 loss_meanflow=0.0000 mean_model_t=0.4943 mean_corrupt_t=0.4943 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7699 corrupt_frac=0.5532 acc_corrupt=0.6009 loss_corrupt=2.6049 wrong_frac=0.5056 init_acc_corrupt=0.4593 acc_corrupt_t_0p0_0p2=0.2239 corrupt_frac_t_0p0_0p2=0.2062 acc_corrupt_t_0p2_0p4=0.4259 corrupt_frac_t_0p2_0p4=0.2031 acc_corrupt_t_0p4_0p6=0.6418 corrupt_frac_t_0p4_0p6=0.1982 acc_corrupt_t_0p6_0p8=0.8027 corrupt_frac_t_0p6_0p8=0.2006 acc_corrupt_t_0p8_1p0=0.9378 corrupt_frac_t_0p8_1p0=0.1920 out_w_norm=108.0347 out_g_norm=0.2873 loss_all=1.4253 init_gold_top10=0.4855 init_gold_top100=0.4898 +step=7400 micro_steps=14800 elapsed=77.8s lr=3.000000e-04 loss=2.5567 loss_recon=2.5567 loss_meanflow=0.0000 mean_model_t=0.4988 mean_corrupt_t=0.4988 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7740 corrupt_frac=0.5520 acc_corrupt=0.6069 loss_corrupt=2.5567 wrong_frac=0.5005 init_acc_corrupt=0.4645 acc_corrupt_t_0p0_0p2=0.2251 corrupt_frac_t_0p0_0p2=0.1989 acc_corrupt_t_0p2_0p4=0.4294 corrupt_frac_t_0p2_0p4=0.2011 acc_corrupt_t_0p4_0p6=0.6402 corrupt_frac_t_0p4_0p6=0.2018 acc_corrupt_t_0p6_0p8=0.8008 corrupt_frac_t_0p6_0p8=0.1971 acc_corrupt_t_0p8_1p0=0.9386 corrupt_frac_t_0p8_1p0=0.2010 out_w_norm=108.9473 out_g_norm=0.2868 loss_all=1.2317 init_gold_top10=0.5452 init_gold_top100=0.5493 +step=7500 micro_steps=15000 elapsed=77.6s lr=3.000000e-04 loss=2.5327 loss_recon=2.5327 loss_meanflow=0.0000 mean_model_t=0.5048 mean_corrupt_t=0.5048 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7768 corrupt_frac=0.5509 acc_corrupt=0.6101 loss_corrupt=2.5327 wrong_frac=0.4950 init_acc_corrupt=0.4709 acc_corrupt_t_0p0_0p2=0.2218 corrupt_frac_t_0p0_0p2=0.1934 acc_corrupt_t_0p2_0p4=0.4227 corrupt_frac_t_0p2_0p4=0.2010 acc_corrupt_t_0p4_0p6=0.6448 corrupt_frac_t_0p4_0p6=0.1977 acc_corrupt_t_0p6_0p8=0.8027 corrupt_frac_t_0p6_0p8=0.2056 acc_corrupt_t_0p8_1p0=0.9381 corrupt_frac_t_0p8_1p0=0.2022 out_w_norm=109.8328 out_g_norm=0.2806 loss_all=1.5490 init_gold_top10=0.5035 init_gold_top100=0.5103 +step=7600 micro_steps=15200 elapsed=77.8s lr=3.000000e-04 loss=2.5636 loss_recon=2.5636 loss_meanflow=0.0000 mean_model_t=0.4999 mean_corrupt_t=0.4999 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7753 corrupt_frac=0.5483 acc_corrupt=0.6059 loss_corrupt=2.5636 wrong_frac=0.5012 init_acc_corrupt=0.4634 acc_corrupt_t_0p0_0p2=0.2242 corrupt_frac_t_0p0_0p2=0.1970 acc_corrupt_t_0p2_0p4=0.4300 corrupt_frac_t_0p2_0p4=0.2062 acc_corrupt_t_0p4_0p6=0.6410 corrupt_frac_t_0p4_0p6=0.1983 acc_corrupt_t_0p6_0p8=0.8025 corrupt_frac_t_0p6_0p8=0.2044 acc_corrupt_t_0p8_1p0=0.9371 corrupt_frac_t_0p8_1p0=0.1942 out_w_norm=110.7253 out_g_norm=0.2854 loss_all=1.6910 init_gold_top10=0.4204 init_gold_top100=0.4264 +step=7700 micro_steps=15400 elapsed=77.8s lr=3.000000e-04 loss=2.5735 loss_recon=2.5735 loss_meanflow=0.0000 mean_model_t=0.4996 mean_corrupt_t=0.4996 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7729 corrupt_frac=0.5518 acc_corrupt=0.6036 loss_corrupt=2.5735 wrong_frac=0.5025 init_acc_corrupt=0.4620 acc_corrupt_t_0p0_0p2=0.2235 corrupt_frac_t_0p0_0p2=0.2008 acc_corrupt_t_0p2_0p4=0.4214 corrupt_frac_t_0p2_0p4=0.2026 acc_corrupt_t_0p4_0p6=0.6425 corrupt_frac_t_0p4_0p6=0.1968 acc_corrupt_t_0p6_0p8=0.8028 corrupt_frac_t_0p6_0p8=0.2071 acc_corrupt_t_0p8_1p0=0.9372 corrupt_frac_t_0p8_1p0=0.1928 out_w_norm=111.6133 out_g_norm=0.2785 loss_all=1.4644 init_gold_top10=0.4975 init_gold_top100=0.5049 +step=7800 micro_steps=15600 elapsed=77.6s lr=3.000000e-04 loss=2.5675 loss_recon=2.5675 loss_meanflow=0.0000 mean_model_t=0.5001 mean_corrupt_t=0.5001 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7750 corrupt_frac=0.5481 acc_corrupt=0.6046 loss_corrupt=2.5675 wrong_frac=0.5018 init_acc_corrupt=0.4629 acc_corrupt_t_0p0_0p2=0.2218 corrupt_frac_t_0p0_0p2=0.2016 acc_corrupt_t_0p2_0p4=0.4264 corrupt_frac_t_0p2_0p4=0.2031 acc_corrupt_t_0p4_0p6=0.6429 corrupt_frac_t_0p4_0p6=0.1980 acc_corrupt_t_0p6_0p8=0.8014 corrupt_frac_t_0p6_0p8=0.1971 acc_corrupt_t_0p8_1p0=0.9390 corrupt_frac_t_0p8_1p0=0.2002 out_w_norm=112.4894 out_g_norm=0.2731 loss_all=1.2262 init_gold_top10=0.5450 init_gold_top100=0.5481 +step=7900 micro_steps=15800 elapsed=60.0s lr=3.000000e-04 loss=2.5359 loss_recon=2.5359 loss_meanflow=0.0000 mean_model_t=0.5017 mean_corrupt_t=0.5017 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7772 corrupt_frac=0.5486 acc_corrupt=0.6089 loss_corrupt=2.5359 wrong_frac=0.4989 init_acc_corrupt=0.4665 acc_corrupt_t_0p0_0p2=0.2245 corrupt_frac_t_0p0_0p2=0.1991 acc_corrupt_t_0p2_0p4=0.4282 corrupt_frac_t_0p2_0p4=0.2002 acc_corrupt_t_0p4_0p6=0.6462 corrupt_frac_t_0p4_0p6=0.2004 acc_corrupt_t_0p6_0p8=0.8027 corrupt_frac_t_0p6_0p8=0.1967 acc_corrupt_t_0p8_1p0=0.9389 corrupt_frac_t_0p8_1p0=0.2036 out_w_norm=113.3593 out_g_norm=0.2828 loss_all=1.6426 init_gold_top10=0.4438 init_gold_top100=0.4500 +step=8000 micro_steps=16000 elapsed=56.5s lr=3.000000e-04 loss=2.5481 loss_recon=2.5481 loss_meanflow=0.0000 mean_model_t=0.5010 mean_corrupt_t=0.5010 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7742 corrupt_frac=0.5532 acc_corrupt=0.6068 loss_corrupt=2.5481 wrong_frac=0.5013 init_acc_corrupt=0.4636 acc_corrupt_t_0p0_0p2=0.2203 corrupt_frac_t_0p0_0p2=0.1997 acc_corrupt_t_0p2_0p4=0.4297 corrupt_frac_t_0p2_0p4=0.2035 acc_corrupt_t_0p4_0p6=0.6469 corrupt_frac_t_0p4_0p6=0.2002 acc_corrupt_t_0p6_0p8=0.8037 corrupt_frac_t_0p6_0p8=0.1978 acc_corrupt_t_0p8_1p0=0.9401 corrupt_frac_t_0p8_1p0=0.1989 out_w_norm=114.2595 out_g_norm=0.2802 loss_all=1.4955 init_gold_top10=0.4832 init_gold_top100=0.4893 +step=8100 micro_steps=16200 elapsed=39.3s lr=3.000000e-04 loss=2.5472 loss_recon=2.5472 loss_meanflow=0.0000 mean_model_t=0.5015 mean_corrupt_t=0.5015 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7746 corrupt_frac=0.5516 acc_corrupt=0.6065 loss_corrupt=2.5472 wrong_frac=0.4996 init_acc_corrupt=0.4663 acc_corrupt_t_0p0_0p2=0.2194 corrupt_frac_t_0p0_0p2=0.1983 acc_corrupt_t_0p2_0p4=0.4271 corrupt_frac_t_0p2_0p4=0.2007 acc_corrupt_t_0p4_0p6=0.6405 corrupt_frac_t_0p4_0p6=0.2023 acc_corrupt_t_0p6_0p8=0.8054 corrupt_frac_t_0p6_0p8=0.1969 acc_corrupt_t_0p8_1p0=0.9374 corrupt_frac_t_0p8_1p0=0.2018 out_w_norm=115.1267 out_g_norm=0.2704 loss_all=1.5898 init_gold_top10=0.4654 init_gold_top100=0.4717 +step=8200 micro_steps=16400 elapsed=36.2s lr=3.000000e-04 loss=2.5546 loss_recon=2.5546 loss_meanflow=0.0000 mean_model_t=0.5010 mean_corrupt_t=0.5010 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7744 corrupt_frac=0.5511 acc_corrupt=0.6057 loss_corrupt=2.5546 wrong_frac=0.5010 init_acc_corrupt=0.4643 acc_corrupt_t_0p0_0p2=0.2224 corrupt_frac_t_0p0_0p2=0.2004 acc_corrupt_t_0p2_0p4=0.4306 corrupt_frac_t_0p2_0p4=0.2022 acc_corrupt_t_0p4_0p6=0.6424 corrupt_frac_t_0p4_0p6=0.1993 acc_corrupt_t_0p6_0p8=0.7999 corrupt_frac_t_0p6_0p8=0.1968 acc_corrupt_t_0p8_1p0=0.9372 corrupt_frac_t_0p8_1p0=0.2012 out_w_norm=115.9831 out_g_norm=0.2709 loss_all=1.2683 init_gold_top10=0.5174 init_gold_top100=0.5245 +step=8300 micro_steps=16600 elapsed=60.2s lr=3.000000e-04 loss=2.5135 loss_recon=2.5135 loss_meanflow=0.0000 mean_model_t=0.5015 mean_corrupt_t=0.5015 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7779 corrupt_frac=0.5513 acc_corrupt=0.6121 loss_corrupt=2.5135 wrong_frac=0.4966 init_acc_corrupt=0.4687 acc_corrupt_t_0p0_0p2=0.2265 corrupt_frac_t_0p0_0p2=0.1973 acc_corrupt_t_0p2_0p4=0.4333 corrupt_frac_t_0p2_0p4=0.1979 acc_corrupt_t_0p4_0p6=0.6429 corrupt_frac_t_0p4_0p6=0.1988 acc_corrupt_t_0p6_0p8=0.8043 corrupt_frac_t_0p6_0p8=0.2015 acc_corrupt_t_0p8_1p0=0.9377 corrupt_frac_t_0p8_1p0=0.2046 out_w_norm=116.8535 out_g_norm=0.2664 loss_all=1.4136 init_gold_top10=0.5238 init_gold_top100=0.5294 +step=8400 micro_steps=16800 elapsed=78.0s lr=3.000000e-04 loss=2.5300 loss_recon=2.5300 loss_meanflow=0.0000 mean_model_t=0.4992 mean_corrupt_t=0.4992 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7771 corrupt_frac=0.5484 acc_corrupt=0.6085 loss_corrupt=2.5300 wrong_frac=0.5015 init_acc_corrupt=0.4641 acc_corrupt_t_0p0_0p2=0.2264 corrupt_frac_t_0p0_0p2=0.2003 acc_corrupt_t_0p2_0p4=0.4319 corrupt_frac_t_0p2_0p4=0.1987 acc_corrupt_t_0p4_0p6=0.6433 corrupt_frac_t_0p4_0p6=0.2043 acc_corrupt_t_0p6_0p8=0.8034 corrupt_frac_t_0p6_0p8=0.1968 acc_corrupt_t_0p8_1p0=0.9398 corrupt_frac_t_0p8_1p0=0.1998 out_w_norm=117.7216 out_g_norm=0.2737 loss_all=1.5622 init_gold_top10=0.4523 init_gold_top100=0.4589 +step=8500 micro_steps=17000 elapsed=77.7s lr=3.000000e-04 loss=2.5651 loss_recon=2.5651 loss_meanflow=0.0000 mean_model_t=0.4970 mean_corrupt_t=0.4970 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7731 corrupt_frac=0.5516 acc_corrupt=0.6031 loss_corrupt=2.5651 wrong_frac=0.5051 init_acc_corrupt=0.4597 acc_corrupt_t_0p0_0p2=0.2193 corrupt_frac_t_0p0_0p2=0.2013 acc_corrupt_t_0p2_0p4=0.4280 corrupt_frac_t_0p2_0p4=0.2045 acc_corrupt_t_0p4_0p6=0.6426 corrupt_frac_t_0p4_0p6=0.1978 acc_corrupt_t_0p6_0p8=0.8019 corrupt_frac_t_0p6_0p8=0.2034 acc_corrupt_t_0p8_1p0=0.9390 corrupt_frac_t_0p8_1p0=0.1930 out_w_norm=118.5776 out_g_norm=0.2607 loss_all=1.6912 init_gold_top10=0.4639 init_gold_top100=0.4711 +step=8600 micro_steps=17200 elapsed=69.6s lr=3.000000e-04 loss=2.5629 loss_recon=2.5629 loss_meanflow=0.0000 mean_model_t=0.4940 mean_corrupt_t=0.4940 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7722 corrupt_frac=0.5534 acc_corrupt=0.6036 loss_corrupt=2.5629 wrong_frac=0.5046 init_acc_corrupt=0.4603 acc_corrupt_t_0p0_0p2=0.2247 corrupt_frac_t_0p0_0p2=0.2054 acc_corrupt_t_0p2_0p4=0.4270 corrupt_frac_t_0p2_0p4=0.1985 acc_corrupt_t_0p4_0p6=0.6434 corrupt_frac_t_0p4_0p6=0.2034 acc_corrupt_t_0p6_0p8=0.8043 corrupt_frac_t_0p6_0p8=0.1982 acc_corrupt_t_0p8_1p0=0.9382 corrupt_frac_t_0p8_1p0=0.1944 out_w_norm=119.4251 out_g_norm=0.2572 loss_all=1.4473 init_gold_top10=0.5125 init_gold_top100=0.5164 +step=8700 micro_steps=17400 elapsed=54.0s lr=3.000000e-04 loss=2.4918 loss_recon=2.4918 loss_meanflow=0.0000 mean_model_t=0.5047 mean_corrupt_t=0.5047 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7806 corrupt_frac=0.5476 acc_corrupt=0.6135 loss_corrupt=2.4918 wrong_frac=0.4960 init_acc_corrupt=0.4709 acc_corrupt_t_0p0_0p2=0.2231 corrupt_frac_t_0p0_0p2=0.1964 acc_corrupt_t_0p2_0p4=0.4346 corrupt_frac_t_0p2_0p4=0.1918 acc_corrupt_t_0p4_0p6=0.6454 corrupt_frac_t_0p4_0p6=0.2097 acc_corrupt_t_0p6_0p8=0.8061 corrupt_frac_t_0p6_0p8=0.1995 acc_corrupt_t_0p8_1p0=0.9388 corrupt_frac_t_0p8_1p0=0.2026 out_w_norm=120.2533 out_g_norm=0.2531 loss_all=1.3193 init_gold_top10=0.5162 init_gold_top100=0.5198 +step=8800 micro_steps=17600 elapsed=55.7s lr=3.000000e-04 loss=2.5282 loss_recon=2.5282 loss_meanflow=0.0000 mean_model_t=0.5011 mean_corrupt_t=0.5011 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7762 corrupt_frac=0.5535 acc_corrupt=0.6105 loss_corrupt=2.5282 wrong_frac=0.4989 init_acc_corrupt=0.4666 acc_corrupt_t_0p0_0p2=0.2256 corrupt_frac_t_0p0_0p2=0.1986 acc_corrupt_t_0p2_0p4=0.4314 corrupt_frac_t_0p2_0p4=0.2010 acc_corrupt_t_0p4_0p6=0.6479 corrupt_frac_t_0p4_0p6=0.2020 acc_corrupt_t_0p6_0p8=0.8083 corrupt_frac_t_0p6_0p8=0.1998 acc_corrupt_t_0p8_1p0=0.9394 corrupt_frac_t_0p8_1p0=0.1986 out_w_norm=121.0735 out_g_norm=0.2551 loss_all=1.4900 init_gold_top10=0.5553 init_gold_top100=0.5626 +step=8900 micro_steps=17800 elapsed=79.2s lr=3.000000e-04 loss=2.5405 loss_recon=2.5405 loss_meanflow=0.0000 mean_model_t=0.4970 mean_corrupt_t=0.4970 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7753 corrupt_frac=0.5500 acc_corrupt=0.6064 loss_corrupt=2.5405 wrong_frac=0.5037 init_acc_corrupt=0.4611 acc_corrupt_t_0p0_0p2=0.2249 corrupt_frac_t_0p0_0p2=0.1990 acc_corrupt_t_0p2_0p4=0.4304 corrupt_frac_t_0p2_0p4=0.2076 acc_corrupt_t_0p4_0p6=0.6453 corrupt_frac_t_0p4_0p6=0.2025 acc_corrupt_t_0p6_0p8=0.8057 corrupt_frac_t_0p6_0p8=0.1894 acc_corrupt_t_0p8_1p0=0.9380 corrupt_frac_t_0p8_1p0=0.2015 out_w_norm=121.8974 out_g_norm=0.2576 loss_all=1.3561 init_gold_top10=0.5018 init_gold_top100=0.5089 +step=9000 micro_steps=18000 elapsed=79.3s lr=3.000000e-04 loss=2.5433 loss_recon=2.5433 loss_meanflow=0.0000 mean_model_t=0.4963 mean_corrupt_t=0.4963 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7743 corrupt_frac=0.5521 acc_corrupt=0.6061 loss_corrupt=2.5433 wrong_frac=0.5035 init_acc_corrupt=0.4618 acc_corrupt_t_0p0_0p2=0.2259 corrupt_frac_t_0p0_0p2=0.1982 acc_corrupt_t_0p2_0p4=0.4272 corrupt_frac_t_0p2_0p4=0.2088 acc_corrupt_t_0p4_0p6=0.6456 corrupt_frac_t_0p4_0p6=0.2003 acc_corrupt_t_0p6_0p8=0.8052 corrupt_frac_t_0p6_0p8=0.1950 acc_corrupt_t_0p8_1p0=0.9399 corrupt_frac_t_0p8_1p0=0.1977 out_w_norm=122.7221 out_g_norm=0.2489 loss_all=1.3807 init_gold_top10=0.5063 init_gold_top100=0.5107 +step=9100 micro_steps=18200 elapsed=81.4s lr=3.000000e-04 loss=2.5689 loss_recon=2.5689 loss_meanflow=0.0000 mean_model_t=0.4950 mean_corrupt_t=0.4950 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7736 corrupt_frac=0.5490 acc_corrupt=0.6025 loss_corrupt=2.5689 wrong_frac=0.5069 init_acc_corrupt=0.4574 acc_corrupt_t_0p0_0p2=0.2207 corrupt_frac_t_0p0_0p2=0.2090 acc_corrupt_t_0p2_0p4=0.4296 corrupt_frac_t_0p2_0p4=0.2021 acc_corrupt_t_0p4_0p6=0.6490 corrupt_frac_t_0p4_0p6=0.2005 acc_corrupt_t_0p6_0p8=0.8080 corrupt_frac_t_0p6_0p8=0.1947 acc_corrupt_t_0p8_1p0=0.9402 corrupt_frac_t_0p8_1p0=0.1936 out_w_norm=123.5551 out_g_norm=0.2620 loss_all=1.4816 init_gold_top10=0.4793 init_gold_top100=0.4829 +step=9200 micro_steps=18400 elapsed=79.2s lr=3.000000e-04 loss=2.4912 loss_recon=2.4912 loss_meanflow=0.0000 mean_model_t=0.5045 mean_corrupt_t=0.5045 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7787 corrupt_frac=0.5511 acc_corrupt=0.6127 loss_corrupt=2.4912 wrong_frac=0.4965 init_acc_corrupt=0.4694 acc_corrupt_t_0p0_0p2=0.2266 corrupt_frac_t_0p0_0p2=0.1972 acc_corrupt_t_0p2_0p4=0.4315 corrupt_frac_t_0p2_0p4=0.1998 acc_corrupt_t_0p4_0p6=0.6466 corrupt_frac_t_0p4_0p6=0.1932 acc_corrupt_t_0p6_0p8=0.8030 corrupt_frac_t_0p6_0p8=0.2072 acc_corrupt_t_0p8_1p0=0.9402 corrupt_frac_t_0p8_1p0=0.2027 out_w_norm=124.3835 out_g_norm=0.2515 loss_all=1.6247 init_gold_top10=0.4497 init_gold_top100=0.4567 +step=9300 micro_steps=18600 elapsed=79.2s lr=3.000000e-04 loss=2.5151 loss_recon=2.5151 loss_meanflow=0.0000 mean_model_t=0.5010 mean_corrupt_t=0.5010 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7783 corrupt_frac=0.5482 acc_corrupt=0.6102 loss_corrupt=2.5151 wrong_frac=0.4998 init_acc_corrupt=0.4665 acc_corrupt_t_0p0_0p2=0.2243 corrupt_frac_t_0p0_0p2=0.1962 acc_corrupt_t_0p2_0p4=0.4311 corrupt_frac_t_0p2_0p4=0.1982 acc_corrupt_t_0p4_0p6=0.6420 corrupt_frac_t_0p4_0p6=0.2031 acc_corrupt_t_0p6_0p8=0.8043 corrupt_frac_t_0p6_0p8=0.2058 acc_corrupt_t_0p8_1p0=0.9396 corrupt_frac_t_0p8_1p0=0.1967 out_w_norm=125.2086 out_g_norm=0.2555 loss_all=1.3825 init_gold_top10=0.4837 init_gold_top100=0.4874 +step=9400 micro_steps=18800 elapsed=78.1s lr=3.000000e-04 loss=2.5228 loss_recon=2.5228 loss_meanflow=0.0000 mean_model_t=0.5012 mean_corrupt_t=0.5012 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7780 corrupt_frac=0.5492 acc_corrupt=0.6106 loss_corrupt=2.5228 wrong_frac=0.4982 init_acc_corrupt=0.4671 acc_corrupt_t_0p0_0p2=0.2243 corrupt_frac_t_0p0_0p2=0.1966 acc_corrupt_t_0p2_0p4=0.4315 corrupt_frac_t_0p2_0p4=0.2038 acc_corrupt_t_0p4_0p6=0.6457 corrupt_frac_t_0p4_0p6=0.1944 acc_corrupt_t_0p6_0p8=0.8042 corrupt_frac_t_0p6_0p8=0.2031 acc_corrupt_t_0p8_1p0=0.9386 corrupt_frac_t_0p8_1p0=0.2020 out_w_norm=126.0196 out_g_norm=0.2763 loss_all=1.5902 init_gold_top10=0.4629 init_gold_top100=0.4665 +step=9500 micro_steps=19000 elapsed=78.1s lr=3.000000e-04 loss=2.5447 loss_recon=2.5447 loss_meanflow=0.0000 mean_model_t=0.4974 mean_corrupt_t=0.4974 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7743 corrupt_frac=0.5505 acc_corrupt=0.6047 loss_corrupt=2.5447 wrong_frac=0.5016 init_acc_corrupt=0.4632 acc_corrupt_t_0p0_0p2=0.2201 corrupt_frac_t_0p0_0p2=0.2000 acc_corrupt_t_0p2_0p4=0.4271 corrupt_frac_t_0p2_0p4=0.2041 acc_corrupt_t_0p4_0p6=0.6446 corrupt_frac_t_0p4_0p6=0.1975 acc_corrupt_t_0p6_0p8=0.8034 corrupt_frac_t_0p6_0p8=0.2045 acc_corrupt_t_0p8_1p0=0.9381 corrupt_frac_t_0p8_1p0=0.1939 out_w_norm=126.8466 out_g_norm=0.2487 loss_all=1.1039 init_gold_top10=0.5978 init_gold_top100=0.6020 +step=9600 micro_steps=19200 elapsed=78.0s lr=3.000000e-04 loss=2.5117 loss_recon=2.5117 loss_meanflow=0.0000 mean_model_t=0.4996 mean_corrupt_t=0.4996 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7782 corrupt_frac=0.5494 acc_corrupt=0.6104 loss_corrupt=2.5117 wrong_frac=0.5013 init_acc_corrupt=0.4642 acc_corrupt_t_0p0_0p2=0.2260 corrupt_frac_t_0p0_0p2=0.2035 acc_corrupt_t_0p2_0p4=0.4377 corrupt_frac_t_0p2_0p4=0.1983 acc_corrupt_t_0p4_0p6=0.6508 corrupt_frac_t_0p4_0p6=0.2005 acc_corrupt_t_0p6_0p8=0.8059 corrupt_frac_t_0p6_0p8=0.1983 acc_corrupt_t_0p8_1p0=0.9397 corrupt_frac_t_0p8_1p0=0.1994 out_w_norm=127.6655 out_g_norm=0.2472 loss_all=1.4244 init_gold_top10=0.4978 init_gold_top100=0.5038 +step=9700 micro_steps=19400 elapsed=78.1s lr=3.000000e-04 loss=2.5249 loss_recon=2.5249 loss_meanflow=0.0000 mean_model_t=0.5013 mean_corrupt_t=0.5013 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7754 corrupt_frac=0.5549 acc_corrupt=0.6087 loss_corrupt=2.5249 wrong_frac=0.5011 init_acc_corrupt=0.4639 acc_corrupt_t_0p0_0p2=0.2257 corrupt_frac_t_0p0_0p2=0.2057 acc_corrupt_t_0p2_0p4=0.4297 corrupt_frac_t_0p2_0p4=0.1964 acc_corrupt_t_0p4_0p6=0.6468 corrupt_frac_t_0p4_0p6=0.2019 acc_corrupt_t_0p6_0p8=0.8083 corrupt_frac_t_0p6_0p8=0.1903 acc_corrupt_t_0p8_1p0=0.9406 corrupt_frac_t_0p8_1p0=0.2057 out_w_norm=128.4954 out_g_norm=0.2428 loss_all=1.4383 init_gold_top10=0.5177 init_gold_top100=0.5252 +step=9800 micro_steps=19600 elapsed=78.3s lr=3.000000e-04 loss=2.4912 loss_recon=2.4912 loss_meanflow=0.0000 mean_model_t=0.5013 mean_corrupt_t=0.5013 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7795 corrupt_frac=0.5506 acc_corrupt=0.6137 loss_corrupt=2.4912 wrong_frac=0.4983 init_acc_corrupt=0.4680 acc_corrupt_t_0p0_0p2=0.2276 corrupt_frac_t_0p0_0p2=0.1971 acc_corrupt_t_0p2_0p4=0.4370 corrupt_frac_t_0p2_0p4=0.1987 acc_corrupt_t_0p4_0p6=0.6481 corrupt_frac_t_0p4_0p6=0.2029 acc_corrupt_t_0p6_0p8=0.8068 corrupt_frac_t_0p6_0p8=0.2000 acc_corrupt_t_0p8_1p0=0.9397 corrupt_frac_t_0p8_1p0=0.2013 out_w_norm=129.2970 out_g_norm=0.2454 loss_all=1.5075 init_gold_top10=0.4835 init_gold_top100=0.4881 +step=9900 micro_steps=19800 elapsed=77.5s lr=3.000000e-04 loss=2.5266 loss_recon=2.5266 loss_meanflow=0.0000 mean_model_t=0.5004 mean_corrupt_t=0.5004 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7765 corrupt_frac=0.5512 acc_corrupt=0.6085 loss_corrupt=2.5266 wrong_frac=0.5024 init_acc_corrupt=0.4624 acc_corrupt_t_0p0_0p2=0.2215 corrupt_frac_t_0p0_0p2=0.1971 acc_corrupt_t_0p2_0p4=0.4308 corrupt_frac_t_0p2_0p4=0.2067 acc_corrupt_t_0p4_0p6=0.6506 corrupt_frac_t_0p4_0p6=0.1989 acc_corrupt_t_0p6_0p8=0.8044 corrupt_frac_t_0p6_0p8=0.1996 acc_corrupt_t_0p8_1p0=0.9397 corrupt_frac_t_0p8_1p0=0.1978 out_w_norm=130.0938 out_g_norm=0.2402 loss_all=1.6813 init_gold_top10=0.4669 init_gold_top100=0.4731 +step=10000 micro_steps=20000 elapsed=77.6s lr=3.000000e-04 loss=2.4885 loss_recon=2.4885 loss_meanflow=0.0000 mean_model_t=0.4992 mean_corrupt_t=0.4992 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7786 corrupt_frac=0.5529 acc_corrupt=0.6135 loss_corrupt=2.4885 wrong_frac=0.5019 init_acc_corrupt=0.4631 acc_corrupt_t_0p0_0p2=0.2337 corrupt_frac_t_0p0_0p2=0.2033 acc_corrupt_t_0p2_0p4=0.4423 corrupt_frac_t_0p2_0p4=0.1992 acc_corrupt_t_0p4_0p6=0.6501 corrupt_frac_t_0p4_0p6=0.2027 acc_corrupt_t_0p6_0p8=0.8110 corrupt_frac_t_0p6_0p8=0.1921 acc_corrupt_t_0p8_1p0=0.9388 corrupt_frac_t_0p8_1p0=0.2027 out_w_norm=130.8969 out_g_norm=0.2409 loss_all=1.3735 init_gold_top10=0.5392 init_gold_top100=0.5441 +step=10100 micro_steps=20200 elapsed=81.0s lr=3.000000e-04 loss=2.4891 loss_recon=2.4891 loss_meanflow=0.0000 mean_model_t=0.4990 mean_corrupt_t=0.4990 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7791 corrupt_frac=0.5493 acc_corrupt=0.6116 loss_corrupt=2.4891 wrong_frac=0.5015 init_acc_corrupt=0.4635 acc_corrupt_t_0p0_0p2=0.2310 corrupt_frac_t_0p0_0p2=0.1999 acc_corrupt_t_0p2_0p4=0.4360 corrupt_frac_t_0p2_0p4=0.2031 acc_corrupt_t_0p4_0p6=0.6510 corrupt_frac_t_0p4_0p6=0.2033 acc_corrupt_t_0p6_0p8=0.8072 corrupt_frac_t_0p6_0p8=0.1935 acc_corrupt_t_0p8_1p0=0.9409 corrupt_frac_t_0p8_1p0=0.2002 out_w_norm=131.6763 out_g_norm=0.2390 loss_all=1.5713 init_gold_top10=0.4371 init_gold_top100=0.4436 +step=10200 micro_steps=20400 elapsed=78.6s lr=3.000000e-04 loss=2.4943 loss_recon=2.4943 loss_meanflow=0.0000 mean_model_t=0.4977 mean_corrupt_t=0.4977 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7803 corrupt_frac=0.5451 acc_corrupt=0.6106 loss_corrupt=2.4943 wrong_frac=0.5018 init_acc_corrupt=0.4636 acc_corrupt_t_0p0_0p2=0.2270 corrupt_frac_t_0p0_0p2=0.2020 acc_corrupt_t_0p2_0p4=0.4358 corrupt_frac_t_0p2_0p4=0.1970 acc_corrupt_t_0p4_0p6=0.6491 corrupt_frac_t_0p4_0p6=0.2031 acc_corrupt_t_0p6_0p8=0.8072 corrupt_frac_t_0p6_0p8=0.2030 acc_corrupt_t_0p8_1p0=0.9403 corrupt_frac_t_0p8_1p0=0.1949 out_w_norm=132.4425 out_g_norm=0.2349 loss_all=1.6068 init_gold_top10=0.4436 init_gold_top100=0.4512 +step=10300 micro_steps=20600 elapsed=78.5s lr=3.000000e-04 loss=2.4502 loss_recon=2.4502 loss_meanflow=0.0000 mean_model_t=0.4999 mean_corrupt_t=0.4999 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7815 corrupt_frac=0.5524 acc_corrupt=0.6183 loss_corrupt=2.4502 wrong_frac=0.4979 init_acc_corrupt=0.4676 acc_corrupt_t_0p0_0p2=0.2371 corrupt_frac_t_0p0_0p2=0.2013 acc_corrupt_t_0p2_0p4=0.4445 corrupt_frac_t_0p2_0p4=0.1964 acc_corrupt_t_0p4_0p6=0.6554 corrupt_frac_t_0p4_0p6=0.1958 acc_corrupt_t_0p6_0p8=0.8084 corrupt_frac_t_0p6_0p8=0.2094 acc_corrupt_t_0p8_1p0=0.9417 corrupt_frac_t_0p8_1p0=0.1972 out_w_norm=133.2187 out_g_norm=0.2617 loss_all=1.2587 init_gold_top10=0.5488 init_gold_top100=0.5551 +step=10400 micro_steps=20800 elapsed=78.1s lr=3.000000e-04 loss=2.5002 loss_recon=2.5002 loss_meanflow=0.0000 mean_model_t=0.4941 mean_corrupt_t=0.4941 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7770 corrupt_frac=0.5527 acc_corrupt=0.6103 loss_corrupt=2.5002 wrong_frac=0.5025 init_acc_corrupt=0.4626 acc_corrupt_t_0p0_0p2=0.2287 corrupt_frac_t_0p0_0p2=0.2081 acc_corrupt_t_0p2_0p4=0.4370 corrupt_frac_t_0p2_0p4=0.1917 acc_corrupt_t_0p4_0p6=0.6484 corrupt_frac_t_0p4_0p6=0.2027 acc_corrupt_t_0p6_0p8=0.8078 corrupt_frac_t_0p6_0p8=0.2007 acc_corrupt_t_0p8_1p0=0.9419 corrupt_frac_t_0p8_1p0=0.1968 out_w_norm=133.9914 out_g_norm=0.2355 loss_all=1.2520 init_gold_top10=0.5265 init_gold_top100=0.5309 +step=10500 micro_steps=21000 elapsed=78.1s lr=3.000000e-04 loss=2.4819 loss_recon=2.4819 loss_meanflow=0.0000 mean_model_t=0.4993 mean_corrupt_t=0.4993 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7802 corrupt_frac=0.5488 acc_corrupt=0.6139 loss_corrupt=2.4819 wrong_frac=0.4979 init_acc_corrupt=0.4672 acc_corrupt_t_0p0_0p2=0.2293 corrupt_frac_t_0p0_0p2=0.2014 acc_corrupt_t_0p2_0p4=0.4377 corrupt_frac_t_0p2_0p4=0.1987 acc_corrupt_t_0p4_0p6=0.6509 corrupt_frac_t_0p4_0p6=0.2017 acc_corrupt_t_0p6_0p8=0.8122 corrupt_frac_t_0p6_0p8=0.1966 acc_corrupt_t_0p8_1p0=0.9411 corrupt_frac_t_0p8_1p0=0.2016 out_w_norm=134.7821 out_g_norm=0.2354 loss_all=1.3908 init_gold_top10=0.4715 init_gold_top100=0.4756 +step=10600 micro_steps=21200 elapsed=78.2s lr=3.000000e-04 loss=2.4803 loss_recon=2.4803 loss_meanflow=0.0000 mean_model_t=0.5010 mean_corrupt_t=0.5010 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7792 corrupt_frac=0.5515 acc_corrupt=0.6136 loss_corrupt=2.4803 wrong_frac=0.4984 init_acc_corrupt=0.4679 acc_corrupt_t_0p0_0p2=0.2312 corrupt_frac_t_0p0_0p2=0.2007 acc_corrupt_t_0p2_0p4=0.4377 corrupt_frac_t_0p2_0p4=0.1978 acc_corrupt_t_0p4_0p6=0.6447 corrupt_frac_t_0p4_0p6=0.1965 acc_corrupt_t_0p6_0p8=0.8065 corrupt_frac_t_0p6_0p8=0.2007 acc_corrupt_t_0p8_1p0=0.9399 corrupt_frac_t_0p8_1p0=0.2043 out_w_norm=135.5821 out_g_norm=0.2384 loss_all=1.6395 init_gold_top10=0.4212 init_gold_top100=0.4283 +step=10700 micro_steps=21400 elapsed=78.3s lr=3.000000e-04 loss=2.4871 loss_recon=2.4871 loss_meanflow=0.0000 mean_model_t=0.5004 mean_corrupt_t=0.5004 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7796 corrupt_frac=0.5498 acc_corrupt=0.6128 loss_corrupt=2.4871 wrong_frac=0.4989 init_acc_corrupt=0.4667 acc_corrupt_t_0p0_0p2=0.2266 corrupt_frac_t_0p0_0p2=0.2026 acc_corrupt_t_0p2_0p4=0.4388 corrupt_frac_t_0p2_0p4=0.1937 acc_corrupt_t_0p4_0p6=0.6487 corrupt_frac_t_0p4_0p6=0.2003 acc_corrupt_t_0p6_0p8=0.8079 corrupt_frac_t_0p6_0p8=0.2048 acc_corrupt_t_0p8_1p0=0.9391 corrupt_frac_t_0p8_1p0=0.1987 out_w_norm=136.3883 out_g_norm=0.2421 loss_all=1.3143 init_gold_top10=0.5061 init_gold_top100=0.5105 +step=10800 micro_steps=21600 elapsed=78.5s lr=3.000000e-04 loss=2.5006 loss_recon=2.5006 loss_meanflow=0.0000 mean_model_t=0.4973 mean_corrupt_t=0.4973 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7802 corrupt_frac=0.5465 acc_corrupt=0.6116 loss_corrupt=2.5006 wrong_frac=0.5015 init_acc_corrupt=0.4636 acc_corrupt_t_0p0_0p2=0.2262 corrupt_frac_t_0p0_0p2=0.2018 acc_corrupt_t_0p2_0p4=0.4389 corrupt_frac_t_0p2_0p4=0.2011 acc_corrupt_t_0p4_0p6=0.6497 corrupt_frac_t_0p4_0p6=0.1977 acc_corrupt_t_0p6_0p8=0.8089 corrupt_frac_t_0p6_0p8=0.2004 acc_corrupt_t_0p8_1p0=0.9404 corrupt_frac_t_0p8_1p0=0.1991 out_w_norm=137.1830 out_g_norm=0.2383 loss_all=1.4582 init_gold_top10=0.4919 init_gold_top100=0.4992 +step=10900 micro_steps=21800 elapsed=77.4s lr=3.000000e-04 loss=2.4389 loss_recon=2.4389 loss_meanflow=0.0000 mean_model_t=0.5022 mean_corrupt_t=0.5022 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7821 corrupt_frac=0.5500 acc_corrupt=0.6176 loss_corrupt=2.4389 wrong_frac=0.4966 init_acc_corrupt=0.4696 acc_corrupt_t_0p0_0p2=0.2359 corrupt_frac_t_0p0_0p2=0.1957 acc_corrupt_t_0p2_0p4=0.4370 corrupt_frac_t_0p2_0p4=0.1981 acc_corrupt_t_0p4_0p6=0.6504 corrupt_frac_t_0p4_0p6=0.2028 acc_corrupt_t_0p6_0p8=0.8093 corrupt_frac_t_0p6_0p8=0.2005 acc_corrupt_t_0p8_1p0=0.9400 corrupt_frac_t_0p8_1p0=0.2029 out_w_norm=137.9841 out_g_norm=0.2304 loss_all=1.4441 init_gold_top10=0.5298 init_gold_top100=0.5363 +step=11000 micro_steps=22000 elapsed=77.5s lr=3.000000e-04 loss=2.4778 loss_recon=2.4778 loss_meanflow=0.0000 mean_model_t=0.4988 mean_corrupt_t=0.4988 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7800 corrupt_frac=0.5515 acc_corrupt=0.6143 loss_corrupt=2.4778 wrong_frac=0.5000 init_acc_corrupt=0.4655 acc_corrupt_t_0p0_0p2=0.2322 corrupt_frac_t_0p0_0p2=0.2000 acc_corrupt_t_0p2_0p4=0.4344 corrupt_frac_t_0p2_0p4=0.1967 acc_corrupt_t_0p4_0p6=0.6491 corrupt_frac_t_0p4_0p6=0.1994 acc_corrupt_t_0p6_0p8=0.8081 corrupt_frac_t_0p6_0p8=0.2051 acc_corrupt_t_0p8_1p0=0.9418 corrupt_frac_t_0p8_1p0=0.1988 out_w_norm=138.7526 out_g_norm=0.2246 loss_all=1.4903 init_gold_top10=0.4663 init_gold_top100=0.4731 +step=11100 micro_steps=22200 elapsed=80.8s lr=3.000000e-04 loss=2.4813 loss_recon=2.4813 loss_meanflow=0.0000 mean_model_t=0.5015 mean_corrupt_t=0.5015 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7807 corrupt_frac=0.5501 acc_corrupt=0.6145 loss_corrupt=2.4813 wrong_frac=0.4997 init_acc_corrupt=0.4664 acc_corrupt_t_0p0_0p2=0.2267 corrupt_frac_t_0p0_0p2=0.1991 acc_corrupt_t_0p2_0p4=0.4385 corrupt_frac_t_0p2_0p4=0.1956 acc_corrupt_t_0p4_0p6=0.6522 corrupt_frac_t_0p4_0p6=0.2068 acc_corrupt_t_0p6_0p8=0.8104 corrupt_frac_t_0p6_0p8=0.1984 acc_corrupt_t_0p8_1p0=0.9394 corrupt_frac_t_0p8_1p0=0.2002 out_w_norm=139.5021 out_g_norm=0.2246 loss_all=1.5176 init_gold_top10=0.5167 init_gold_top100=0.5222 +step=11200 micro_steps=22400 elapsed=78.4s lr=3.000000e-04 loss=2.5297 loss_recon=2.5297 loss_meanflow=0.0000 mean_model_t=0.4963 mean_corrupt_t=0.4963 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7770 corrupt_frac=0.5478 acc_corrupt=0.6074 loss_corrupt=2.5297 wrong_frac=0.5054 init_acc_corrupt=0.4595 acc_corrupt_t_0p0_0p2=0.2268 corrupt_frac_t_0p0_0p2=0.2035 acc_corrupt_t_0p2_0p4=0.4308 corrupt_frac_t_0p2_0p4=0.2060 acc_corrupt_t_0p4_0p6=0.6511 corrupt_frac_t_0p4_0p6=0.1954 acc_corrupt_t_0p6_0p8=0.8081 corrupt_frac_t_0p6_0p8=0.1972 acc_corrupt_t_0p8_1p0=0.9398 corrupt_frac_t_0p8_1p0=0.1978 out_w_norm=140.2809 out_g_norm=0.2267 loss_all=1.2737 init_gold_top10=0.5220 init_gold_top100=0.5279 +step=11300 micro_steps=22600 elapsed=78.4s lr=3.000000e-04 loss=2.4726 loss_recon=2.4726 loss_meanflow=0.0000 mean_model_t=0.5017 mean_corrupt_t=0.5017 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7798 corrupt_frac=0.5532 acc_corrupt=0.6147 loss_corrupt=2.4726 wrong_frac=0.4996 init_acc_corrupt=0.4664 acc_corrupt_t_0p0_0p2=0.2285 corrupt_frac_t_0p0_0p2=0.1976 acc_corrupt_t_0p2_0p4=0.4408 corrupt_frac_t_0p2_0p4=0.2018 acc_corrupt_t_0p4_0p6=0.6550 corrupt_frac_t_0p4_0p6=0.2042 acc_corrupt_t_0p6_0p8=0.8085 corrupt_frac_t_0p6_0p8=0.1970 acc_corrupt_t_0p8_1p0=0.9407 corrupt_frac_t_0p8_1p0=0.1994 out_w_norm=141.0322 out_g_norm=0.2224 loss_all=1.3936 init_gold_top10=0.5401 init_gold_top100=0.5439 +step=11400 micro_steps=22800 elapsed=78.5s lr=3.000000e-04 loss=2.4325 loss_recon=2.4325 loss_meanflow=0.0000 mean_model_t=0.5007 mean_corrupt_t=0.5007 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7839 corrupt_frac=0.5472 acc_corrupt=0.6187 loss_corrupt=2.4325 wrong_frac=0.4983 init_acc_corrupt=0.4668 acc_corrupt_t_0p0_0p2=0.2386 corrupt_frac_t_0p0_0p2=0.1940 acc_corrupt_t_0p2_0p4=0.4416 corrupt_frac_t_0p2_0p4=0.2073 acc_corrupt_t_0p4_0p6=0.6557 corrupt_frac_t_0p4_0p6=0.1952 acc_corrupt_t_0p6_0p8=0.8096 corrupt_frac_t_0p6_0p8=0.2050 acc_corrupt_t_0p8_1p0=0.9418 corrupt_frac_t_0p8_1p0=0.1984 out_w_norm=141.7729 out_g_norm=0.2223 loss_all=1.3529 init_gold_top10=0.4228 init_gold_top100=0.4299 +step=11500 micro_steps=23000 elapsed=78.5s lr=3.000000e-04 loss=2.4835 loss_recon=2.4835 loss_meanflow=0.0000 mean_model_t=0.4979 mean_corrupt_t=0.4979 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7798 corrupt_frac=0.5497 acc_corrupt=0.6133 loss_corrupt=2.4835 wrong_frac=0.5003 init_acc_corrupt=0.4642 acc_corrupt_t_0p0_0p2=0.2317 corrupt_frac_t_0p0_0p2=0.2010 acc_corrupt_t_0p2_0p4=0.4364 corrupt_frac_t_0p2_0p4=0.2047 acc_corrupt_t_0p4_0p6=0.6546 corrupt_frac_t_0p4_0p6=0.1943 acc_corrupt_t_0p6_0p8=0.8091 corrupt_frac_t_0p6_0p8=0.1994 acc_corrupt_t_0p8_1p0=0.9417 corrupt_frac_t_0p8_1p0=0.2006 out_w_norm=142.5034 out_g_norm=0.2241 loss_all=1.6209 init_gold_top10=0.4565 init_gold_top100=0.4632 +step=11600 micro_steps=23200 elapsed=78.3s lr=3.000000e-04 loss=2.4797 loss_recon=2.4797 loss_meanflow=0.0000 mean_model_t=0.4997 mean_corrupt_t=0.4997 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7805 corrupt_frac=0.5493 acc_corrupt=0.6135 loss_corrupt=2.4797 wrong_frac=0.5018 init_acc_corrupt=0.4634 acc_corrupt_t_0p0_0p2=0.2327 corrupt_frac_t_0p0_0p2=0.2052 acc_corrupt_t_0p2_0p4=0.4389 corrupt_frac_t_0p2_0p4=0.1999 acc_corrupt_t_0p4_0p6=0.6524 corrupt_frac_t_0p4_0p6=0.1933 acc_corrupt_t_0p6_0p8=0.8116 corrupt_frac_t_0p6_0p8=0.2032 acc_corrupt_t_0p8_1p0=0.9423 corrupt_frac_t_0p8_1p0=0.1985 out_w_norm=143.2474 out_g_norm=0.2225 loss_all=1.2418 init_gold_top10=0.5315 init_gold_top100=0.5367 +step=11700 micro_steps=23400 elapsed=78.2s lr=3.000000e-04 loss=2.4419 loss_recon=2.4419 loss_meanflow=0.0000 mean_model_t=0.4999 mean_corrupt_t=0.4999 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7846 corrupt_frac=0.5448 acc_corrupt=0.6180 loss_corrupt=2.4419 wrong_frac=0.4980 init_acc_corrupt=0.4673 acc_corrupt_t_0p0_0p2=0.2363 corrupt_frac_t_0p0_0p2=0.1983 acc_corrupt_t_0p2_0p4=0.4446 corrupt_frac_t_0p2_0p4=0.1998 acc_corrupt_t_0p4_0p6=0.6518 corrupt_frac_t_0p4_0p6=0.2025 acc_corrupt_t_0p6_0p8=0.8123 corrupt_frac_t_0p6_0p8=0.1976 acc_corrupt_t_0p8_1p0=0.9407 corrupt_frac_t_0p8_1p0=0.2018 out_w_norm=144.0088 out_g_norm=0.2239 loss_all=0.8972 init_gold_top10=0.5097 init_gold_top100=0.5175 +step=11800 micro_steps=23600 elapsed=78.4s lr=3.000000e-04 loss=2.4461 loss_recon=2.4461 loss_meanflow=0.0000 mean_model_t=0.5028 mean_corrupt_t=0.5028 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7827 corrupt_frac=0.5516 acc_corrupt=0.6185 loss_corrupt=2.4461 wrong_frac=0.4956 init_acc_corrupt=0.4697 acc_corrupt_t_0p0_0p2=0.2311 corrupt_frac_t_0p0_0p2=0.1903 acc_corrupt_t_0p2_0p4=0.4383 corrupt_frac_t_0p2_0p4=0.2025 acc_corrupt_t_0p4_0p6=0.6549 corrupt_frac_t_0p4_0p6=0.2054 acc_corrupt_t_0p6_0p8=0.8101 corrupt_frac_t_0p6_0p8=0.2031 acc_corrupt_t_0p8_1p0=0.9399 corrupt_frac_t_0p8_1p0=0.1987 out_w_norm=144.7678 out_g_norm=0.2226 loss_all=1.3459 init_gold_top10=0.5210 init_gold_top100=0.5247 +step=11900 micro_steps=23800 elapsed=77.4s lr=3.000000e-04 loss=2.4108 loss_recon=2.4108 loss_meanflow=0.0000 mean_model_t=0.5058 mean_corrupt_t=0.5058 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7869 corrupt_frac=0.5457 acc_corrupt=0.6222 loss_corrupt=2.4108 wrong_frac=0.4934 init_acc_corrupt=0.4726 acc_corrupt_t_0p0_0p2=0.2348 corrupt_frac_t_0p0_0p2=0.1945 acc_corrupt_t_0p2_0p4=0.4455 corrupt_frac_t_0p2_0p4=0.2000 acc_corrupt_t_0p4_0p6=0.6562 corrupt_frac_t_0p4_0p6=0.1999 acc_corrupt_t_0p6_0p8=0.8114 corrupt_frac_t_0p6_0p8=0.1967 acc_corrupt_t_0p8_1p0=0.9416 corrupt_frac_t_0p8_1p0=0.2088 out_w_norm=145.5224 out_g_norm=0.2164 loss_all=1.4347 init_gold_top10=0.5000 init_gold_top100=0.5049 +step=12000 micro_steps=24000 elapsed=68.7s lr=3.000000e-04 loss=2.5106 loss_recon=2.5106 loss_meanflow=0.0000 mean_model_t=0.4956 mean_corrupt_t=0.4956 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7760 corrupt_frac=0.5523 acc_corrupt=0.6080 loss_corrupt=2.5106 wrong_frac=0.5047 init_acc_corrupt=0.4601 acc_corrupt_t_0p0_0p2=0.2233 corrupt_frac_t_0p0_0p2=0.2083 acc_corrupt_t_0p2_0p4=0.4385 corrupt_frac_t_0p2_0p4=0.2015 acc_corrupt_t_0p4_0p6=0.6525 corrupt_frac_t_0p4_0p6=0.1936 acc_corrupt_t_0p6_0p8=0.8080 corrupt_frac_t_0p6_0p8=0.1979 acc_corrupt_t_0p8_1p0=0.9406 corrupt_frac_t_0p8_1p0=0.1987 out_w_norm=146.2869 out_g_norm=0.2269 loss_all=1.2314 init_gold_top10=0.5385 init_gold_top100=0.5414 +step=12100 micro_steps=24200 elapsed=77.3s lr=3.000000e-04 loss=2.4713 loss_recon=2.4713 loss_meanflow=0.0000 mean_model_t=0.4976 mean_corrupt_t=0.4976 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7820 corrupt_frac=0.5460 acc_corrupt=0.6138 loss_corrupt=2.4713 wrong_frac=0.5021 init_acc_corrupt=0.4633 acc_corrupt_t_0p0_0p2=0.2295 corrupt_frac_t_0p0_0p2=0.2051 acc_corrupt_t_0p2_0p4=0.4446 corrupt_frac_t_0p2_0p4=0.1984 acc_corrupt_t_0p4_0p6=0.6553 corrupt_frac_t_0p4_0p6=0.2009 acc_corrupt_t_0p6_0p8=0.8104 corrupt_frac_t_0p6_0p8=0.1962 acc_corrupt_t_0p8_1p0=0.9422 corrupt_frac_t_0p8_1p0=0.1994 out_w_norm=147.0443 out_g_norm=0.2161 loss_all=1.3617 init_gold_top10=0.5240 init_gold_top100=0.5275 +step=12200 micro_steps=24400 elapsed=71.2s lr=3.000000e-04 loss=2.4591 loss_recon=2.4591 loss_meanflow=0.0000 mean_model_t=0.5026 mean_corrupt_t=0.5026 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7834 corrupt_frac=0.5456 acc_corrupt=0.6165 loss_corrupt=2.4591 wrong_frac=0.4979 init_acc_corrupt=0.4680 acc_corrupt_t_0p0_0p2=0.2308 corrupt_frac_t_0p0_0p2=0.1925 acc_corrupt_t_0p2_0p4=0.4384 corrupt_frac_t_0p2_0p4=0.2010 acc_corrupt_t_0p4_0p6=0.6483 corrupt_frac_t_0p4_0p6=0.2042 acc_corrupt_t_0p6_0p8=0.8080 corrupt_frac_t_0p6_0p8=0.2029 acc_corrupt_t_0p8_1p0=0.9410 corrupt_frac_t_0p8_1p0=0.1993 out_w_norm=147.7654 out_g_norm=0.2292 loss_all=1.5232 init_gold_top10=0.4903 init_gold_top100=0.4971 +step=12300 micro_steps=24600 elapsed=55.1s lr=3.000000e-04 loss=2.4368 loss_recon=2.4368 loss_meanflow=0.0000 mean_model_t=0.5014 mean_corrupt_t=0.5014 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7835 corrupt_frac=0.5507 acc_corrupt=0.6193 loss_corrupt=2.4368 wrong_frac=0.4963 init_acc_corrupt=0.4691 acc_corrupt_t_0p0_0p2=0.2336 corrupt_frac_t_0p0_0p2=0.1973 acc_corrupt_t_0p2_0p4=0.4379 corrupt_frac_t_0p2_0p4=0.1989 acc_corrupt_t_0p4_0p6=0.6565 corrupt_frac_t_0p4_0p6=0.2001 acc_corrupt_t_0p6_0p8=0.8108 corrupt_frac_t_0p6_0p8=0.1959 acc_corrupt_t_0p8_1p0=0.9429 corrupt_frac_t_0p8_1p0=0.2077 out_w_norm=148.4982 out_g_norm=0.2128 loss_all=1.2771 init_gold_top10=0.4864 init_gold_top100=0.4914 +step=12400 micro_steps=24800 elapsed=66.4s lr=3.000000e-04 loss=2.4806 loss_recon=2.4806 loss_meanflow=0.0000 mean_model_t=0.4981 mean_corrupt_t=0.4981 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7804 corrupt_frac=0.5491 acc_corrupt=0.6130 loss_corrupt=2.4806 wrong_frac=0.5037 init_acc_corrupt=0.4613 acc_corrupt_t_0p0_0p2=0.2322 corrupt_frac_t_0p0_0p2=0.2032 acc_corrupt_t_0p2_0p4=0.4355 corrupt_frac_t_0p2_0p4=0.2004 acc_corrupt_t_0p4_0p6=0.6539 corrupt_frac_t_0p4_0p6=0.1990 acc_corrupt_t_0p6_0p8=0.8141 corrupt_frac_t_0p6_0p8=0.2036 acc_corrupt_t_0p8_1p0=0.9425 corrupt_frac_t_0p8_1p0=0.1938 out_w_norm=149.2032 out_g_norm=0.2129 loss_all=1.1427 init_gold_top10=0.5564 init_gold_top100=0.5591 +step=12500 micro_steps=25000 elapsed=79.1s lr=3.000000e-04 loss=2.4556 loss_recon=2.4556 loss_meanflow=0.0000 mean_model_t=0.5002 mean_corrupt_t=0.5002 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7821 corrupt_frac=0.5502 acc_corrupt=0.6169 loss_corrupt=2.4556 wrong_frac=0.4992 init_acc_corrupt=0.4665 acc_corrupt_t_0p0_0p2=0.2335 corrupt_frac_t_0p0_0p2=0.2009 acc_corrupt_t_0p2_0p4=0.4416 corrupt_frac_t_0p2_0p4=0.1986 acc_corrupt_t_0p4_0p6=0.6536 corrupt_frac_t_0p4_0p6=0.1989 acc_corrupt_t_0p6_0p8=0.8098 corrupt_frac_t_0p6_0p8=0.1979 acc_corrupt_t_0p8_1p0=0.9422 corrupt_frac_t_0p8_1p0=0.2038 out_w_norm=149.9173 out_g_norm=0.2124 loss_all=1.2746 init_gold_top10=0.5372 init_gold_top100=0.5432 +step=12600 micro_steps=25200 elapsed=79.2s lr=3.000000e-04 loss=2.4270 loss_recon=2.4270 loss_meanflow=0.0000 mean_model_t=0.5021 mean_corrupt_t=0.5021 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7828 corrupt_frac=0.5529 acc_corrupt=0.6197 loss_corrupt=2.4270 wrong_frac=0.4970 init_acc_corrupt=0.4691 acc_corrupt_t_0p0_0p2=0.2317 corrupt_frac_t_0p0_0p2=0.1943 acc_corrupt_t_0p2_0p4=0.4417 corrupt_frac_t_0p2_0p4=0.1985 acc_corrupt_t_0p4_0p6=0.6578 corrupt_frac_t_0p4_0p6=0.2037 acc_corrupt_t_0p6_0p8=0.8096 corrupt_frac_t_0p6_0p8=0.2041 acc_corrupt_t_0p8_1p0=0.9414 corrupt_frac_t_0p8_1p0=0.1995 out_w_norm=150.6348 out_g_norm=0.2119 loss_all=1.7182 init_gold_top10=0.4854 init_gold_top100=0.4899 +step=12700 micro_steps=25400 elapsed=79.1s lr=3.000000e-04 loss=2.4663 loss_recon=2.4663 loss_meanflow=0.0000 mean_model_t=0.5010 mean_corrupt_t=0.5010 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7814 corrupt_frac=0.5508 acc_corrupt=0.6156 loss_corrupt=2.4663 wrong_frac=0.4995 init_acc_corrupt=0.4659 acc_corrupt_t_0p0_0p2=0.2333 corrupt_frac_t_0p0_0p2=0.1985 acc_corrupt_t_0p2_0p4=0.4370 corrupt_frac_t_0p2_0p4=0.1998 acc_corrupt_t_0p4_0p6=0.6540 corrupt_frac_t_0p4_0p6=0.2001 acc_corrupt_t_0p6_0p8=0.8088 corrupt_frac_t_0p6_0p8=0.2002 acc_corrupt_t_0p8_1p0=0.9396 corrupt_frac_t_0p8_1p0=0.2014 out_w_norm=151.3602 out_g_norm=0.2115 loss_all=1.6347 init_gold_top10=0.5069 init_gold_top100=0.5128 +step=12800 micro_steps=25600 elapsed=79.3s lr=3.000000e-04 loss=2.4877 loss_recon=2.4877 loss_meanflow=0.0000 mean_model_t=0.4976 mean_corrupt_t=0.4976 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7768 corrupt_frac=0.5538 acc_corrupt=0.6131 loss_corrupt=2.4877 wrong_frac=0.5022 init_acc_corrupt=0.4637 acc_corrupt_t_0p0_0p2=0.2316 corrupt_frac_t_0p0_0p2=0.2065 acc_corrupt_t_0p2_0p4=0.4390 corrupt_frac_t_0p2_0p4=0.1948 acc_corrupt_t_0p4_0p6=0.6519 corrupt_frac_t_0p4_0p6=0.1989 acc_corrupt_t_0p6_0p8=0.8091 corrupt_frac_t_0p6_0p8=0.1978 acc_corrupt_t_0p8_1p0=0.9405 corrupt_frac_t_0p8_1p0=0.2021 out_w_norm=152.0712 out_g_norm=0.2264 loss_all=1.5618 init_gold_top10=0.4786 init_gold_top100=0.4876 +step=12900 micro_steps=25800 elapsed=78.4s lr=3.000000e-04 loss=2.4714 loss_recon=2.4714 loss_meanflow=0.0000 mean_model_t=0.5013 mean_corrupt_t=0.5013 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.7841 corrupt_frac=0.5447 acc_corrupt=0.6166 loss_corrupt=2.4714 wrong_frac=0.4989 init_acc_corrupt=0.4668 acc_corrupt_t_0p0_0p2=0.2307 corrupt_frac_t_0p0_0p2=0.2016 acc_corrupt_t_0p2_0p4=0.4396 corrupt_frac_t_0p2_0p4=0.1965 acc_corrupt_t_0p4_0p6=0.6569 corrupt_frac_t_0p4_0p6=0.1995 acc_corrupt_t_0p6_0p8=0.8118 corrupt_frac_t_0p6_0p8=0.2045 acc_corrupt_t_0p8_1p0=0.9433 corrupt_frac_t_0p8_1p0=0.1979 out_w_norm=152.8020 out_g_norm=0.2195 loss_all=1.3889 init_gold_top10=0.5061 init_gold_top100=0.5112 +Terminated diff --git a/LTA_openwebtext_dualt/logs/lta_owt_gpt2cached_len1024_rollout1_p1_ddit768x12_gbs512_8gpu_1m_20260513_143213.log b/LTA_openwebtext_dualt/logs/lta_owt_gpt2cached_len1024_rollout1_p1_ddit768x12_gbs512_8gpu_1m_20260513_143213.log new file mode 100644 index 0000000000000000000000000000000000000000..7ec1e1eebc22bb68149b101848c248245765b958 --- /dev/null +++ b/LTA_openwebtext_dualt/logs/lta_owt_gpt2cached_len1024_rollout1_p1_ddit768x12_gbs512_8gpu_1m_20260513_143213.log @@ -0,0 +1,924 @@ +t-20260513223132-g9wrc-worker-0:10255:10255 [0] NCCL INFO NCCL_SOCKET_IFNAME set by 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NCCL_TOPO_FILE set by environment to /var/run/nvidia-topologyd/virtualTopology.xml +t-20260513223132-g9wrc-worker-0:10260:10328 [5] NCCL INFO NCCL_TOPO_FILE set by environment to /var/run/nvidia-topologyd/virtualTopology.xml +t-20260513223132-g9wrc-worker-0:10261:10329 [6] NCCL INFO Setting affinity for GPU 6 to 0fffff,ffffffff,ffffffff,fc000000,00000000,00000000 +t-20260513223132-g9wrc-worker-0:10261:10329 [6] NCCL INFO NVLS multicast support is available on dev 6 +t-20260513223132-g9wrc-worker-0:10259:10332 [4] NCCL INFO Setting affinity for GPU 4 to 0fffff,ffffffff,ffffffff,fc000000,00000000,00000000 +t-20260513223132-g9wrc-worker-0:10259:10332 [4] NCCL INFO NVLS multicast support is available on dev 4 +t-20260513223132-g9wrc-worker-0:10260:10328 [5] NCCL INFO Setting affinity for GPU 5 to 0fffff,ffffffff,ffffffff,fc000000,00000000,00000000 +t-20260513223132-g9wrc-worker-0:10258:10333 [3] NCCL INFO Setting affinity for GPU 3 to 03ffffff,ffffffff,ffffffff +t-20260513223132-g9wrc-worker-0:10258:10333 [3] NCCL INFO NVLS multicast support is available on dev 3 +t-20260513223132-g9wrc-worker-0:10257:10334 [2] NCCL INFO Setting affinity for GPU 2 to 03ffffff,ffffffff,ffffffff +t-20260513223132-g9wrc-worker-0:10257:10334 [2] NCCL INFO NVLS multicast support is available on dev 2 +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO Setting affinity for GPU 0 to 03ffffff,ffffffff,ffffffff +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO NVLS multicast support is available on dev 0 +t-20260513223132-g9wrc-worker-0:10260:10328 [5] NCCL INFO NVLS multicast support is available on dev 5 +t-20260513223132-g9wrc-worker-0:10256:10331 [1] NCCL INFO Setting affinity for GPU 1 to 03ffffff,ffffffff,ffffffff +t-20260513223132-g9wrc-worker-0:10262:10330 [7] NCCL INFO Setting affinity for GPU 7 to 0fffff,ffffffff,ffffffff,fc000000,00000000,00000000 +t-20260513223132-g9wrc-worker-0:10262:10330 [7] NCCL INFO NVLS multicast support is available on dev 7 +t-20260513223132-g9wrc-worker-0:10256:10331 [1] NCCL INFO NVLS multicast support is available on dev 1 +t-20260513223132-g9wrc-worker-0:10258:10333 [3] NCCL INFO comm 0xb5ef120 rank 3 nRanks 8 nNodes 1 localRanks 8 localRank 3 MNNVL 0 +t-20260513223132-g9wrc-worker-0:10257:10334 [2] NCCL INFO comm 0xb0afb80 rank 2 nRanks 8 nNodes 1 localRanks 8 localRank 2 MNNVL 0 +t-20260513223132-g9wrc-worker-0:10262:10330 [7] NCCL INFO comm 0xab21280 rank 7 nRanks 8 nNodes 1 localRanks 8 localRank 7 MNNVL 0 +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO comm 0xb5c2750 rank 0 nRanks 8 nNodes 1 localRanks 8 localRank 0 MNNVL 0 +t-20260513223132-g9wrc-worker-0:10256:10331 [1] NCCL INFO comm 0x9e75e40 rank 1 nRanks 8 nNodes 1 localRanks 8 localRank 1 MNNVL 0 +t-20260513223132-g9wrc-worker-0:10261:10329 [6] NCCL INFO comm 0xb044c70 rank 6 nRanks 8 nNodes 1 localRanks 8 localRank 6 MNNVL 0 +t-20260513223132-g9wrc-worker-0:10260:10328 [5] NCCL INFO comm 0xa89c920 rank 5 nRanks 8 nNodes 1 localRanks 8 localRank 5 MNNVL 0 +t-20260513223132-g9wrc-worker-0:10259:10332 [4] NCCL INFO comm 0xb1d1a20 rank 4 nRanks 8 nNodes 1 localRanks 8 localRank 4 MNNVL 0 +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO Channel 00/24 : 0 1 2 3 4 5 6 7 +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO Channel 01/24 : 0 1 2 3 4 5 6 7 +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO Channel 02/24 : 0 1 2 3 4 5 6 7 +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO Channel 03/24 : 0 1 2 3 4 5 6 7 +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO Channel 04/24 : 0 1 2 3 4 5 6 7 +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +t-20260513223132-g9wrc-worker-0:10262:10330 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +t-20260513223132-g9wrc-worker-0:10259:10332 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +t-20260513223132-g9wrc-worker-0:10262:10330 [7] NCCL INFO P2P Chunksize set to 524288 +t-20260513223132-g9wrc-worker-0:10259:10332 [4] NCCL INFO P2P Chunksize set to 524288 +t-20260513223132-g9wrc-worker-0:10258:10333 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +t-20260513223132-g9wrc-worker-0:10258:10333 [3] NCCL INFO P2P Chunksize set to 524288 +t-20260513223132-g9wrc-worker-0:10257:10334 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +t-20260513223132-g9wrc-worker-0:10256:10331 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +t-20260513223132-g9wrc-worker-0:10261:10329 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +t-20260513223132-g9wrc-worker-0:10260:10328 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +t-20260513223132-g9wrc-worker-0:10256:10331 [1] NCCL INFO P2P Chunksize set to 524288 +t-20260513223132-g9wrc-worker-0:10257:10334 [2] NCCL INFO P2P Chunksize set to 524288 +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +t-20260513223132-g9wrc-worker-0:10261:10329 [6] NCCL INFO P2P Chunksize set to 524288 +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +t-20260513223132-g9wrc-worker-0:10260:10328 [5] NCCL INFO P2P Chunksize set to 524288 +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO P2P Chunksize set to 524288 +t-20260513223132-g9wrc-worker-0:10257:10408 [2] NCCL INFO [Proxy Service UDS] Device 2 CPU core 4 +t-20260513223132-g9wrc-worker-0:10257:10407 [2] NCCL INFO [Proxy Service] Device 2 CPU core 2 +t-20260513223132-g9wrc-worker-0:10256:10409 [1] NCCL INFO [Proxy Service] Device 1 CPU core 2 +t-20260513223132-g9wrc-worker-0:10256:10410 [1] NCCL INFO [Proxy Service UDS] Device 1 CPU core 4 +t-20260513223132-g9wrc-worker-0:10261:10411 [6] NCCL INFO [Proxy Service] Device 6 CPU core 92 +t-20260513223132-g9wrc-worker-0:10261:10412 [6] NCCL INFO [Proxy Service UDS] Device 6 CPU core 96 +t-20260513223132-g9wrc-worker-0:10260:10414 [5] NCCL INFO [Proxy Service] Device 5 CPU core 92 +t-20260513223132-g9wrc-worker-0:10259:10415 [4] NCCL INFO [Proxy Service] Device 4 CPU core 96 +t-20260513223132-g9wrc-worker-0:10262:10413 [7] NCCL INFO [Proxy Service] Device 7 CPU core 106 +t-20260513223132-g9wrc-worker-0:10262:10416 [7] NCCL INFO [Proxy Service UDS] Device 7 CPU core 108 +t-20260513223132-g9wrc-worker-0:10259:10417 [4] NCCL INFO [Proxy Service UDS] Device 4 CPU core 98 +t-20260513223132-g9wrc-worker-0:10260:10419 [5] NCCL INFO [Proxy Service UDS] Device 5 CPU core 100 +t-20260513223132-g9wrc-worker-0:10258:10418 [3] NCCL INFO [Proxy Service] Device 3 CPU core 55 +t-20260513223132-g9wrc-worker-0:10258:10420 [3] NCCL INFO [Proxy Service UDS] Device 3 CPU core 56 +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO Check P2P Type intraNodeP2pSupport 1 directMode 0 +t-20260513223132-g9wrc-worker-0:10255:10421 [0] NCCL INFO [Proxy Service] Device 0 CPU core 86 +t-20260513223132-g9wrc-worker-0:10255:10422 [0] NCCL INFO [Proxy Service UDS] Device 0 CPU core 88 +t-20260513223132-g9wrc-worker-0:10257:10334 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +t-20260513223132-g9wrc-worker-0:10257:10334 [2] NCCL INFO 24 coll channels, 24 collnet channels, 16 nvls channels, 32 p2p channels, 32 p2p channels per peer +t-20260513223132-g9wrc-worker-0:10258:10333 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +t-20260513223132-g9wrc-worker-0:10258:10333 [3] NCCL INFO 24 coll channels, 24 collnet channels, 16 nvls channels, 32 p2p channels, 32 p2p channels per peer +t-20260513223132-g9wrc-worker-0:10259:10332 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +t-20260513223132-g9wrc-worker-0:10259:10332 [4] NCCL INFO 24 coll channels, 24 collnet channels, 16 nvls channels, 32 p2p channels, 32 p2p channels per peer +t-20260513223132-g9wrc-worker-0:10260:10328 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +t-20260513223132-g9wrc-worker-0:10260:10328 [5] NCCL INFO 24 coll channels, 24 collnet channels, 16 nvls channels, 32 p2p channels, 32 p2p channels per peer +t-20260513223132-g9wrc-worker-0:10261:10329 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +t-20260513223132-g9wrc-worker-0:10261:10329 [6] NCCL INFO 24 coll channels, 24 collnet channels, 16 nvls channels, 32 p2p channels, 32 p2p channels per peer +t-20260513223132-g9wrc-worker-0:10262:10330 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO 24 coll channels, 24 collnet channels, 16 nvls channels, 32 p2p channels, 32 p2p channels per peer +t-20260513223132-g9wrc-worker-0:10262:10330 [7] NCCL INFO 24 coll channels, 24 collnet channels, 16 nvls channels, 32 p2p channels, 32 p2p channels per peer +t-20260513223132-g9wrc-worker-0:10256:10331 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +t-20260513223132-g9wrc-worker-0:10256:10331 [1] NCCL INFO 24 coll channels, 24 collnet channels, 16 nvls channels, 32 p2p channels, 32 p2p channels per peer +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO CC Off, workFifoBytes 1048576 +t-20260513223132-g9wrc-worker-0:10257:10334 [2] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v4 symbol. +t-20260513223132-g9wrc-worker-0:10256:10331 [1] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v4 symbol. +t-20260513223132-g9wrc-worker-0:10260:10328 [5] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v4 symbol. +t-20260513223132-g9wrc-worker-0:10262:10330 [7] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v4 symbol. +t-20260513223132-g9wrc-worker-0:10257:10334 [2] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v3 symbol. +t-20260513223132-g9wrc-worker-0:10256:10331 [1] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v3 symbol. +t-20260513223132-g9wrc-worker-0:10260:10328 [5] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v3 symbol. +t-20260513223132-g9wrc-worker-0:10259:10332 [4] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v4 symbol. +t-20260513223132-g9wrc-worker-0:10257:10334 [2] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v2 symbol, using internal tuner instead. +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v4 symbol. +t-20260513223132-g9wrc-worker-0:10258:10333 [3] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v4 symbol. +t-20260513223132-g9wrc-worker-0:10259:10332 [4] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v3 symbol. +t-20260513223132-g9wrc-worker-0:10262:10330 [7] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v3 symbol. +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v3 symbol. +t-20260513223132-g9wrc-worker-0:10260:10328 [5] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v2 symbol, using internal tuner instead. +t-20260513223132-g9wrc-worker-0:10256:10331 [1] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v2 symbol, using internal tuner instead. +t-20260513223132-g9wrc-worker-0:10262:10330 [7] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v2 symbol, using internal tuner instead. +t-20260513223132-g9wrc-worker-0:10261:10329 [6] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v4 symbol. +t-20260513223132-g9wrc-worker-0:10256:10331 [1] NCCL INFO ncclCommInitRankConfig comm 0x9e75e40 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 67020 commId 0x93a3726e358b8ce4 - Init COMPLETE +t-20260513223132-g9wrc-worker-0:10261:10329 [6] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v3 symbol. +t-20260513223132-g9wrc-worker-0:10261:10329 [6] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v2 symbol, using internal tuner instead. +t-20260513223132-g9wrc-worker-0:10257:10334 [2] NCCL INFO ncclCommInitRankConfig comm 0xb0afb80 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 69020 commId 0x93a3726e358b8ce4 - Init COMPLETE +t-20260513223132-g9wrc-worker-0:10259:10332 [4] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v2 symbol, using internal tuner instead. +t-20260513223132-g9wrc-worker-0:10258:10333 [3] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v3 symbol. +t-20260513223132-g9wrc-worker-0:10261:10329 [6] NCCL INFO ncclCommInitRankConfig comm 0xb044c70 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId 73020 commId 0x93a3726e358b8ce4 - Init COMPLETE +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v2 symbol, using internal tuner instead. +t-20260513223132-g9wrc-worker-0:10260:10328 [5] NCCL INFO ncclCommInitRankConfig comm 0xa89c920 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 71020 commId 0x93a3726e358b8ce4 - Init COMPLETE +t-20260513223132-g9wrc-worker-0:10262:10330 [7] NCCL INFO ncclCommInitRankConfig comm 0xab21280 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId 75020 commId 0x93a3726e358b8ce4 - Init COMPLETE +t-20260513223132-g9wrc-worker-0:10256:10331 [1] NCCL INFO Init timings - ncclCommInitRankConfig: rank 1 nranks 8 total 2.03 (kernels 0.18, alloc 0.92, bootstrap 0.07, allgathers 0.00, topo 0.55, graphs 0.01, connections 0.28, rest 0.02) +t-20260513223132-g9wrc-worker-0:10259:10332 [4] NCCL INFO ncclCommInitRankConfig comm 0xb1d1a20 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 6f020 commId 0x93a3726e358b8ce4 - Init COMPLETE +t-20260513223132-g9wrc-worker-0:10257:10334 [2] NCCL INFO Init timings - ncclCommInitRankConfig: rank 2 nranks 8 total 2.01 (kernels 0.30, alloc 0.84, bootstrap 0.01, allgathers 0.01, topo 0.55, graphs 0.01, connections 0.28, rest 0.02) +t-20260513223132-g9wrc-worker-0:10258:10333 [3] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v2 symbol, using internal tuner instead. +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO ncclCommInitRankConfig comm 0xb5c2750 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 65040 commId 0x93a3726e358b8ce4 - Init COMPLETE +t-20260513223132-g9wrc-worker-0:10261:10329 [6] NCCL INFO Init timings - ncclCommInitRankConfig: rank 6 nranks 8 total 2.04 (kernels 0.18, alloc 0.91, bootstrap 0.09, allgathers 0.01, topo 0.55, graphs 0.01, connections 0.27, rest 0.02) +t-20260513223132-g9wrc-worker-0:10260:10328 [5] NCCL INFO Init timings - ncclCommInitRankConfig: rank 5 nranks 8 total 2.06 (kernels 0.18, alloc 0.88, bootstrap 0.14, allgathers 0.00, topo 0.55, graphs 0.01, connections 0.26, rest 0.03) +t-20260513223132-g9wrc-worker-0:10262:10330 [7] NCCL INFO Init timings - ncclCommInitRankConfig: rank 7 nranks 8 total 2.03 (kernels 0.18, alloc 0.92, bootstrap 0.07, allgathers 0.01, topo 0.55, graphs 0.01, connections 0.26, rest 0.03) +t-20260513223132-g9wrc-worker-0:10258:10333 [3] NCCL INFO ncclCommInitRankConfig comm 0xb5ef120 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 6b020 commId 0x93a3726e358b8ce4 - Init COMPLETE +t-20260513223132-g9wrc-worker-0:10259:10332 [4] NCCL INFO Init timings - ncclCommInitRankConfig: rank 4 nranks 8 total 2.02 (kernels 0.39, alloc 0.77, bootstrap 0.00, allgathers 0.01, topo 0.55, graphs 0.01, connections 0.26, rest 0.03) +t-20260513223132-g9wrc-worker-0:10255:10327 [0] NCCL INFO Init timings - ncclCommInitRankConfig: rank 0 nranks 8 total 2.09 (kernels 0.18, alloc 0.71, bootstrap 0.35, allgathers 0.01, topo 0.55, graphs 0.01, connections 0.26, rest 0.03) +t-20260513223132-g9wrc-worker-0:10258:10333 [3] NCCL INFO Init timings - ncclCommInitRankConfig: rank 3 nranks 8 total 2.01 (kernels 0.23, alloc 0.90, bootstrap 0.03, allgathers 0.01, topo 0.55, graphs 0.01, connections 0.26, rest 0.03) +t-20260513223132-g9wrc-worker-0:10261:10423 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10259:10424 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10261:10423 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10259:10424 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10261:10423 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10258:10426 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10259:10424 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10261:10423 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10255:10425 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10258:10426 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10259:10424 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10261:10423 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10255:10425 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10258:10426 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10259:10424 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10261:10423 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10255:10425 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10258:10426 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10259:10424 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10257:10428 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10261:10423 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10255:10425 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10258:10426 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10259:10424 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10257:10428 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10261:10423 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10255:10425 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10260:10429 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10258:10426 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10256:10427 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10259:10424 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10257:10428 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10261:10423 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10255:10425 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10260:10429 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10258:10426 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10256:10427 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10262:10430 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10259:10424 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10257:10428 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10261:10423 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10255:10425 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10260:10429 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10258:10426 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10256:10427 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10262:10430 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10259:10424 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10257:10428 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10261:10423 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10255:10425 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10260:10429 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10258:10426 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10256:10427 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10262:10430 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10259:10424 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10257:10428 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10261:10423 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10255:10425 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10260:10429 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10258:10426 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10256:10427 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10262:10430 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10259:10424 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10257:10428 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10261:10423 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10255:10425 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10260:10429 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10258:10426 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10256:10427 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10262:10430 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10259:10424 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10257:10428 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10261:10423 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10255:10425 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10260:10429 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10258:10426 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10256:10427 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10262:10430 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10259:10424 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10257:10428 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10261:10423 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10255:10425 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10260:10429 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10258:10426 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10256:10427 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10262:10430 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10259:10424 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10257:10428 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10261:10423 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10255:10425 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10260:10429 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10258:10426 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10256:10427 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10262:10430 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10259:10424 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10257:10428 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10261:10423 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10255:10425 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10260:10429 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10258:10426 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10256:10427 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10262:10430 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10259:10424 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10257:10428 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10261:10423 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10255:10425 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10260:10429 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10258:10426 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10256:10427 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10262:10430 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10259:10424 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10257:10428 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via 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: 7[7] -> 0[0] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10262:10430 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM +t-20260513223132-g9wrc-worker-0:10258:10426 [3] NCCL INFO Connected all rings, use ring PXN 0 GDR 1 +t-20260513223132-g9wrc-worker-0:10257:10428 [2] NCCL INFO Connected all rings, use ring PXN 0 GDR 1 +t-20260513223132-g9wrc-worker-0:10256:10427 [1] NCCL INFO Connected all rings, use ring PXN 0 GDR 1 +t-20260513223132-g9wrc-worker-0:10261:10423 [6] NCCL INFO Connected all rings, use ring PXN 0 GDR 1 +t-20260513223132-g9wrc-worker-0:10262:10430 [7] NCCL INFO Connected all rings, use ring PXN 0 GDR 1 +t-20260513223132-g9wrc-worker-0:10260:10429 [5] NCCL INFO Connected all rings, use ring PXN 0 GDR 1 +t-20260513223132-g9wrc-worker-0:10259:10424 [4] NCCL INFO Connected all rings, use ring PXN 0 GDR 1 +t-20260513223132-g9wrc-worker-0:10255:10425 [0] NCCL INFO Connected all rings, use ring PXN 0 GDR 1 +{ + "device": "cuda:0", + "rank": 0, + "world_size": 8, + "samples": "owt_cached_chunks:8734897", + "vocab_size": 50257, + "tokenizer_vocab_size": 50257, + "save_dir": "runs/lta_owt_gpt2cached_len1024_rollout1_p1_ddit768x12_gbs512_8gpu_1m_20260513_143213", + "batch_size": 32, + "grad_accum": 2, + "effective_batch_size": 512, + "global_batch_size": 512, + "lr_schedule": "cosine", + "optimizer": "adamw", + "warmup_steps": 2000, + "min_lr": 6e-05, + "weight_decay": 0.1, + "adamw_param_groups": "nanogpt", + "adam_beta1": 0.9, + "adam_beta2": 0.95, + "adam_eps": 1e-08, + "muon_momentum": 0.95, + "muon_ns_steps": 5, + "muon_update_scale": 1.0, + "ema_decay": 0.0, + "ema_start_step": 0, + "model_type": "ddit", + "dual_t": true, + "corrupt_t_mode": "same", + "corrupt_min_t": 0.0, + "corrupt_max_t": 1.0, + "prefix_block_prob": 0.0, + "prefix_block_len": 128, + "dirichlet_endpoint_mode": "categorical_dual_t", + "dirichlet_semantic_t_mode": "same", + "dirichlet_semantic_t_value": 0.0, + "categorical_wrong_from_full_vocab": true, + "categorical_wrong_from_batch_valid_tokens": false, + "mask_mixture_original_prob": 0.0, + "mask_mixture_lowk_prob": 0.0, + "mask_mixture_lowcorrupt_prob": 0.0, + "mask_mixture_block_prob": 0.0, + "mask_mixture_all_prob": 0.0, + "mask_mixture_lowk_clean_tokens": "1,2,4,8,16,32,64", + "mask_mixture_lowcorrupt_tokens": "1,2,4,8,16,32,64", + "mask_mixture_block_tokens": "64,128", + "simplex_bridge_sampler": "dirichlet", + "logistic_normal_sigma_min": 0.18, + "logistic_normal_sigma_max": 2.2, + "logistic_normal_tau_min": 0.65, + "logistic_normal_tau_max": 1.15, + "torch_compile": false, + "compile_mode": "max-autotune", + "state_format": "prob", + "target_loss": "hard_ce", + "meanflow_weight": 0.0, + "rollout_train_prob": 1.0, + "rollout_train_steps": 1, + "rollout_train_infer_steps": 64, + "rollout_train_temp": 1.45, + "rollout_train_max_gamma": 1.0, + "rollout_train_corrupt_only": true, + "bridge_noise_init": "logistic_normal", + "noise_sigma": -1.0, + "allow_tf32": true, + "activation_checkpointing": false, + "activation_checkpoint_interval": 1, + "ddp_static_graph": false, + "ddp_gradient_as_bucket_view": true, + "blocking_data_transfer": false, + "dataloader_prefetch_factor": 2, + "full_train_stats": false, + "record_pad_truncate": false, + "record_add_eos": false, + "record_add_special_tokens": false, + "record_pad_token": "pad", + "record_shuffle_buffer": 10000, + "wrap": true, + "wrap_mode": "stream", + "wrap_record_buffer_size": 200, + "owt_cached_chunks": true, + "owt_chunk_cache_dir": "/e2e-data/evad-tech-vla/wanghan58/data/small_benchmarks/langflow_2604_11748/openwebtext_lta_cached_chunks/gpt2_len1024_train_minus_100k", + "owt_chunk_cache_rebuild": false, + "owt_chunk_cache_write_batch": 4096, + "owt_exact_repeat_per_chunk": 0, + "online_chunk_shuffle": false, + "online_chunk_shuffle_buffer": 10000, + "openwebtext_split": "train_minus_100k", + "detokenizer": "auto", + "resolved_detokenizer": null, + "num_workers": 4, + "latest_every": 1000, + "resume_path": "" +} +[rank6]: Traceback (most recent call last): +[rank6]: File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1487, in +[rank6]: main() +[rank6]: File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1372, in main +[rank6]: logits = trainable_model(loss_state, model_t, bridge.attn_mask) +[rank6]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank6]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl +[rank6]: return self._call_impl(*args, **kwargs) +[rank6]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank6]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1750, in _call_impl +[rank6]: return forward_call(*args, **kwargs) +[rank6]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank6]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/parallel/distributed.py", line 1650, in forward +[rank6]: inputs, kwargs = self._pre_forward(*inputs, **kwargs) +[rank6]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank6]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/parallel/distributed.py", line 1539, in _pre_forward +[rank6]: if torch.is_grad_enabled() and self.reducer._rebuild_buckets(): +[rank6]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank6]: RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss. You can enable unused parameter detection by passing the keyword argument `find_unused_parameters=True` to `torch.nn.parallel.DistributedDataParallel`, and by +[rank6]: making sure all `forward` function outputs participate in calculating loss. +[rank6]: If you already have done the above, then the distributed data parallel module wasn't able to locate the output tensors in the return value of your module's `forward` function. Please include the loss function and the structure of the return value of `forward` of your module when reporting this issue (e.g. list, dict, iterable). +[rank6]: Parameter indices which did not receive grad for rank 6: 0 1 2 3 4 6 7 9 10 11 12 13 14 16 17 19 20 21 22 23 24 26 27 29 30 31 32 33 34 36 37 39 40 41 42 43 44 46 47 49 50 51 52 53 54 56 57 59 60 61 62 63 64 66 67 69 70 71 72 73 74 76 77 79 80 81 82 83 84 86 87 89 90 91 92 93 94 96 97 99 100 101 102 103 104 106 107 109 110 111 112 113 114 116 117 119 120 121 122 123 ... +[rank6]: In addition, you can set the environment variable TORCH_DISTRIBUTED_DEBUG to either INFO or DETAIL to print out information about which particular parameters did not receive gradient on this rank as part of this error +[rank4]: Traceback (most recent call last): +[rank4]: File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1487, in +[rank4]: main() +[rank4]: File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1372, in main +[rank4]: logits = trainable_model(loss_state, model_t, bridge.attn_mask) +[rank4]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank4]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl +[rank4]: return self._call_impl(*args, **kwargs) +[rank4]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank4]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1750, in _call_impl +[rank4]: return forward_call(*args, **kwargs) +[rank4]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank4]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/parallel/distributed.py", line 1650, in forward +[rank4]: inputs, kwargs = self._pre_forward(*inputs, **kwargs) +[rank4]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank4]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/parallel/distributed.py", line 1539, in _pre_forward +[rank4]: if torch.is_grad_enabled() and self.reducer._rebuild_buckets(): +[rank4]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank4]: RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss. You can enable unused parameter detection by passing the keyword argument `find_unused_parameters=True` to `torch.nn.parallel.DistributedDataParallel`, and by +[rank4]: making sure all `forward` function outputs participate in calculating loss. +[rank4]: If you already have done the above, then the distributed data parallel module wasn't able to locate the output tensors in the return value of your module's `forward` function. Please include the loss function and the structure of the return value of `forward` of your module when reporting this issue (e.g. list, dict, iterable). +[rank4]: Parameter indices which did not receive grad for rank 4: 0 1 2 3 4 6 7 9 10 11 12 13 14 16 17 19 20 21 22 23 24 26 27 29 30 31 32 33 34 36 37 39 40 41 42 43 44 46 47 49 50 51 52 53 54 56 57 59 60 61 62 63 64 66 67 69 70 71 72 73 74 76 77 79 80 81 82 83 84 86 87 89 90 91 92 93 94 96 97 99 100 101 102 103 104 106 107 109 110 111 112 113 114 116 117 119 120 121 122 123 ... +[rank4]: In addition, you can set the environment variable TORCH_DISTRIBUTED_DEBUG to either INFO or DETAIL to print out information about which particular parameters did not receive gradient on this rank as part of this error +[rank7]: Traceback (most recent call last): +[rank7]: File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1487, in +[rank7]: main() +[rank7]: File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1372, in main +[rank7]: logits = trainable_model(loss_state, model_t, bridge.attn_mask) +[rank7]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank7]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl +[rank7]: return self._call_impl(*args, **kwargs) +[rank7]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank7]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1750, in _call_impl +[rank7]: return forward_call(*args, **kwargs) +[rank7]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank7]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/parallel/distributed.py", line 1650, in forward +[rank7]: inputs, kwargs = self._pre_forward(*inputs, **kwargs) +[rank7]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank7]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/parallel/distributed.py", line 1539, in _pre_forward +[rank7]: if torch.is_grad_enabled() and self.reducer._rebuild_buckets(): +[rank7]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank7]: RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss. You can enable unused parameter detection by passing the keyword argument `find_unused_parameters=True` to `torch.nn.parallel.DistributedDataParallel`, and by +[rank7]: making sure all `forward` function outputs participate in calculating loss. +[rank7]: If you already have done the above, then the distributed data parallel module wasn't able to locate the output tensors in the return value of your module's `forward` function. Please include the loss function and the structure of the return value of `forward` of your module when reporting this issue (e.g. list, dict, iterable). +[rank7]: Parameter indices which did not receive grad for rank 7: 0 1 2 3 4 6 7 9 10 11 12 13 14 16 17 19 20 21 22 23 24 26 27 29 30 31 32 33 34 36 37 39 40 41 42 43 44 46 47 49 50 51 52 53 54 56 57 59 60 61 62 63 64 66 67 69 70 71 72 73 74 76 77 79 80 81 82 83 84 86 87 89 90 91 92 93 94 96 97 99 100 101 102 103 104 106 107 109 110 111 112 113 114 116 117 119 120 121 122 123 ... +[rank7]: In addition, you can set the environment variable TORCH_DISTRIBUTED_DEBUG to either INFO or DETAIL to print out information about which particular parameters did not receive gradient on this rank as part of this error +[rank3]: Traceback (most recent call last): +[rank3]: File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1487, in +[rank3]: main() +[rank3]: File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1372, in main +[rank3]: logits = trainable_model(loss_state, model_t, bridge.attn_mask) +[rank3]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank3]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl +[rank3]: return self._call_impl(*args, **kwargs) +[rank3]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank3]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1750, in _call_impl +[rank3]: return forward_call(*args, **kwargs) +[rank3]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank3]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/parallel/distributed.py", line 1650, in forward +[rank3]: inputs, kwargs = self._pre_forward(*inputs, **kwargs) +[rank3]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank3]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/parallel/distributed.py", line 1539, in _pre_forward +[rank3]: if torch.is_grad_enabled() and self.reducer._rebuild_buckets(): +[rank3]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank3]: RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss. You can enable unused parameter detection by passing the keyword argument `find_unused_parameters=True` to `torch.nn.parallel.DistributedDataParallel`, and by +[rank3]: making sure all `forward` function outputs participate in calculating loss. +[rank3]: If you already have done the above, then the distributed data parallel module wasn't able to locate the output tensors in the return value of your module's `forward` function. Please include the loss function and the structure of the return value of `forward` of your module when reporting this issue (e.g. list, dict, iterable). +[rank3]: Parameter indices which did not receive grad for rank 3: 0 1 2 3 4 6 7 9 10 11 12 13 14 16 17 19 20 21 22 23 24 26 27 29 30 31 32 33 34 36 37 39 40 41 42 43 44 46 47 49 50 51 52 53 54 56 57 59 60 61 62 63 64 66 67 69 70 71 72 73 74 76 77 79 80 81 82 83 84 86 87 89 90 91 92 93 94 96 97 99 100 101 102 103 104 106 107 109 110 111 112 113 114 116 117 119 120 121 122 123 ... +[rank3]: In addition, you can set the environment variable TORCH_DISTRIBUTED_DEBUG to either INFO or DETAIL to print out information about which particular parameters did not receive gradient on this rank as part of this error +[rank2]: Traceback (most recent call last): +[rank2]: File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1487, in +[rank2]: main() +[rank2]: File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1372, in main +[rank2]: logits = trainable_model(loss_state, model_t, bridge.attn_mask) +[rank2]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank2]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl +[rank2]: return self._call_impl(*args, **kwargs) +[rank2]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank2]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1750, in _call_impl +[rank2]: return forward_call(*args, **kwargs) +[rank2]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank2]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/parallel/distributed.py", line 1650, in forward +[rank2]: inputs, kwargs = self._pre_forward(*inputs, **kwargs) +[rank2]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank2]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/parallel/distributed.py", line 1539, in _pre_forward +[rank2]: if torch.is_grad_enabled() and self.reducer._rebuild_buckets(): +[rank2]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank2]: RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss. You can enable unused parameter detection by passing the keyword argument `find_unused_parameters=True` to `torch.nn.parallel.DistributedDataParallel`, and by +[rank2]: making sure all `forward` function outputs participate in calculating loss. +[rank2]: If you already have done the above, then the distributed data parallel module wasn't able to locate the output tensors in the return value of your module's `forward` function. Please include the loss function and the structure of the return value of `forward` of your module when reporting this issue (e.g. list, dict, iterable). +[rank2]: Parameter indices which did not receive grad for rank 2: 0 1 2 3 4 6 7 9 10 11 12 13 14 16 17 19 20 21 22 23 24 26 27 29 30 31 32 33 34 36 37 39 40 41 42 43 44 46 47 49 50 51 52 53 54 56 57 59 60 61 62 63 64 66 67 69 70 71 72 73 74 76 77 79 80 81 82 83 84 86 87 89 90 91 92 93 94 96 97 99 100 101 102 103 104 106 107 109 110 111 112 113 114 116 117 119 120 121 122 123 ... +[rank2]: In addition, you can set the environment variable TORCH_DISTRIBUTED_DEBUG to either INFO or DETAIL to print out information about which particular parameters did not receive gradient on this rank as part of this error +[rank5]: Traceback (most recent call last): +[rank5]: File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1487, in +[rank5]: main() +[rank5]: File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1372, in main +[rank5]: logits = trainable_model(loss_state, model_t, bridge.attn_mask) +[rank5]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank5]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl +[rank5]: return self._call_impl(*args, **kwargs) +[rank5]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank5]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1750, in _call_impl +[rank5]: return forward_call(*args, **kwargs) +[rank5]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank5]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/parallel/distributed.py", line 1650, in forward +[rank5]: inputs, kwargs = self._pre_forward(*inputs, **kwargs) +[rank5]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank5]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/parallel/distributed.py", line 1539, in _pre_forward +[rank5]: if torch.is_grad_enabled() and self.reducer._rebuild_buckets(): +[rank5]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank5]: RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss. You can enable unused parameter detection by passing the keyword argument `find_unused_parameters=True` to `torch.nn.parallel.DistributedDataParallel`, and by +[rank5]: making sure all `forward` function outputs participate in calculating loss. +[rank5]: If you already have done the above, then the distributed data parallel module wasn't able to locate the output tensors in the return value of your module's `forward` function. Please include the loss function and the structure of the return value of `forward` of your module when reporting this issue (e.g. list, dict, iterable). +[rank5]: Parameter indices which did not receive grad for rank 5: 0 1 2 3 4 6 7 9 10 11 12 13 14 16 17 19 20 21 22 23 24 26 27 29 30 31 32 33 34 36 37 39 40 41 42 43 44 46 47 49 50 51 52 53 54 56 57 59 60 61 62 63 64 66 67 69 70 71 72 73 74 76 77 79 80 81 82 83 84 86 87 89 90 91 92 93 94 96 97 99 100 101 102 103 104 106 107 109 110 111 112 113 114 116 117 119 120 121 122 123 ... +[rank5]: In addition, you can set the environment variable TORCH_DISTRIBUTED_DEBUG to either INFO or DETAIL to print out information about which particular parameters did not receive gradient on this rank as part of this error +[rank1]: Traceback (most recent call last): +[rank1]: File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1487, in +[rank1]: main() +[rank1]: File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1372, in main +[rank1]: logits = trainable_model(loss_state, model_t, bridge.attn_mask) +[rank1]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank1]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl +[rank1]: return self._call_impl(*args, **kwargs) +[rank1]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank1]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1750, in _call_impl +[rank1]: return forward_call(*args, **kwargs) +[rank1]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank1]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/parallel/distributed.py", line 1650, in forward +[rank1]: inputs, kwargs = self._pre_forward(*inputs, **kwargs) +[rank1]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank1]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/parallel/distributed.py", line 1539, in _pre_forward +[rank1]: if torch.is_grad_enabled() and self.reducer._rebuild_buckets(): +[rank1]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank1]: RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss. You can enable unused parameter detection by passing the keyword argument `find_unused_parameters=True` to `torch.nn.parallel.DistributedDataParallel`, and by +[rank1]: making sure all `forward` function outputs participate in calculating loss. +[rank1]: If you already have done the above, then the distributed data parallel module wasn't able to locate the output tensors in the return value of your module's `forward` function. Please include the loss function and the structure of the return value of `forward` of your module when reporting this issue (e.g. list, dict, iterable). +[rank1]: Parameter indices which did not receive grad for rank 1: 0 1 2 3 4 6 7 9 10 11 12 13 14 16 17 19 20 21 22 23 24 26 27 29 30 31 32 33 34 36 37 39 40 41 42 43 44 46 47 49 50 51 52 53 54 56 57 59 60 61 62 63 64 66 67 69 70 71 72 73 74 76 77 79 80 81 82 83 84 86 87 89 90 91 92 93 94 96 97 99 100 101 102 103 104 106 107 109 110 111 112 113 114 116 117 119 120 121 122 123 ... +[rank1]: In addition, you can set the environment variable TORCH_DISTRIBUTED_DEBUG to either INFO or DETAIL to print out information about which particular parameters did not receive gradient on this rank as part of this error +[rank0]: Traceback (most recent call last): +[rank0]: File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1487, in +[rank0]: main() +[rank0]: File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1372, in main +[rank0]: logits = trainable_model(loss_state, model_t, bridge.attn_mask) +[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank0]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl +[rank0]: return self._call_impl(*args, **kwargs) +[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank0]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1750, in _call_impl +[rank0]: return forward_call(*args, **kwargs) +[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank0]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/parallel/distributed.py", line 1650, in forward +[rank0]: inputs, kwargs = self._pre_forward(*inputs, **kwargs) +[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank0]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/parallel/distributed.py", line 1539, in _pre_forward +[rank0]: if torch.is_grad_enabled() and self.reducer._rebuild_buckets(): +[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank0]: RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss. You can enable unused parameter detection by passing the keyword argument `find_unused_parameters=True` to `torch.nn.parallel.DistributedDataParallel`, and by +[rank0]: making sure all `forward` function outputs participate in calculating loss. +[rank0]: If you already have done the above, then the distributed data parallel module wasn't able to locate the output tensors in the return value of your module's `forward` function. Please include the loss function and the structure of the return value of `forward` of your module when reporting this issue (e.g. list, dict, iterable). +[rank0]: Parameter indices which did not receive grad for rank 0: 0 1 2 3 4 6 7 9 10 11 12 13 14 16 17 19 20 21 22 23 24 26 27 29 30 31 32 33 34 36 37 39 40 41 42 43 44 46 47 49 50 51 52 53 54 56 57 59 60 61 62 63 64 66 67 69 70 71 72 73 74 76 77 79 80 81 82 83 84 86 87 89 90 91 92 93 94 96 97 99 100 101 102 103 104 106 107 109 110 111 112 113 114 116 117 119 120 121 122 123 ... +[rank0]: In addition, you can set the environment variable TORCH_DISTRIBUTED_DEBUG to either INFO or DETAIL to print out information about which particular parameters did not receive gradient on this rank as part of this error +t-20260513223132-g9wrc-worker-0:10256:10750 [1] NCCL INFO misc/socket.cc:64 -> 3 +t-20260513223132-g9wrc-worker-0:10256:10750 [1] NCCL INFO misc/socket.cc:80 -> 3 +t-20260513223132-g9wrc-worker-0:10256:10750 [1] NCCL INFO misc/socket.cc:828 -> 3 +t-20260513223132-g9wrc-worker-0:10256:10750 [1] NCCL INFO misc/socket.cc:64 -> 3 +t-20260513223132-g9wrc-worker-0:10256:10750 [1] NCCL INFO misc/socket.cc:80 -> 3 +t-20260513223132-g9wrc-worker-0:10256:10750 [1] NCCL INFO misc/socket.cc:828 -> 3 +t-20260513223132-g9wrc-worker-0:10256:10750 [1] NCCL INFO misc/socket.cc:64 -> 3 +t-20260513223132-g9wrc-worker-0:10256:10750 [1] NCCL INFO misc/socket.cc:80 -> 3 +t-20260513223132-g9wrc-worker-0:10256:10750 [1] NCCL INFO misc/socket.cc:828 -> 3 +t-20260513223132-g9wrc-worker-0:10256:10409 [1] NCCL INFO misc/socket.cc:880 -> 3 +t-20260513223132-g9wrc-worker-0:10261:10752 [6] NCCL INFO misc/socket.cc:64 -> 3 +t-20260513223132-g9wrc-worker-0:10261:10752 [6] NCCL INFO misc/socket.cc:80 -> 3 +t-20260513223132-g9wrc-worker-0:10261:10752 [6] NCCL INFO misc/socket.cc:828 -> 3 +t-20260513223132-g9wrc-worker-0:10261:10752 [6] NCCL INFO misc/socket.cc:64 -> 3 +t-20260513223132-g9wrc-worker-0:10261:10752 [6] NCCL INFO misc/socket.cc:80 -> 3 +t-20260513223132-g9wrc-worker-0:10261:10752 [6] NCCL INFO misc/socket.cc:828 -> 3 +t-20260513223132-g9wrc-worker-0:10261:10752 [6] NCCL INFO misc/socket.cc:64 -> 3 +t-20260513223132-g9wrc-worker-0:10261:10752 [6] NCCL INFO misc/socket.cc:80 -> 3 +t-20260513223132-g9wrc-worker-0:10261:10752 [6] NCCL INFO misc/socket.cc:828 -> 3 +t-20260513223132-g9wrc-worker-0:10261:10752 [6] NCCL INFO misc/socket.cc:64 -> 3 +t-20260513223132-g9wrc-worker-0:10261:10752 [6] NCCL INFO misc/socket.cc:80 -> 3 +t-20260513223132-g9wrc-worker-0:10261:10752 [6] NCCL INFO misc/socket.cc:828 -> 3 +t-20260513223132-g9wrc-worker-0:10261:10411 [6] NCCL INFO misc/socket.cc:880 -> 3 +t-20260513223132-g9wrc-worker-0:10257:10754 [2] NCCL INFO misc/socket.cc:64 -> 3 +t-20260513223132-g9wrc-worker-0:10257:10754 [2] NCCL INFO misc/socket.cc:80 -> 3 +t-20260513223132-g9wrc-worker-0:10257:10754 [2] NCCL INFO misc/socket.cc:828 -> 3 +t-20260513223132-g9wrc-worker-0:10257:10754 [2] NCCL INFO misc/socket.cc:64 -> 3 +t-20260513223132-g9wrc-worker-0:10257:10754 [2] NCCL INFO misc/socket.cc:80 -> 3 +t-20260513223132-g9wrc-worker-0:10257:10754 [2] NCCL INFO misc/socket.cc:828 -> 3 +t-20260513223132-g9wrc-worker-0:10257:10754 [2] NCCL INFO misc/socket.cc:64 -> 3 +t-20260513223132-g9wrc-worker-0:10257:10754 [2] NCCL INFO misc/socket.cc:80 -> 3 +t-20260513223132-g9wrc-worker-0:10257:10754 [2] NCCL INFO misc/socket.cc:828 -> 3 +t-20260513223132-g9wrc-worker-0:10256:10409 [1] NCCL INFO misc/socket.cc:880 -> 3 +t-20260513223132-g9wrc-worker-0:10257:10754 [2] NCCL INFO misc/socket.cc:64 -> 3 +t-20260513223132-g9wrc-worker-0:10257:10754 [2] NCCL INFO misc/socket.cc:80 -> 3 +t-20260513223132-g9wrc-worker-0:10257:10754 [2] NCCL INFO misc/socket.cc:828 -> 3 +t-20260513223132-g9wrc-worker-0:10257:10407 [2] NCCL INFO misc/socket.cc:880 -> 3 +t-20260513223132-g9wrc-worker-0:10260:10756 [5] NCCL INFO misc/socket.cc:64 -> 3 +t-20260513223132-g9wrc-worker-0:10260:10756 [5] NCCL INFO misc/socket.cc:80 -> 3 +t-20260513223132-g9wrc-worker-0:10260:10756 [5] NCCL INFO misc/socket.cc:828 -> 3 +t-20260513223132-g9wrc-worker-0:10260:10756 [5] NCCL INFO misc/socket.cc:64 -> 3 +t-20260513223132-g9wrc-worker-0:10260:10756 [5] NCCL INFO misc/socket.cc:80 -> 3 +t-20260513223132-g9wrc-worker-0:10260:10756 [5] NCCL INFO misc/socket.cc:828 -> 3 +t-20260513223132-g9wrc-worker-0:10260:10756 [5] NCCL INFO misc/socket.cc:64 -> 3 +t-20260513223132-g9wrc-worker-0:10260:10756 [5] NCCL INFO misc/socket.cc:80 -> 3 +t-20260513223132-g9wrc-worker-0:10260:10756 [5] NCCL INFO misc/socket.cc:828 -> 3 +t-20260513223132-g9wrc-worker-0:10260:10756 [5] NCCL INFO misc/socket.cc:64 -> 3 +t-20260513223132-g9wrc-worker-0:10260:10756 [5] NCCL INFO misc/socket.cc:80 -> 3 +t-20260513223132-g9wrc-worker-0:10260:10756 [5] NCCL INFO misc/socket.cc:828 -> 3 +t-20260513223132-g9wrc-worker-0:10260:10414 [5] NCCL INFO misc/socket.cc:880 -> 3 +t-20260513223132-g9wrc-worker-0:10261:10411 [6] NCCL INFO misc/socket.cc:880 -> 3 +t-20260513223132-g9wrc-worker-0:10258:10758 [3] NCCL INFO misc/socket.cc:64 -> 3 +t-20260513223132-g9wrc-worker-0:10258:10758 [3] NCCL INFO misc/socket.cc:80 -> 3 +t-20260513223132-g9wrc-worker-0:10258:10758 [3] NCCL INFO misc/socket.cc:828 -> 3 +t-20260513223132-g9wrc-worker-0:10258:10758 [3] NCCL INFO misc/socket.cc:64 -> 3 +t-20260513223132-g9wrc-worker-0:10258:10758 [3] NCCL INFO misc/socket.cc:80 -> 3 +t-20260513223132-g9wrc-worker-0:10258:10758 [3] NCCL INFO misc/socket.cc:828 -> 3 +t-20260513223132-g9wrc-worker-0:10258:10758 [3] NCCL INFO misc/socket.cc:64 -> 3 +t-20260513223132-g9wrc-worker-0:10257:10407 [2] NCCL INFO misc/socket.cc:880 -> 3 +t-20260513223132-g9wrc-worker-0:10258:10758 [3] NCCL INFO misc/socket.cc:80 -> 3 +t-20260513223132-g9wrc-worker-0:10258:10758 [3] NCCL INFO misc/socket.cc:828 -> 3 +t-20260513223132-g9wrc-worker-0:10258:10758 [3] NCCL INFO misc/socket.cc:64 -> 3 +t-20260513223132-g9wrc-worker-0:10258:10758 [3] NCCL INFO misc/socket.cc:80 -> 3 +t-20260513223132-g9wrc-worker-0:10258:10758 [3] NCCL INFO misc/socket.cc:828 -> 3 +t-20260513223132-g9wrc-worker-0:10258:10418 [3] NCCL INFO misc/socket.cc:880 -> 3 +t-20260513223132-g9wrc-worker-0:10262:10760 [7] NCCL INFO misc/socket.cc:64 -> 3 +t-20260513223132-g9wrc-worker-0:10262:10760 [7] NCCL INFO misc/socket.cc:80 -> 3 +t-20260513223132-g9wrc-worker-0:10262:10760 [7] NCCL INFO misc/socket.cc:828 -> 3 +t-20260513223132-g9wrc-worker-0:10262:10760 [7] NCCL INFO misc/socket.cc:64 -> 3 +t-20260513223132-g9wrc-worker-0:10262:10760 [7] NCCL INFO misc/socket.cc:80 -> 3 +t-20260513223132-g9wrc-worker-0:10262:10760 [7] NCCL INFO misc/socket.cc:828 -> 3 +t-20260513223132-g9wrc-worker-0:10262:10760 [7] NCCL INFO misc/socket.cc:64 -> 3 +t-20260513223132-g9wrc-worker-0:10262:10760 [7] NCCL INFO misc/socket.cc:80 -> 3 +t-20260513223132-g9wrc-worker-0:10262:10760 [7] NCCL INFO misc/socket.cc:828 -> 3 +t-20260513223132-g9wrc-worker-0:10262:10413 [7] NCCL INFO misc/socket.cc:880 -> 3 +t-20260513223132-g9wrc-worker-0:10259:10762 [4] NCCL INFO misc/socket.cc:64 -> 3 +t-20260513223132-g9wrc-worker-0:10259:10762 [4] NCCL INFO misc/socket.cc:80 -> 3 +t-20260513223132-g9wrc-worker-0:10259:10762 [4] NCCL INFO misc/socket.cc:828 -> 3 +t-20260513223132-g9wrc-worker-0:10259:10762 [4] NCCL INFO misc/socket.cc:64 -> 3 +t-20260513223132-g9wrc-worker-0:10259:10762 [4] NCCL INFO misc/socket.cc:80 -> 3 +t-20260513223132-g9wrc-worker-0:10259:10762 [4] NCCL INFO misc/socket.cc:828 -> 3 +t-20260513223132-g9wrc-worker-0:10258:10418 [3] NCCL INFO misc/socket.cc:880 -> 3 +t-20260513223132-g9wrc-worker-0:10259:10762 [4] NCCL INFO misc/socket.cc:64 -> 3 +t-20260513223132-g9wrc-worker-0:10259:10762 [4] NCCL INFO misc/socket.cc:80 -> 3 +t-20260513223132-g9wrc-worker-0:10259:10762 [4] NCCL INFO misc/socket.cc:828 -> 3 +t-20260513223132-g9wrc-worker-0:10259:10762 [4] NCCL INFO misc/socket.cc:64 -> 3 +t-20260513223132-g9wrc-worker-0:10259:10762 [4] NCCL INFO misc/socket.cc:80 -> 3 +t-20260513223132-g9wrc-worker-0:10259:10762 [4] NCCL INFO misc/socket.cc:828 -> 3 +t-20260513223132-g9wrc-worker-0:10259:10415 [4] NCCL INFO misc/socket.cc:880 -> 3 +t-20260513223132-g9wrc-worker-0:10260:10414 [5] NCCL INFO misc/socket.cc:880 -> 3 +[rank0]:[W513 14:32:46.158776236 ProcessGroupNCCL.cpp:1487] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) +t-20260513223132-g9wrc-worker-0:10255:10764 [0] NCCL INFO misc/socket.cc:64 -> 3 +t-20260513223132-g9wrc-worker-0:10255:10764 [0] NCCL INFO misc/socket.cc:80 -> 3 +t-20260513223132-g9wrc-worker-0:10255:10764 [0] NCCL INFO misc/socket.cc:828 -> 3 +t-20260513223132-g9wrc-worker-0:10255:10421 [0] NCCL INFO misc/socket.cc:880 -> 3 +t-20260513223132-g9wrc-worker-0:10255:10764 [0] NCCL INFO misc/socket.cc:64 -> 3 +t-20260513223132-g9wrc-worker-0:10255:10764 [0] NCCL INFO misc/socket.cc:80 -> 3 +t-20260513223132-g9wrc-worker-0:10255:10764 [0] NCCL INFO misc/socket.cc:828 -> 3 +t-20260513223132-g9wrc-worker-0:10255:10764 [0] NCCL INFO misc/socket.cc:64 -> 3 +t-20260513223132-g9wrc-worker-0:10255:10764 [0] NCCL INFO misc/socket.cc:80 -> 3 +t-20260513223132-g9wrc-worker-0:10255:10764 [0] NCCL INFO misc/socket.cc:828 -> 3 +t-20260513223132-g9wrc-worker-0:10262:10413 [7] NCCL INFO misc/socket.cc:880 -> 3 +t-20260513223132-g9wrc-worker-0:10257:10754 [2] NCCL INFO comm 0xb0afb80 rank 2 nranks 8 cudaDev 2 busId 69020 - Abort COMPLETE +t-20260513223132-g9wrc-worker-0:10256:10750 [1] NCCL INFO comm 0x9e75e40 rank 1 nranks 8 cudaDev 1 busId 67020 - Abort COMPLETE +t-20260513223132-g9wrc-worker-0:10260:10756 [5] NCCL INFO comm 0xa89c920 rank 5 nranks 8 cudaDev 5 busId 71020 - Abort COMPLETE +t-20260513223132-g9wrc-worker-0:10258:10758 [3] NCCL INFO comm 0xb5ef120 rank 3 nranks 8 cudaDev 3 busId 6b020 - Abort COMPLETE +t-20260513223132-g9wrc-worker-0:10261:10752 [6] NCCL INFO comm 0xb044c70 rank 6 nranks 8 cudaDev 6 busId 73020 - Abort COMPLETE +t-20260513223132-g9wrc-worker-0:10262:10760 [7] NCCL INFO comm 0xab21280 rank 7 nranks 8 cudaDev 7 busId 75020 - Abort COMPLETE +t-20260513223132-g9wrc-worker-0:10259:10762 [4] NCCL INFO comm 0xb1d1a20 rank 4 nranks 8 cudaDev 4 busId 6f020 - Abort COMPLETE +t-20260513223132-g9wrc-worker-0:10255:10764 [0] NCCL INFO comm 0xb5c2750 rank 0 nranks 8 cudaDev 0 busId 65040 - Abort COMPLETE +W0513 14:32:47.004000 10251 torch/distributed/elastic/multiprocessing/api.py:898] Sending process 10255 closing signal SIGTERM +W0513 14:32:47.005000 10251 torch/distributed/elastic/multiprocessing/api.py:898] Sending process 10258 closing signal SIGTERM +W0513 14:32:47.005000 10251 torch/distributed/elastic/multiprocessing/api.py:898] Sending process 10259 closing signal SIGTERM +W0513 14:32:47.006000 10251 torch/distributed/elastic/multiprocessing/api.py:898] Sending process 10260 closing signal SIGTERM +W0513 14:32:47.006000 10251 torch/distributed/elastic/multiprocessing/api.py:898] Sending process 10261 closing signal SIGTERM +W0513 14:32:47.007000 10251 torch/distributed/elastic/multiprocessing/api.py:898] Sending process 10262 closing signal SIGTERM +E0513 14:32:47.522000 10251 torch/distributed/elastic/multiprocessing/api.py:870] failed (exitcode: 1) local_rank: 1 (pid: 10256) of binary: /usr/bin/python +Traceback (most recent call last): + File "", line 198, in _run_module_as_main + File "", line 88, in _run_code + File "/usr/local/lib/python3.12/dist-packages/torch/distributed/run.py", line 922, in + main() + File "/usr/local/lib/python3.12/dist-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 355, in wrapper + return f(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^ + File "/usr/local/lib/python3.12/dist-packages/torch/distributed/run.py", line 918, in main + run(args) + File "/usr/local/lib/python3.12/dist-packages/torch/distributed/run.py", line 909, in run + elastic_launch( + File "/usr/local/lib/python3.12/dist-packages/torch/distributed/launcher/api.py", line 139, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/usr/local/lib/python3.12/dist-packages/torch/distributed/launcher/api.py", line 270, in launch_agent + raise ChildFailedError( +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +train.py FAILED +------------------------------------------------------------ +Failures: +[1]: + time : 2026-05-13_14:32:47 + host : t-20260513223132-g9wrc-worker-0.t-20260513223132-g9wrc-worker.mlplatform-customtask.svc.cluster.local + rank : 2 (local_rank: 2) + exitcode : 1 (pid: 10257) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2026-05-13_14:32:47 + host : t-20260513223132-g9wrc-worker-0.t-20260513223132-g9wrc-worker.mlplatform-customtask.svc.cluster.local + rank : 1 (local_rank: 1) + exitcode : 1 (pid: 10256) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ diff --git a/LTA_openwebtext_dualt/logs/owt_bert_absrope_time4_C1_to_1024_mask1_sameT_dualline_watch/infer_lta_owt_bert_absrope_time4_dirichlet_len1024_C1_to_1024_mask1_sameT_gbs512_b32_8gpu_1m_save10k_20260526_step_0040000.log b/LTA_openwebtext_dualt/logs/owt_bert_absrope_time4_C1_to_1024_mask1_sameT_dualline_watch/infer_lta_owt_bert_absrope_time4_dirichlet_len1024_C1_to_1024_mask1_sameT_gbs512_b32_8gpu_1m_save10k_20260526_step_0040000.log new file mode 100644 index 0000000000000000000000000000000000000000..1d34e91998171db381bac33fab185d4116486f9f --- /dev/null +++ b/LTA_openwebtext_dualt/logs/owt_bert_absrope_time4_C1_to_1024_mask1_sameT_dualline_watch/infer_lta_owt_bert_absrope_time4_dirichlet_len1024_C1_to_1024_mask1_sameT_gbs512_b32_8gpu_1m_save10k_20260526_step_0040000.log @@ -0,0 +1,398 @@ +[watch-gumbel] 2026-05-26_08:08:35 infer runs/lta_owt_bert_absrope_time4_dirichlet_len1024_C1_to_1024_mask1_sameT_gbs512_b32_8gpu_1m_save10k_20260526/step_0040000.pt -> docs/lta_samples/metrics_20260526/owt_bert_absrope_time4_C1_to_1024_mask1_sameT_dualline_dirres_c1_1024_n128/lta_owt_bert_absrope_time4_dirichlet_len1024_C1_to_1024_mask1_sameT_gbs512_b32_8gpu_1m_save10k_20260526/step_0040000 +[watch-gumbel] 2026-05-26_08:08:37 infer runs/lta_owt_bert_absrope_time4_dirichlet_len1024_C1_to_1024_mask1_sameT_gbs512_b32_8gpu_1m_save10k_20260526/step_0040000.pt -> docs/lta_samples/metrics_20260526/owt_bert_absrope_time4_C1_to_1024_mask1_sameT_dualline_dirres_c1_1024_n128/lta_owt_bert_absrope_time4_dirichlet_len1024_C1_to_1024_mask1_sameT_gbs512_b32_8gpu_1m_save10k_20260526/step_0040000 +[decode] max_len=1024 generated=1/128 +[decode] max_len=1024 generated=1/128 +[decode] max_len=1024 generated=2/128 +[decode] max_len=1024 generated=2/128 +[decode] max_len=1024 generated=3/128 +[decode] max_len=1024 generated=3/128 +[decode] max_len=1024 generated=4/128 +[decode] max_len=1024 generated=4/128 +[decode] max_len=1024 generated=5/128 +[decode] max_len=1024 generated=5/128 +[decode] max_len=1024 generated=6/128 +[decode] max_len=1024 generated=6/128 +[decode] max_len=1024 generated=7/128 +[decode] max_len=1024 generated=7/128 +[decode] max_len=1024 generated=8/128 +[decode] max_len=1024 generated=8/128 +[decode] max_len=1024 generated=9/128 +[decode] max_len=1024 generated=9/128 +[decode] max_len=1024 generated=10/128 +[decode] max_len=1024 generated=10/128 +[decode] max_len=1024 generated=11/128 +[decode] max_len=1024 generated=11/128 +[decode] max_len=1024 generated=12/128 +[decode] max_len=1024 generated=12/128 +[decode] max_len=1024 generated=13/128 +[decode] max_len=1024 generated=13/128 +[decode] max_len=1024 generated=14/128 +[decode] max_len=1024 generated=14/128 +[decode] max_len=1024 generated=15/128 +[decode] max_len=1024 generated=15/128 +[decode] max_len=1024 generated=16/128 +[decode] max_len=1024 generated=16/128 +[decode] max_len=1024 generated=17/128 +[decode] max_len=1024 generated=17/128 +[decode] max_len=1024 generated=18/128 +[decode] max_len=1024 generated=18/128 +[decode] max_len=1024 generated=19/128 +[decode] max_len=1024 generated=19/128 +[decode] max_len=1024 generated=20/128 +[decode] max_len=1024 generated=20/128 +[decode] max_len=1024 generated=21/128 +[decode] max_len=1024 generated=21/128 +[decode] max_len=1024 generated=22/128 +[decode] max_len=1024 generated=22/128 +[decode] max_len=1024 generated=23/128 +[decode] max_len=1024 generated=23/128 +[decode] max_len=1024 generated=24/128 +[decode] max_len=1024 generated=24/128 +[decode] max_len=1024 generated=25/128 +[decode] max_len=1024 generated=25/128 +[decode] max_len=1024 generated=26/128 +[decode] max_len=1024 generated=26/128 +[decode] max_len=1024 generated=27/128 +[decode] max_len=1024 generated=27/128 +[decode] max_len=1024 generated=28/128 +[decode] max_len=1024 generated=28/128 +[decode] max_len=1024 generated=29/128 +[decode] max_len=1024 generated=29/128 +[decode] max_len=1024 generated=30/128 +[decode] max_len=1024 generated=30/128 +[decode] max_len=1024 generated=31/128 +[decode] max_len=1024 generated=31/128 +[decode] max_len=1024 generated=32/128 +[decode] max_len=1024 generated=32/128 +[decode] max_len=1024 generated=33/128 +[decode] max_len=1024 generated=33/128 +[decode] max_len=1024 generated=34/128 +[decode] max_len=1024 generated=34/128 +[decode] max_len=1024 generated=35/128 +[decode] max_len=1024 generated=35/128 +[decode] max_len=1024 generated=36/128 +[decode] max_len=1024 generated=36/128 +[decode] max_len=1024 generated=37/128 +[decode] max_len=1024 generated=37/128 +[decode] max_len=1024 generated=38/128 +[decode] max_len=1024 generated=38/128 +[decode] max_len=1024 generated=39/128 +[decode] max_len=1024 generated=39/128 +[decode] max_len=1024 generated=40/128 +[decode] max_len=1024 generated=40/128 +[decode] max_len=1024 generated=41/128 +[decode] max_len=1024 generated=41/128 +[decode] max_len=1024 generated=42/128 +[decode] max_len=1024 generated=42/128 +[decode] max_len=1024 generated=43/128 +[decode] max_len=1024 generated=43/128 +[decode] max_len=1024 generated=44/128 +[decode] max_len=1024 generated=44/128 +[decode] max_len=1024 generated=45/128 +[decode] max_len=1024 generated=45/128 +[decode] max_len=1024 generated=46/128 +[decode] max_len=1024 generated=46/128 +[decode] max_len=1024 generated=47/128 +[decode] max_len=1024 generated=47/128 +[decode] max_len=1024 generated=48/128 +[decode] max_len=1024 generated=48/128 +[decode] max_len=1024 generated=49/128 +[decode] max_len=1024 generated=49/128 +[decode] max_len=1024 generated=50/128 +[decode] max_len=1024 generated=50/128 +[decode] max_len=1024 generated=51/128 +[decode] max_len=1024 generated=51/128 +[decode] max_len=1024 generated=52/128 +[decode] max_len=1024 generated=52/128 +[decode] max_len=1024 generated=53/128 +[decode] max_len=1024 generated=53/128 +[decode] max_len=1024 generated=54/128 +[decode] max_len=1024 generated=54/128 +[decode] max_len=1024 generated=55/128 +[decode] max_len=1024 generated=55/128 +[decode] max_len=1024 generated=56/128 +[decode] max_len=1024 generated=56/128 +[decode] max_len=1024 generated=57/128 +[decode] max_len=1024 generated=57/128 +[decode] max_len=1024 generated=58/128 +[decode] max_len=1024 generated=58/128 +[decode] max_len=1024 generated=59/128 +[decode] max_len=1024 generated=59/128 +[decode] max_len=1024 generated=60/128 +[decode] max_len=1024 generated=60/128 +[decode] max_len=1024 generated=61/128 +[decode] max_len=1024 generated=61/128 +[decode] max_len=1024 generated=62/128 +[decode] max_len=1024 generated=62/128 +[decode] max_len=1024 generated=63/128 +[decode] max_len=1024 generated=63/128 +[decode] max_len=1024 generated=64/128 +[decode] max_len=1024 generated=64/128 +[decode] max_len=1024 generated=65/128 +[decode] max_len=1024 generated=65/128 +[decode] max_len=1024 generated=66/128 +[decode] max_len=1024 generated=66/128 +[decode] max_len=1024 generated=67/128 +[decode] max_len=1024 generated=67/128 +[decode] max_len=1024 generated=68/128 +[decode] max_len=1024 generated=68/128 +[decode] max_len=1024 generated=69/128 +[decode] max_len=1024 generated=69/128 +[decode] max_len=1024 generated=70/128 +[decode] max_len=1024 generated=70/128 +[decode] max_len=1024 generated=71/128 +[decode] max_len=1024 generated=71/128 +[decode] max_len=1024 generated=72/128 +[decode] max_len=1024 generated=72/128 +[decode] max_len=1024 generated=73/128 +[decode] max_len=1024 generated=73/128 +[decode] max_len=1024 generated=74/128 +[decode] max_len=1024 generated=74/128 +[decode] max_len=1024 generated=75/128 +[decode] max_len=1024 generated=75/128 +[decode] max_len=1024 generated=76/128 +[decode] max_len=1024 generated=76/128 +[decode] max_len=1024 generated=77/128 +[decode] max_len=1024 generated=77/128 +[decode] max_len=1024 generated=78/128 +[decode] max_len=1024 generated=78/128 +[decode] max_len=1024 generated=79/128 +[decode] max_len=1024 generated=79/128 +[decode] max_len=1024 generated=80/128 +[decode] max_len=1024 generated=80/128 +[decode] max_len=1024 generated=81/128 +[decode] max_len=1024 generated=81/128 +[decode] max_len=1024 generated=82/128 +[decode] max_len=1024 generated=82/128 +[decode] max_len=1024 generated=83/128 +[decode] max_len=1024 generated=83/128 +[decode] max_len=1024 generated=84/128 +[decode] max_len=1024 generated=84/128 +[decode] max_len=1024 generated=85/128 +[decode] max_len=1024 generated=85/128 +[decode] max_len=1024 generated=86/128 +[decode] max_len=1024 generated=86/128 +[decode] max_len=1024 generated=87/128 +[decode] max_len=1024 generated=87/128 +[decode] max_len=1024 generated=88/128 +[decode] max_len=1024 generated=88/128 +[decode] max_len=1024 generated=89/128 +[decode] max_len=1024 generated=89/128 +[decode] max_len=1024 generated=90/128 +[decode] max_len=1024 generated=90/128 +[decode] max_len=1024 generated=91/128 +[decode] max_len=1024 generated=91/128 +[decode] max_len=1024 generated=92/128 +[decode] max_len=1024 generated=92/128 +[decode] max_len=1024 generated=93/128 +[decode] max_len=1024 generated=93/128 +[decode] max_len=1024 generated=94/128 +[decode] max_len=1024 generated=94/128 +[decode] max_len=1024 generated=95/128 +[decode] max_len=1024 generated=95/128 +[decode] max_len=1024 generated=96/128 +[decode] max_len=1024 generated=96/128 +[decode] max_len=1024 generated=97/128 +[decode] max_len=1024 generated=97/128 +[decode] max_len=1024 generated=98/128 +[decode] max_len=1024 generated=98/128 +[decode] max_len=1024 generated=99/128 +[decode] max_len=1024 generated=99/128 +[decode] max_len=1024 generated=100/128 +[decode] max_len=1024 generated=100/128 +[decode] max_len=1024 generated=101/128 +[decode] max_len=1024 generated=101/128 +[decode] max_len=1024 generated=102/128 +[decode] max_len=1024 generated=103/128 +[decode] max_len=1024 generated=102/128 +[decode] max_len=1024 generated=104/128 +[decode] max_len=1024 generated=103/128 +[decode] max_len=1024 generated=105/128 +[decode] max_len=1024 generated=104/128 +[decode] max_len=1024 generated=106/128 +[decode] max_len=1024 generated=105/128 +[decode] max_len=1024 generated=107/128 +[decode] max_len=1024 generated=106/128 +[decode] max_len=1024 generated=108/128 +[decode] max_len=1024 generated=107/128 +[decode] max_len=1024 generated=109/128 +[decode] max_len=1024 generated=108/128 +[decode] max_len=1024 generated=110/128 +[decode] max_len=1024 generated=109/128 +[decode] max_len=1024 generated=111/128 +[decode] max_len=1024 generated=110/128 +[decode] max_len=1024 generated=112/128 +[decode] max_len=1024 generated=111/128 +[decode] max_len=1024 generated=113/128 +[decode] max_len=1024 generated=112/128 +[decode] max_len=1024 generated=114/128 +[decode] max_len=1024 generated=113/128 +[decode] max_len=1024 generated=115/128 +[decode] max_len=1024 generated=114/128 +[decode] max_len=1024 generated=116/128 +[decode] max_len=1024 generated=115/128 +[decode] max_len=1024 generated=117/128 +[decode] max_len=1024 generated=116/128 +[decode] max_len=1024 generated=118/128 +[decode] max_len=1024 generated=117/128 +[decode] max_len=1024 generated=119/128 +[decode] max_len=1024 generated=118/128 +[decode] max_len=1024 generated=120/128 +[decode] max_len=1024 generated=119/128 +[decode] max_len=1024 generated=121/128 +[decode] max_len=1024 generated=120/128 +[decode] max_len=1024 generated=122/128 +[decode] max_len=1024 generated=121/128 +[decode] max_len=1024 generated=123/128 +[decode] max_len=1024 generated=122/128 +[decode] max_len=1024 generated=124/128 +[decode] max_len=1024 generated=123/128 +[decode] max_len=1024 generated=125/128 +[decode] max_len=1024 generated=124/128 +[decode] max_len=1024 generated=126/128 +[decode] max_len=1024 generated=125/128 +[decode] max_len=1024 generated=127/128 +[decode] max_len=1024 generated=126/128 +[decode] max_len=1024 generated=128/128 +[decode] max_len=1024 generated=127/128 +[decode] max_len=1024 generated=128/128 +[ + { + "checkpoint": "runs/lta_owt_bert_absrope_time4_dirichlet_len1024_C1_to_1024_mask1_sameT_gbs512_b32_8gpu_1m_save10k_20260526/step_0040000.pt", + "ckpt_step": 40000, + "max_len": 1024, + "decode_rule": "dual_line_resample", + "support_power": 1.0, + "semantic_power": 1.5, + "steps": 128, + "c_min": 1.0, + "c_max": 1024.0, + "anchor_mode": "state", + "model_t_mode": "flow", + "time_schedule": "uniform", + "time_logit_mean": -1.5, + "time_logit_std": 0.8, + "time_power": 2.0, + "input_noise_scale": 0.0, + "input_noise_until": 1.0, + "input_noise_dirichlet_concentration": 1.0, + "endpoint_softening": "none", + "endpoint_soft_power": 2.0, + "endpoint_soft_min_conf": 0.0, + "endpoint_soft_max_conf": 1.0, + "soft_target_decode_mode": "off", + "soft_target_power": 1.0, + "soft_target_min_conf": 0.0, + "soft_target_max_conf": 1.0, + "soft_target_debias_start": 0.7, + "final_from": "blend", + "final_decode": "argmax", + "final_sample_temp": 1.0, + "final_top_k": 0, + "final_top_p": 1.0, + "commit_mode": "off", 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"???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? —????????????? —??????????????????????? —???????????????????????????????????????????????????????????? —?? —??????? —??????????????? —??????? —? —???? — —?????????????????????????????????????? — — — — — —? —? — — —?????? —? —?? —? — —?????? —? —????? —????????????? —????????? —??????????????????????????????????????? —??????????????????????????????????????? —????????????? —? —?????? —???????? —??????????????? —?????????????????????????????????????????", + "[CLS]. korika, kojima kojima, kojima. korika. korika, kojima koi. korika, kojima kojima. korika. korika, - kojima kojima, kodaleni. kojima. koma kontinen, - - kojima korika, - kodaleni. kodaleni. kodaleni. kojima. korika kojima - korika. ko kojima, kona korika. kodalena. kodaleni kojima - kojima. kodaleni kojima. kodaleni. kodalena, - koanum. koriki, ko - kodaleni. ko kodaleni. ko kodaleni. kodalena. kodalena. kodalena. kojima, ko - kojima. kodalena, kojima - kojima, kojima. ko kodaleni. ko - koma. kodaleni. kojima. kodaleni. ko kodaleni. kodalena. ko kodaleni. kodalena. kodalenna. kodalena. kojima - kojima. kojima, kojima - ko - kojima. kojima -, -, - - - - - - -, - - - - - -,, - - - - - - -, - - - - -,, - - - -,, - ko - - -, - ko, - - - -, - - ko -, - - - -,,, ko ko - -, -,, ko -, - - - -, ko, - - - - - - - -, - ko - - -, ko - -, -, ko - - - - -,,, - - - - - - - - - - - - - - - - - - - - - - - -,, - - -, ko,,,,, ko - - - -,, - koa,, - - - - -,,, ko - - -, -,, koa - -, - - - -,,, - ko - - - - - -, - ko - - - - -, - ko - -,,, - - - - - - - - - - -,,, - - -,, ko -,,, ko - - -, -, ko,,,, ko -,,, - - - ko -,,,, ko, ko - - -, - - ko - - - - - -,, ko -, - -, -, - - - - - -, -, -, ko - - -, -, ko, ko - -, - - ko -, ko, - - -, -,,,, ko - - - - -, - - - -,, koma -ma, -, - - - ko -,,, ko -, -, ko,, ko - - -, ko,, ko - - -, ko, - - -, -, ko,, - ko - -,, -, ko - - -,,,, ko ko - - - - - -,, -, - ko - -,, ko ko ko -, - - -, -, ko -.ka, ko, ko - chei,.,.,, :, koka - -,, - - - ko, -, - - koma, - ko - - ko - -, ko, koku kokuma -,,, ko, koku - ko -, ko, - -, ko,, -,ma - - -,, - ko -, ko - ko ko - -,,ka, ko, - - ko - ko, -, - -, -, - - - - - - -, ko ko, koa, - -, -, ko, ko,, - - - - ko -,,, ko, ko ko,. -, ko, ko, -, -, -, ko, - - -,, ko.,. -, -, - - - - - - - ko, - - - -,., ko - koka,. - ko ko ko -,ma -... - ko - - - -, -,,, - - - ko -, -, ko ko - -ma, -, ko -, -, ko, - - - - ko ko - - - -,, - - - -, ko - - - - -,, - - ko ko -, -, ko - - - - - -, - - - - ko,, ko ko - koa, -, - - -,,, ko, ko - - - - ko -,. -, -,, -, ko, ko - - ko, - [SEP]", + "??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? —??????? — —?????????????????????????????????????????????????????????????????????????????????????????????????????? —??? —??????????????????? —???? —????????????????????????? —?????????????????????????????????????????????????????????????????? — —? — — —? —????????????????????? —? —? —??? —?? —???? —??????? —?? —???? — —?? —??? — — —????????????? —? —?? — —? —??????? —??????? — —?????? —???? —???? —????? — —??? — — — — —? —? — — — —??? —? —? —??????? — —?????? — —?????? —? —???? — — — — —? — — — — — — — —??? —?? — —? — — — — — — — — —? —?? —? —?? —?????????????? — —??? —??? —??????????????????????? —??????? —????????? —????????????? —?????????????? —????????? —?? —??? —??????? —????? —? — —??? —?? —??????? — —?????? —?? — —??? —?????????????????????????????????", + "?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? —?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????" + ], + "gen_ppl": 4.906654522033406, + "gen_nll": 1.590592349500496, + "gen_tokens": 46838 + } +] +[watch-gumbel] 2026-05-26_08:15:10 done step_0040000 +[ + { + "checkpoint": "runs/lta_owt_bert_absrope_time4_dirichlet_len1024_C1_to_1024_mask1_sameT_gbs512_b32_8gpu_1m_save10k_20260526/step_0040000.pt", + "ckpt_step": 40000, + "max_len": 1024, + "decode_rule": "dual_line_resample", + "support_power": 1.0, + "semantic_power": 1.5, + "steps": 128, + "c_min": 1.0, + "c_max": 1024.0, + "anchor_mode": "state", + "model_t_mode": "flow", + "time_schedule": "uniform", + "time_logit_mean": -1.5, + "time_logit_std": 0.8, + "time_power": 2.0, + "input_noise_scale": 0.0, + "input_noise_until": 1.0, + "input_noise_dirichlet_concentration": 1.0, + "endpoint_softening": "none", + "endpoint_soft_power": 2.0, + "endpoint_soft_min_conf": 0.0, + "endpoint_soft_max_conf": 1.0, + "soft_target_decode_mode": "off", + "soft_target_power": 1.0, + "soft_target_min_conf": 0.0, + "soft_target_max_conf": 1.0, + "soft_target_debias_start": 0.7, + "final_from": "blend", + "final_decode": "argmax", + "final_sample_temp": 1.0, + "final_top_k": 0, + "final_top_p": 1.0, + "commit_mode": "off", + "commit_conf_threshold": 0.0, + "commit_margin_threshold": 0.0, + "commit_start": 0.0, + "commit_min_ratio": 0.0, + "commit_max_ratio": 1.0, + "commit_power": 2.0, + "commit_freq_max_frac": 0.08, + "early_temp": 2.8, + "late_temp": 1.45, + "temp_end": 0.55, + "temp_power": 1.5, + "pos_extend": "repeat", + "fixed_first_token_id": null, + "fixed_first_token_text": "", + "fixed_first_initial_argmax": false, + "use_ema": false, + "n_samples": 128, + "sample_entropy": 0.5497349080951995, + "unique_tokens": 467, + "token_count": 131072, + "distinct_1": 0.00356292724609375, + "distinct_2": 0.018824841153470186, + "top_token_mass": 0.7095108032226562, + "texts_preview": [ + "???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? —????????????? —??????????????????????? —???????????????????????????????????????????????????????????? —?? —??????? —??????????????? —??????? —? —???? — —?????????????????????????????????????? — — — — — —? —? — — —?????? —? —?? —? — —?????? —? —????? —????????????? —????????? —??????????????????????????????????????? —??????????????????????????????????????? —????????????? —? —?????? —???????? —??????????????? —?????????????????????????????????????????", + "[CLS]. korika, kojima kojima, kojima. korika. korika, kojima koi. korika, kojima kojima. korika. korika, - kojima kojima, kodaleni. kojima. koma kontinen, - - kojima korika, - kodaleni. kodaleni. kodaleni. kojima. korika kojima - korika. ko kojima, kona korika. kodalena. kodaleni kojima - kojima. kodaleni kojima. kodaleni. kodalena, - koanum. koriki, ko - kodaleni. ko kodaleni. ko kodaleni. kodalena. kodalena. kodalena. kojima, ko - kojima. kodalena, kojima - kojima, kojima. ko kodaleni. ko - koma. kodaleni. kojima. kodaleni. ko kodaleni. kodalena. ko kodaleni. kodalena. kodalenna. kodalena. kojima - kojima. kojima, kojima - ko - kojima. kojima -, -, - - - - - - -, - - - - - -,, - - - - - - -, - - - - -,, - - - -,, - ko - - -, - ko, - - - -, - - ko -, - - - -,,, ko ko - -, -,, ko -, - - - -, ko, - - - - - - - -, - ko - - -, ko - -, -, ko - - - - -,,, - - - - - - - - - - - - - - - - - - - - - - - -,, - - -, ko,,,,, ko - - - -,, - koa,, - - - - -,,, ko - - -, -,, koa - -, - - - -,,, - ko - - - - - -, - ko - - - - -, - ko - -,,, - - - - - - - - - - -,,, - - -,, ko -,,, ko - - -, -, ko,,,, ko -,,, - - - ko -,,,, ko, ko - - -, - - ko - - - - - -,, ko -, - -, -, - - - - - -, -, -, ko - - -, -, ko, ko - -, - - ko -, ko, - - -, -,,,, ko - - - - -, - - - -,, koma -ma, -, - - - ko -,,, ko -, -, ko,, ko - - -, ko,, ko - - -, ko, - - -, -, ko,, - ko - -,, -, ko - - -,,,, ko ko - - - - - -,, -, - ko - -,, ko ko ko -, - - -, -, ko -.ka, ko, ko - chei,.,.,, :, koka - -,, - - - ko, -, - - koma, - ko - - ko - -, ko, koku kokuma -,,, ko, koku - ko -, ko, - -, ko,, -,ma - - -,, - ko -, ko - ko ko - -,,ka, ko, - - ko - ko, -, - -, -, - - - - - - -, ko ko, koa, - -, -, ko, ko,, - - - - ko -,,, ko, ko ko,. -, ko, ko, -, -, -, ko, - - -,, ko.,. -, -, - - - - - - - ko, - - - -,., ko - koka,. - ko ko ko -,ma -... - ko - - - -, -,,, - - - ko -, -, ko ko - -ma, -, ko -, -, ko, - - - - ko ko - - - -,, - - - -, ko - - - - -,, - - ko ko -, -, ko - - - - - -, - - - - ko,, ko ko - koa, -, - - -,,, ko, ko - - - - ko -,. -, -,, -, ko, ko - - ko, - [SEP]", + "??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? —??????? — —?????????????????????????????????????????????????????????????????????????????????????????????????????? —??? —??????????????????? —???? —????????????????????????? —?????????????????????????????????????????????????????????????????? — —? — — —? —????????????????????? —? —? —??? —?? —???? —??????? —?? —???? — —?? —??? — — —????????????? —? —?? — —? —??????? —??????? — —?????? —???? —???? —????? — —??? — — — — —? —? — — — —??? —? —? —??????? — —?????? — —?????? —? —???? — — — — —? — — — — — — — —??? —?? — —? — — — — — — — — —? —?? —? —?? —?????????????? — —??? —??? —??????????????????????? —??????? —????????? —????????????? —?????????????? —????????? —?? —??? —??????? —????? —? — —??? —?? —??????? — —?????? —?? — —??? —?????????????????????????????????", + "?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? —?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????" + ], + "gen_ppl": 4.906654522033406, + "gen_nll": 1.590592349500496, + "gen_tokens": 46838 + } +] +[watch-gumbel] 2026-05-26_08:15:15 done step_0040000 diff --git a/LTA_openwebtext_dualt/logs/owt_bert_absrope_time4_C1_to_1024_mask1_sameT_dualline_watch/infer_lta_owt_bert_absrope_time4_dirichlet_len1024_C1_to_1024_mask1_sameT_gbs512_b32_8gpu_1m_save10k_20260526_step_0050000.log b/LTA_openwebtext_dualt/logs/owt_bert_absrope_time4_C1_to_1024_mask1_sameT_dualline_watch/infer_lta_owt_bert_absrope_time4_dirichlet_len1024_C1_to_1024_mask1_sameT_gbs512_b32_8gpu_1m_save10k_20260526_step_0050000.log new file mode 100644 index 0000000000000000000000000000000000000000..840db2e558e57f20051fc2b0150d5062a79467dd --- /dev/null +++ b/LTA_openwebtext_dualt/logs/owt_bert_absrope_time4_C1_to_1024_mask1_sameT_dualline_watch/infer_lta_owt_bert_absrope_time4_dirichlet_len1024_C1_to_1024_mask1_sameT_gbs512_b32_8gpu_1m_save10k_20260526_step_0050000.log @@ -0,0 +1,398 @@ +[watch-gumbel] 2026-05-26_09:07:11 infer runs/lta_owt_bert_absrope_time4_dirichlet_len1024_C1_to_1024_mask1_sameT_gbs512_b32_8gpu_1m_save10k_20260526/step_0050000.pt -> docs/lta_samples/metrics_20260526/owt_bert_absrope_time4_C1_to_1024_mask1_sameT_dualline_dirres_c1_1024_n128/lta_owt_bert_absrope_time4_dirichlet_len1024_C1_to_1024_mask1_sameT_gbs512_b32_8gpu_1m_save10k_20260526/step_0050000 +[watch-gumbel] 2026-05-26_09:07:17 infer runs/lta_owt_bert_absrope_time4_dirichlet_len1024_C1_to_1024_mask1_sameT_gbs512_b32_8gpu_1m_save10k_20260526/step_0050000.pt -> docs/lta_samples/metrics_20260526/owt_bert_absrope_time4_C1_to_1024_mask1_sameT_dualline_dirres_c1_1024_n128/lta_owt_bert_absrope_time4_dirichlet_len1024_C1_to_1024_mask1_sameT_gbs512_b32_8gpu_1m_save10k_20260526/step_0050000 +[decode] max_len=1024 generated=1/128 +[decode] max_len=1024 generated=2/128 +[decode] max_len=1024 generated=3/128 +[decode] max_len=1024 generated=1/128 +[decode] max_len=1024 generated=4/128 +[decode] max_len=1024 generated=2/128 +[decode] max_len=1024 generated=5/128 +[decode] max_len=1024 generated=3/128 +[decode] max_len=1024 generated=6/128 +[decode] max_len=1024 generated=4/128 +[decode] max_len=1024 generated=7/128 +[decode] max_len=1024 generated=5/128 +[decode] max_len=1024 generated=8/128 +[decode] max_len=1024 generated=6/128 +[decode] max_len=1024 generated=9/128 +[decode] max_len=1024 generated=7/128 +[decode] max_len=1024 generated=10/128 +[decode] max_len=1024 generated=8/128 +[decode] max_len=1024 generated=11/128 +[decode] max_len=1024 generated=9/128 +[decode] max_len=1024 generated=12/128 +[decode] max_len=1024 generated=10/128 +[decode] max_len=1024 generated=13/128 +[decode] max_len=1024 generated=11/128 +[decode] max_len=1024 generated=14/128 +[decode] max_len=1024 generated=12/128 +[decode] max_len=1024 generated=15/128 +[decode] max_len=1024 generated=13/128 +[decode] max_len=1024 generated=16/128 +[decode] max_len=1024 generated=14/128 +[decode] max_len=1024 generated=17/128 +[decode] max_len=1024 generated=15/128 +[decode] max_len=1024 generated=18/128 +[decode] max_len=1024 generated=16/128 +[decode] max_len=1024 generated=19/128 +[decode] max_len=1024 generated=17/128 +[decode] max_len=1024 generated=20/128 +[decode] max_len=1024 generated=18/128 +[decode] max_len=1024 generated=21/128 +[decode] max_len=1024 generated=19/128 +[decode] max_len=1024 generated=22/128 +[decode] max_len=1024 generated=20/128 +[decode] max_len=1024 generated=23/128 +[decode] max_len=1024 generated=21/128 +[decode] max_len=1024 generated=24/128 +[decode] max_len=1024 generated=22/128 +[decode] max_len=1024 generated=25/128 +[decode] max_len=1024 generated=23/128 +[decode] max_len=1024 generated=26/128 +[decode] max_len=1024 generated=24/128 +[decode] max_len=1024 generated=27/128 +[decode] max_len=1024 generated=25/128 +[decode] max_len=1024 generated=28/128 +[decode] max_len=1024 generated=26/128 +[decode] max_len=1024 generated=29/128 +[decode] max_len=1024 generated=27/128 +[decode] max_len=1024 generated=30/128 +[decode] max_len=1024 generated=28/128 +[decode] max_len=1024 generated=31/128 +[decode] max_len=1024 generated=29/128 +[decode] max_len=1024 generated=32/128 +[decode] max_len=1024 generated=30/128 +[decode] max_len=1024 generated=33/128 +[decode] max_len=1024 generated=31/128 +[decode] max_len=1024 generated=34/128 +[decode] max_len=1024 generated=32/128 +[decode] max_len=1024 generated=35/128 +[decode] max_len=1024 generated=33/128 +[decode] max_len=1024 generated=36/128 +[decode] max_len=1024 generated=34/128 +[decode] max_len=1024 generated=37/128 +[decode] max_len=1024 generated=35/128 +[decode] max_len=1024 generated=38/128 +[decode] max_len=1024 generated=36/128 +[decode] max_len=1024 generated=39/128 +[decode] max_len=1024 generated=37/128 +[decode] max_len=1024 generated=40/128 +[decode] max_len=1024 generated=38/128 +[decode] max_len=1024 generated=41/128 +[decode] max_len=1024 generated=39/128 +[decode] max_len=1024 generated=42/128 +[decode] max_len=1024 generated=40/128 +[decode] max_len=1024 generated=43/128 +[decode] max_len=1024 generated=41/128 +[decode] max_len=1024 generated=44/128 +[decode] max_len=1024 generated=42/128 +[decode] max_len=1024 generated=45/128 +[decode] max_len=1024 generated=43/128 +[decode] max_len=1024 generated=46/128 +[decode] max_len=1024 generated=44/128 +[decode] max_len=1024 generated=47/128 +[decode] max_len=1024 generated=45/128 +[decode] max_len=1024 generated=48/128 +[decode] max_len=1024 generated=46/128 +[decode] max_len=1024 generated=49/128 +[decode] max_len=1024 generated=47/128 +[decode] max_len=1024 generated=50/128 +[decode] max_len=1024 generated=48/128 +[decode] max_len=1024 generated=51/128 +[decode] max_len=1024 generated=49/128 +[decode] max_len=1024 generated=52/128 +[decode] max_len=1024 generated=50/128 +[decode] max_len=1024 generated=53/128 +[decode] max_len=1024 generated=51/128 +[decode] max_len=1024 generated=54/128 +[decode] max_len=1024 generated=52/128 +[decode] max_len=1024 generated=55/128 +[decode] max_len=1024 generated=53/128 +[decode] max_len=1024 generated=56/128 +[decode] max_len=1024 generated=54/128 +[decode] max_len=1024 generated=57/128 +[decode] max_len=1024 generated=55/128 +[decode] max_len=1024 generated=58/128 +[decode] max_len=1024 generated=56/128 +[decode] max_len=1024 generated=59/128 +[decode] max_len=1024 generated=57/128 +[decode] max_len=1024 generated=60/128 +[decode] max_len=1024 generated=58/128 +[decode] max_len=1024 generated=61/128 +[decode] max_len=1024 generated=59/128 +[decode] max_len=1024 generated=62/128 +[decode] max_len=1024 generated=60/128 +[decode] max_len=1024 generated=63/128 +[decode] max_len=1024 generated=61/128 +[decode] max_len=1024 generated=64/128 +[decode] max_len=1024 generated=62/128 +[decode] max_len=1024 generated=65/128 +[decode] max_len=1024 generated=63/128 +[decode] max_len=1024 generated=66/128 +[decode] max_len=1024 generated=64/128 +[decode] max_len=1024 generated=67/128 +[decode] max_len=1024 generated=65/128 +[decode] max_len=1024 generated=68/128 +[decode] max_len=1024 generated=66/128 +[decode] max_len=1024 generated=69/128 +[decode] max_len=1024 generated=67/128 +[decode] max_len=1024 generated=70/128 +[decode] max_len=1024 generated=68/128 +[decode] max_len=1024 generated=71/128 +[decode] max_len=1024 generated=69/128 +[decode] max_len=1024 generated=72/128 +[decode] max_len=1024 generated=70/128 +[decode] max_len=1024 generated=73/128 +[decode] max_len=1024 generated=71/128 +[decode] max_len=1024 generated=74/128 +[decode] max_len=1024 generated=72/128 +[decode] max_len=1024 generated=75/128 +[decode] max_len=1024 generated=73/128 +[decode] max_len=1024 generated=76/128 +[decode] max_len=1024 generated=74/128 +[decode] max_len=1024 generated=77/128 +[decode] max_len=1024 generated=75/128 +[decode] max_len=1024 generated=78/128 +[decode] max_len=1024 generated=76/128 +[decode] max_len=1024 generated=79/128 +[decode] max_len=1024 generated=77/128 +[decode] max_len=1024 generated=80/128 +[decode] max_len=1024 generated=78/128 +[decode] max_len=1024 generated=81/128 +[decode] max_len=1024 generated=79/128 +[decode] max_len=1024 generated=82/128 +[decode] max_len=1024 generated=80/128 +[decode] max_len=1024 generated=83/128 +[decode] max_len=1024 generated=81/128 +[decode] max_len=1024 generated=84/128 +[decode] max_len=1024 generated=82/128 +[decode] max_len=1024 generated=85/128 +[decode] max_len=1024 generated=83/128 +[decode] max_len=1024 generated=86/128 +[decode] max_len=1024 generated=84/128 +[decode] max_len=1024 generated=87/128 +[decode] max_len=1024 generated=85/128 +[decode] max_len=1024 generated=88/128 +[decode] max_len=1024 generated=86/128 +[decode] max_len=1024 generated=89/128 +[decode] max_len=1024 generated=87/128 +[decode] max_len=1024 generated=90/128 +[decode] max_len=1024 generated=88/128 +[decode] max_len=1024 generated=91/128 +[decode] max_len=1024 generated=89/128 +[decode] max_len=1024 generated=92/128 +[decode] max_len=1024 generated=90/128 +[decode] max_len=1024 generated=93/128 +[decode] max_len=1024 generated=91/128 +[decode] max_len=1024 generated=94/128 +[decode] max_len=1024 generated=92/128 +[decode] max_len=1024 generated=95/128 +[decode] max_len=1024 generated=93/128 +[decode] max_len=1024 generated=96/128 +[decode] max_len=1024 generated=94/128 +[decode] max_len=1024 generated=97/128 +[decode] max_len=1024 generated=95/128 +[decode] max_len=1024 generated=98/128 +[decode] max_len=1024 generated=96/128 +[decode] max_len=1024 generated=99/128 +[decode] max_len=1024 generated=97/128 +[decode] max_len=1024 generated=100/128 +[decode] max_len=1024 generated=98/128 +[decode] max_len=1024 generated=101/128 +[decode] max_len=1024 generated=99/128 +[decode] max_len=1024 generated=102/128 +[decode] max_len=1024 generated=100/128 +[decode] max_len=1024 generated=103/128 +[decode] max_len=1024 generated=101/128 +[decode] max_len=1024 generated=104/128 +[decode] max_len=1024 generated=102/128 +[decode] max_len=1024 generated=105/128 +[decode] max_len=1024 generated=103/128 +[decode] max_len=1024 generated=106/128 +[decode] max_len=1024 generated=104/128 +[decode] max_len=1024 generated=107/128 +[decode] max_len=1024 generated=105/128 +[decode] max_len=1024 generated=108/128 +[decode] max_len=1024 generated=106/128 +[decode] max_len=1024 generated=109/128 +[decode] max_len=1024 generated=107/128 +[decode] max_len=1024 generated=110/128 +[decode] max_len=1024 generated=108/128 +[decode] max_len=1024 generated=111/128 +[decode] max_len=1024 generated=109/128 +[decode] max_len=1024 generated=112/128 +[decode] max_len=1024 generated=110/128 +[decode] max_len=1024 generated=113/128 +[decode] max_len=1024 generated=111/128 +[decode] max_len=1024 generated=114/128 +[decode] max_len=1024 generated=112/128 +[decode] max_len=1024 generated=115/128 +[decode] max_len=1024 generated=113/128 +[decode] max_len=1024 generated=116/128 +[decode] max_len=1024 generated=114/128 +[decode] max_len=1024 generated=117/128 +[decode] max_len=1024 generated=115/128 +[decode] max_len=1024 generated=118/128 +[decode] max_len=1024 generated=116/128 +[decode] max_len=1024 generated=119/128 +[decode] max_len=1024 generated=117/128 +[decode] max_len=1024 generated=120/128 +[decode] max_len=1024 generated=118/128 +[decode] max_len=1024 generated=121/128 +[decode] max_len=1024 generated=119/128 +[decode] max_len=1024 generated=122/128 +[decode] max_len=1024 generated=120/128 +[decode] max_len=1024 generated=123/128 +[decode] max_len=1024 generated=121/128 +[decode] max_len=1024 generated=124/128 +[decode] max_len=1024 generated=122/128 +[decode] max_len=1024 generated=125/128 +[decode] max_len=1024 generated=123/128 +[decode] max_len=1024 generated=126/128 +[decode] max_len=1024 generated=124/128 +[decode] max_len=1024 generated=127/128 +[decode] max_len=1024 generated=125/128 +[decode] max_len=1024 generated=128/128 +[decode] max_len=1024 generated=126/128 +[decode] max_len=1024 generated=127/128 +[decode] max_len=1024 generated=128/128 +[ + { + "checkpoint": "runs/lta_owt_bert_absrope_time4_dirichlet_len1024_C1_to_1024_mask1_sameT_gbs512_b32_8gpu_1m_save10k_20260526/step_0050000.pt", + "ckpt_step": 50000, + "max_len": 1024, + "decode_rule": "dual_line_resample", + "support_power": 1.0, + "semantic_power": 1.5, + "steps": 128, + "c_min": 1.0, + "c_max": 1024.0, + "anchor_mode": "state", + "model_t_mode": "flow", + "time_schedule": "uniform", + "time_logit_mean": -1.5, + "time_logit_std": 0.8, + 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i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i", + "[CLS],,,,,,,,,,,,,,,,,,,,,,,,, oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation,,, oxidation oxidation oxidation,,√やややややややややややや 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i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i" + ], + "gen_ppl": 1.914934104351525, + "gen_nll": 0.6496832117802624, + "gen_tokens": 120908 + } +] +[watch-gumbel] 2026-05-26_09:13:45 done step_0050000 +[ + { + "checkpoint": "runs/lta_owt_bert_absrope_time4_dirichlet_len1024_C1_to_1024_mask1_sameT_gbs512_b32_8gpu_1m_save10k_20260526/step_0050000.pt", + "ckpt_step": 50000, + "max_len": 1024, + "decode_rule": "dual_line_resample", + "support_power": 1.0, + "semantic_power": 1.5, + "steps": 128, + "c_min": 1.0, + "c_max": 1024.0, + "anchor_mode": "state", + "model_t_mode": "flow", + "time_schedule": "uniform", + "time_logit_mean": -1.5, + "time_logit_std": 0.8, + "time_power": 2.0, + "input_noise_scale": 0.0, + "input_noise_until": 1.0, + "input_noise_dirichlet_concentration": 1.0, + "endpoint_softening": "none", + "endpoint_soft_power": 2.0, + "endpoint_soft_min_conf": 0.0, + "endpoint_soft_max_conf": 1.0, + "soft_target_decode_mode": "off", + "soft_target_power": 1.0, + "soft_target_min_conf": 0.0, + "soft_target_max_conf": 1.0, + "soft_target_debias_start": 0.7, + "final_from": "blend", + "final_decode": "argmax", + "final_sample_temp": 1.0, + "final_top_k": 0, + "final_top_p": 1.0, + "commit_mode": "off", + "commit_conf_threshold": 0.0, + "commit_margin_threshold": 0.0, + "commit_start": 0.0, + "commit_min_ratio": 0.0, + "commit_max_ratio": 1.0, + "commit_power": 2.0, + "commit_freq_max_frac": 0.08, + "early_temp": 2.8, + "late_temp": 1.45, + "temp_end": 0.55, + "temp_power": 1.5, + "pos_extend": "repeat", + "fixed_first_token_id": null, + "fixed_first_token_text": "", + "fixed_first_initial_argmax": false, + "use_ema": false, + "n_samples": 128, + "sample_entropy": 0.5168811757259412, + "unique_tokens": 377, + "token_count": 131072, + "distinct_1": 0.00287628173828125, + "distinct_2": 0.017618218475073315, + "top_token_mass": 0.7140960693359375, + "texts_preview": [ + "i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i", + "[CLS],,,,,,,,,,,,,,,,,,,,,,,,, oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation oxidation,,, oxidation oxidation oxidation,,√やややややややややややや oxidation,やややややややややややややややややややややややややややややχややややややややややや,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, [SEP],,,,,,,,,,,,,,,,,,,,,,,, [SEP]", + "i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i", + "i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i" + ], + "gen_ppl": 1.914934104351525, + "gen_nll": 0.6496832117802624, + "gen_tokens": 120908 + } +] +[watch-gumbel] 2026-05-26_09:13:52 done step_0050000 diff --git a/LTA_openwebtext_dualt/logs/owt_bert_absrope_time4_C1_to_1024_mask1_sameT_dualline_watch/infer_lta_owt_bert_absrope_time4_dirichlet_len1024_C1_to_1024_mask1_sameT_gbs512_b32_8gpu_1m_save10k_20260526_step_0130000.log b/LTA_openwebtext_dualt/logs/owt_bert_absrope_time4_C1_to_1024_mask1_sameT_dualline_watch/infer_lta_owt_bert_absrope_time4_dirichlet_len1024_C1_to_1024_mask1_sameT_gbs512_b32_8gpu_1m_save10k_20260526_step_0130000.log new file mode 100644 index 0000000000000000000000000000000000000000..07b5ea17acce3968a8d69c48168c1f1dabe3f951 --- /dev/null +++ b/LTA_openwebtext_dualt/logs/owt_bert_absrope_time4_C1_to_1024_mask1_sameT_dualline_watch/infer_lta_owt_bert_absrope_time4_dirichlet_len1024_C1_to_1024_mask1_sameT_gbs512_b32_8gpu_1m_save10k_20260526_step_0130000.log @@ -0,0 +1,398 @@ +[watch-gumbel] 2026-05-26_16:53:04 infer runs/lta_owt_bert_absrope_time4_dirichlet_len1024_C1_to_1024_mask1_sameT_gbs512_b32_8gpu_1m_save10k_20260526/step_0130000.pt -> docs/lta_samples/metrics_20260526/owt_bert_absrope_time4_C1_to_1024_mask1_sameT_dualline_dirres_c1_1024_n128/lta_owt_bert_absrope_time4_dirichlet_len1024_C1_to_1024_mask1_sameT_gbs512_b32_8gpu_1m_save10k_20260526/step_0130000 +[watch-gumbel] 2026-05-26_16:53:08 infer runs/lta_owt_bert_absrope_time4_dirichlet_len1024_C1_to_1024_mask1_sameT_gbs512_b32_8gpu_1m_save10k_20260526/step_0130000.pt -> docs/lta_samples/metrics_20260526/owt_bert_absrope_time4_C1_to_1024_mask1_sameT_dualline_dirres_c1_1024_n128/lta_owt_bert_absrope_time4_dirichlet_len1024_C1_to_1024_mask1_sameT_gbs512_b32_8gpu_1m_save10k_20260526/step_0130000 +[decode] max_len=1024 generated=1/128 +[decode] max_len=1024 generated=2/128 +[decode] max_len=1024 generated=1/128 +[decode] max_len=1024 generated=3/128 +[decode] max_len=1024 generated=2/128 +[decode] max_len=1024 generated=4/128 +[decode] max_len=1024 generated=3/128 +[decode] max_len=1024 generated=5/128 +[decode] max_len=1024 generated=4/128 +[decode] max_len=1024 generated=6/128 +[decode] max_len=1024 generated=5/128 +[decode] max_len=1024 generated=7/128 +[decode] max_len=1024 generated=6/128 +[decode] max_len=1024 generated=8/128 +[decode] max_len=1024 generated=7/128 +[decode] max_len=1024 generated=9/128 +[decode] max_len=1024 generated=8/128 +[decode] max_len=1024 generated=10/128 +[decode] max_len=1024 generated=9/128 +[decode] max_len=1024 generated=11/128 +[decode] max_len=1024 generated=10/128 +[decode] max_len=1024 generated=12/128 +[decode] max_len=1024 generated=11/128 +[decode] max_len=1024 generated=13/128 +[decode] max_len=1024 generated=12/128 +[decode] max_len=1024 generated=14/128 +[decode] max_len=1024 generated=13/128 +[decode] max_len=1024 generated=15/128 +[decode] max_len=1024 generated=14/128 +[decode] max_len=1024 generated=16/128 +[decode] max_len=1024 generated=15/128 +[decode] max_len=1024 generated=17/128 +[decode] max_len=1024 generated=16/128 +[decode] max_len=1024 generated=18/128 +[decode] max_len=1024 generated=17/128 +[decode] max_len=1024 generated=19/128 +[decode] max_len=1024 generated=18/128 +[decode] max_len=1024 generated=20/128 +[decode] max_len=1024 generated=19/128 +[decode] max_len=1024 generated=21/128 +[decode] max_len=1024 generated=20/128 +[decode] max_len=1024 generated=22/128 +[decode] max_len=1024 generated=21/128 +[decode] max_len=1024 generated=23/128 +[decode] max_len=1024 generated=22/128 +[decode] max_len=1024 generated=24/128 +[decode] max_len=1024 generated=23/128 +[decode] max_len=1024 generated=25/128 +[decode] max_len=1024 generated=24/128 +[decode] max_len=1024 generated=26/128 +[decode] max_len=1024 generated=25/128 +[decode] max_len=1024 generated=27/128 +[decode] max_len=1024 generated=26/128 +[decode] max_len=1024 generated=28/128 +[decode] max_len=1024 generated=27/128 +[decode] max_len=1024 generated=29/128 +[decode] max_len=1024 generated=28/128 +[decode] max_len=1024 generated=30/128 +[decode] max_len=1024 generated=29/128 +[decode] max_len=1024 generated=31/128 +[decode] max_len=1024 generated=30/128 +[decode] max_len=1024 generated=32/128 +[decode] max_len=1024 generated=31/128 +[decode] max_len=1024 generated=33/128 +[decode] max_len=1024 generated=32/128 +[decode] max_len=1024 generated=34/128 +[decode] max_len=1024 generated=33/128 +[decode] max_len=1024 generated=35/128 +[decode] max_len=1024 generated=34/128 +[decode] max_len=1024 generated=36/128 +[decode] max_len=1024 generated=35/128 +[decode] max_len=1024 generated=37/128 +[decode] max_len=1024 generated=36/128 +[decode] max_len=1024 generated=38/128 +[decode] max_len=1024 generated=37/128 +[decode] max_len=1024 generated=39/128 +[decode] max_len=1024 generated=38/128 +[decode] max_len=1024 generated=40/128 +[decode] max_len=1024 generated=39/128 +[decode] max_len=1024 generated=41/128 +[decode] max_len=1024 generated=40/128 +[decode] max_len=1024 generated=42/128 +[decode] max_len=1024 generated=41/128 +[decode] max_len=1024 generated=43/128 +[decode] max_len=1024 generated=42/128 +[decode] max_len=1024 generated=44/128 +[decode] max_len=1024 generated=43/128 +[decode] max_len=1024 generated=45/128 +[decode] max_len=1024 generated=44/128 +[decode] max_len=1024 generated=46/128 +[decode] max_len=1024 generated=45/128 +[decode] max_len=1024 generated=47/128 +[decode] max_len=1024 generated=46/128 +[decode] max_len=1024 generated=48/128 +[decode] max_len=1024 generated=47/128 +[decode] max_len=1024 generated=49/128 +[decode] max_len=1024 generated=48/128 +[decode] max_len=1024 generated=50/128 +[decode] max_len=1024 generated=49/128 +[decode] max_len=1024 generated=51/128 +[decode] max_len=1024 generated=50/128 +[decode] max_len=1024 generated=52/128 +[decode] max_len=1024 generated=51/128 +[decode] max_len=1024 generated=53/128 +[decode] max_len=1024 generated=52/128 +[decode] max_len=1024 generated=54/128 +[decode] max_len=1024 generated=53/128 +[decode] max_len=1024 generated=55/128 +[decode] max_len=1024 generated=54/128 +[decode] max_len=1024 generated=56/128 +[decode] max_len=1024 generated=55/128 +[decode] max_len=1024 generated=57/128 +[decode] max_len=1024 generated=56/128 +[decode] max_len=1024 generated=58/128 +[decode] max_len=1024 generated=57/128 +[decode] max_len=1024 generated=59/128 +[decode] max_len=1024 generated=58/128 +[decode] max_len=1024 generated=60/128 +[decode] max_len=1024 generated=59/128 +[decode] max_len=1024 generated=61/128 +[decode] max_len=1024 generated=60/128 +[decode] max_len=1024 generated=61/128 +[decode] max_len=1024 generated=62/128 +[decode] max_len=1024 generated=62/128 +[decode] max_len=1024 generated=63/128 +[decode] max_len=1024 generated=63/128 +[decode] max_len=1024 generated=64/128 +[decode] max_len=1024 generated=64/128 +[decode] max_len=1024 generated=65/128 +[decode] max_len=1024 generated=65/128 +[decode] max_len=1024 generated=66/128 +[decode] max_len=1024 generated=66/128 +[decode] max_len=1024 generated=67/128 +[decode] max_len=1024 generated=67/128 +[decode] max_len=1024 generated=68/128 +[decode] max_len=1024 generated=68/128 +[decode] max_len=1024 generated=69/128 +[decode] max_len=1024 generated=69/128 +[decode] max_len=1024 generated=70/128 +[decode] max_len=1024 generated=70/128 +[decode] max_len=1024 generated=71/128 +[decode] max_len=1024 generated=71/128 +[decode] max_len=1024 generated=72/128 +[decode] max_len=1024 generated=72/128 +[decode] max_len=1024 generated=73/128 +[decode] max_len=1024 generated=73/128 +[decode] max_len=1024 generated=74/128 +[decode] max_len=1024 generated=74/128 +[decode] max_len=1024 generated=75/128 +[decode] max_len=1024 generated=75/128 +[decode] max_len=1024 generated=76/128 +[decode] max_len=1024 generated=76/128 +[decode] max_len=1024 generated=77/128 +[decode] max_len=1024 generated=77/128 +[decode] max_len=1024 generated=78/128 +[decode] max_len=1024 generated=78/128 +[decode] max_len=1024 generated=79/128 +[decode] max_len=1024 generated=79/128 +[decode] max_len=1024 generated=80/128 +[decode] max_len=1024 generated=80/128 +[decode] max_len=1024 generated=81/128 +[decode] max_len=1024 generated=81/128 +[decode] max_len=1024 generated=82/128 +[decode] max_len=1024 generated=82/128 +[decode] max_len=1024 generated=83/128 +[decode] max_len=1024 generated=83/128 +[decode] max_len=1024 generated=84/128 +[decode] max_len=1024 generated=84/128 +[decode] max_len=1024 generated=85/128 +[decode] max_len=1024 generated=85/128 +[decode] max_len=1024 generated=86/128 +[decode] max_len=1024 generated=86/128 +[decode] max_len=1024 generated=87/128 +[decode] max_len=1024 generated=87/128 +[decode] max_len=1024 generated=88/128 +[decode] max_len=1024 generated=88/128 +[decode] max_len=1024 generated=89/128 +[decode] max_len=1024 generated=89/128 +[decode] max_len=1024 generated=90/128 +[decode] max_len=1024 generated=90/128 +[decode] max_len=1024 generated=91/128 +[decode] max_len=1024 generated=91/128 +[decode] max_len=1024 generated=92/128 +[decode] max_len=1024 generated=92/128 +[decode] max_len=1024 generated=93/128 +[decode] max_len=1024 generated=93/128 +[decode] max_len=1024 generated=94/128 +[decode] max_len=1024 generated=94/128 +[decode] max_len=1024 generated=95/128 +[decode] max_len=1024 generated=95/128 +[decode] max_len=1024 generated=96/128 +[decode] max_len=1024 generated=96/128 +[decode] max_len=1024 generated=97/128 +[decode] max_len=1024 generated=97/128 +[decode] max_len=1024 generated=98/128 +[decode] max_len=1024 generated=98/128 +[decode] max_len=1024 generated=99/128 +[decode] max_len=1024 generated=99/128 +[decode] max_len=1024 generated=100/128 +[decode] max_len=1024 generated=100/128 +[decode] max_len=1024 generated=101/128 +[decode] max_len=1024 generated=101/128 +[decode] max_len=1024 generated=102/128 +[decode] max_len=1024 generated=102/128 +[decode] max_len=1024 generated=103/128 +[decode] max_len=1024 generated=103/128 +[decode] max_len=1024 generated=104/128 +[decode] max_len=1024 generated=104/128 +[decode] max_len=1024 generated=105/128 +[decode] max_len=1024 generated=105/128 +[decode] max_len=1024 generated=106/128 +[decode] max_len=1024 generated=106/128 +[decode] max_len=1024 generated=107/128 +[decode] max_len=1024 generated=107/128 +[decode] max_len=1024 generated=108/128 +[decode] max_len=1024 generated=108/128 +[decode] max_len=1024 generated=109/128 +[decode] max_len=1024 generated=109/128 +[decode] max_len=1024 generated=110/128 +[decode] max_len=1024 generated=110/128 +[decode] max_len=1024 generated=111/128 +[decode] max_len=1024 generated=111/128 +[decode] max_len=1024 generated=112/128 +[decode] max_len=1024 generated=112/128 +[decode] max_len=1024 generated=113/128 +[decode] max_len=1024 generated=113/128 +[decode] max_len=1024 generated=114/128 +[decode] max_len=1024 generated=114/128 +[decode] max_len=1024 generated=115/128 +[decode] max_len=1024 generated=115/128 +[decode] max_len=1024 generated=116/128 +[decode] max_len=1024 generated=116/128 +[decode] max_len=1024 generated=117/128 +[decode] max_len=1024 generated=117/128 +[decode] max_len=1024 generated=118/128 +[decode] max_len=1024 generated=118/128 +[decode] max_len=1024 generated=119/128 +[decode] max_len=1024 generated=119/128 +[decode] max_len=1024 generated=120/128 +[decode] max_len=1024 generated=120/128 +[decode] max_len=1024 generated=121/128 +[decode] max_len=1024 generated=121/128 +[decode] max_len=1024 generated=122/128 +[decode] max_len=1024 generated=122/128 +[decode] max_len=1024 generated=123/128 +[decode] max_len=1024 generated=123/128 +[decode] max_len=1024 generated=124/128 +[decode] max_len=1024 generated=124/128 +[decode] max_len=1024 generated=125/128 +[decode] max_len=1024 generated=125/128 +[decode] max_len=1024 generated=126/128 +[decode] max_len=1024 generated=126/128 +[decode] max_len=1024 generated=127/128 +[decode] max_len=1024 generated=127/128 +[decode] max_len=1024 generated=128/128 +[decode] max_len=1024 generated=128/128 +[ + { + "checkpoint": "runs/lta_owt_bert_absrope_time4_dirichlet_len1024_C1_to_1024_mask1_sameT_gbs512_b32_8gpu_1m_save10k_20260526/step_0130000.pt", + "ckpt_step": 130000, + "max_len": 1024, + "decode_rule": "dual_line_resample", + "support_power": 1.0, + "semantic_power": 1.5, + "steps": 128, + "c_min": 1.0, + "c_max": 1024.0, + "anchor_mode": "state", + "model_t_mode": "flow", + "time_schedule": "uniform", + "time_logit_mean": -1.5, + "time_logit_std": 0.8, + 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loss_all=10.8041 acc_all=0.3027 loss_corrupt=10.8101 acc_corrupt=0.2199 corrupt_frac=0.3330 loss=10.8101 loss_recon=10.8101 loss_meanflow=0.0000 mean_model_t=0.7491 mean_corrupt_t=0.7061 wrong_frac=0.3050 init_acc_corrupt=0.6950 init_gold_top10=0.6950 init_gold_top100=0.6950 +step=3 micro_steps=3 elapsed=0.0s lr=3.000000e-04 loss_all=10.7836 acc_all=0.2432 loss_corrupt=10.7924 acc_corrupt=0.1938 corrupt_frac=0.8516 loss=10.7924 loss_recon=10.7924 loss_meanflow=0.0000 mean_model_t=0.2118 mean_corrupt_t=0.3812 wrong_frac=0.6250 init_acc_corrupt=0.3635 init_gold_top10=0.3750 init_gold_top100=0.3750 diff --git a/LTA_openwebtext_dualt/logs/train8ctx8_allcorrupt/driver.log b/LTA_openwebtext_dualt/logs/train8ctx8_allcorrupt/driver.log new file mode 100644 index 0000000000000000000000000000000000000000..e29ca164ebaada0ff9697fb5a1adb69dec4c9a1f --- /dev/null +++ b/LTA_openwebtext_dualt/logs/train8ctx8_allcorrupt/driver.log @@ -0,0 +1,670 @@ +[allcorrupt] start 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epoch_step=2/2 micro_steps=130 elapsed=5.2s lr=2.000000e-03 loss=8.7281 loss_recon=8.7281 loss_meanflow=0.0000 mean_model_t=0.2286 mean_corrupt_t=0.2286 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2375 corrupt_frac=1.0000 acc_corrupt=0.2375 loss_corrupt=8.7281 wrong_frac=0.7875 init_acc_corrupt=0.1375 acc_corrupt_t_0p0_0p2=0.1667 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=1.1458 out_g_norm=9.8395 acc_corrupt_t_0p2_0p4=0.2679 corrupt_frac_t_0p2_0p4=1.0000 loss_all=9.1562 init_gold_top10=0.1250 init_gold_top100=0.1250 +step=140 epoch=70/250 epoch_step=2/2 micro_steps=140 elapsed=4.6s lr=2.000000e-03 loss=8.8338 loss_recon=8.8338 loss_meanflow=0.0000 mean_model_t=0.1682 mean_corrupt_t=0.1682 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.1625 corrupt_frac=1.0000 acc_corrupt=0.1625 loss_corrupt=8.8338 wrong_frac=0.8375 init_acc_corrupt=0.0500 acc_corrupt_t_0p0_0p2=0.1250 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=1.2408 out_g_norm=9.9622 acc_corrupt_t_0p2_0p4=0.2500 corrupt_frac_t_0p2_0p4=1.0000 loss_all=9.0508 init_gold_top10=0.1250 init_gold_top100=0.1250 +step=150 epoch=75/250 epoch_step=2/2 micro_steps=150 elapsed=4.2s lr=2.000000e-03 loss=8.2799 loss_recon=8.2799 loss_meanflow=0.0000 mean_model_t=0.2326 mean_corrupt_t=0.2326 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2500 corrupt_frac=1.0000 acc_corrupt=0.2500 loss_corrupt=8.2799 wrong_frac=0.7625 init_acc_corrupt=0.1375 acc_corrupt_t_0p0_0p2=0.1750 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=1.3332 out_g_norm=10.2737 acc_corrupt_t_0p2_0p4=0.2812 corrupt_frac_t_0p2_0p4=1.0000 acc_corrupt_t_0p4_0p6=0.5000 corrupt_frac_t_0p4_0p6=1.0000 loss_all=8.1875 init_gold_top10=0.2500 init_gold_top100=0.2500 +step=160 epoch=80/250 epoch_step=2/2 micro_steps=160 elapsed=4.7s lr=2.000000e-03 loss=7.4644 loss_recon=7.4644 loss_meanflow=0.0000 mean_model_t=0.1882 mean_corrupt_t=0.1882 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.3125 corrupt_frac=1.0000 acc_corrupt=0.3125 loss_corrupt=7.4644 wrong_frac=0.7500 init_acc_corrupt=0.1750 acc_corrupt_t_0p2_0p4=0.4750 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=1.4282 out_g_norm=10.1290 acc_corrupt_t_0p0_0p2=0.1500 corrupt_frac_t_0p0_0p2=1.0000 loss_all=8.6543 init_gold_top10=0.0000 init_gold_top100=0.0000 +step=170 epoch=85/250 epoch_step=2/2 micro_steps=170 elapsed=4.5s lr=2.000000e-03 loss=7.5713 loss_recon=7.5713 loss_meanflow=0.0000 mean_model_t=0.2017 mean_corrupt_t=0.2017 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2500 corrupt_frac=1.0000 acc_corrupt=0.2500 loss_corrupt=7.5713 wrong_frac=0.7250 init_acc_corrupt=0.1000 acc_corrupt_t_0p2_0p4=0.4000 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=1.5251 out_g_norm=10.4161 acc_corrupt_t_0p0_0p2=0.1000 corrupt_frac_t_0p0_0p2=1.0000 loss_all=5.2729 init_gold_top10=0.5000 init_gold_top100=0.6250 +step=180 epoch=90/250 epoch_step=2/2 micro_steps=180 elapsed=4.2s lr=2.000000e-03 loss=7.6099 loss_recon=7.6099 loss_meanflow=0.0000 mean_model_t=0.2081 mean_corrupt_t=0.2081 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2250 corrupt_frac=1.0000 acc_corrupt=0.2250 loss_corrupt=7.6099 wrong_frac=0.7875 init_acc_corrupt=0.1250 acc_corrupt_t_0p0_0p2=0.1458 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=1.6215 out_g_norm=9.9961 acc_corrupt_t_0p2_0p4=0.3438 corrupt_frac_t_0p2_0p4=1.0000 loss_all=7.9766 init_gold_top10=0.3750 init_gold_top100=0.5000 +step=190 epoch=95/250 epoch_step=2/2 micro_steps=190 elapsed=4.6s lr=2.000000e-03 loss=7.4357 loss_recon=7.4357 loss_meanflow=0.0000 mean_model_t=0.1942 mean_corrupt_t=0.1942 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2250 corrupt_frac=1.0000 acc_corrupt=0.2250 loss_corrupt=7.4357 wrong_frac=0.8625 init_acc_corrupt=0.0625 acc_corrupt_t_0p0_0p2=0.1667 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=1.7179 out_g_norm=10.3147 acc_corrupt_t_0p2_0p4=0.3125 corrupt_frac_t_0p2_0p4=1.0000 loss_all=8.7559 init_gold_top10=0.1250 init_gold_top100=0.1250 +step=200 epoch=100/250 epoch_step=2/2 micro_steps=200 elapsed=4.6s lr=2.000000e-03 loss=7.6464 loss_recon=7.6464 loss_meanflow=0.0000 mean_model_t=0.1970 mean_corrupt_t=0.1970 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.1625 corrupt_frac=1.0000 acc_corrupt=0.1625 loss_corrupt=7.6464 wrong_frac=0.8250 init_acc_corrupt=0.0625 acc_corrupt_t_0p0_0p2=0.1071 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=1.8126 out_g_norm=10.9069 acc_corrupt_t_0p2_0p4=0.2917 corrupt_frac_t_0p2_0p4=1.0000 loss_all=7.8730 init_gold_top10=0.0000 init_gold_top100=0.3750 +step=210 epoch=105/250 epoch_step=2/2 micro_steps=210 elapsed=4.2s lr=2.000000e-03 loss=6.5814 loss_recon=6.5814 loss_meanflow=0.0000 mean_model_t=0.1880 mean_corrupt_t=0.1880 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2375 corrupt_frac=1.0000 acc_corrupt=0.2375 loss_corrupt=6.5814 wrong_frac=0.8500 init_acc_corrupt=0.0875 acc_corrupt_t_0p2_0p4=0.2812 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=1.9072 out_g_norm=9.6478 acc_corrupt_t_0p0_0p2=0.2083 corrupt_frac_t_0p0_0p2=1.0000 loss_all=4.4038 init_gold_top10=0.2500 init_gold_top100=0.3750 +step=220 epoch=110/250 epoch_step=2/2 micro_steps=220 elapsed=4.6s lr=2.000000e-03 loss=7.1286 loss_recon=7.1286 loss_meanflow=0.0000 mean_model_t=0.1718 mean_corrupt_t=0.1718 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.1625 corrupt_frac=1.0000 acc_corrupt=0.1625 loss_corrupt=7.1286 wrong_frac=0.8500 init_acc_corrupt=0.0375 acc_corrupt_t_0p0_0p2=0.1667 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=1.9991 out_g_norm=10.3675 acc_corrupt_t_0p2_0p4=0.1562 corrupt_frac_t_0p2_0p4=1.0000 loss_all=7.7090 init_gold_top10=0.1250 init_gold_top100=0.1250 +step=230 epoch=115/250 epoch_step=2/2 micro_steps=230 elapsed=4.6s lr=2.000000e-03 loss=4.9539 loss_recon=4.9539 loss_meanflow=0.0000 mean_model_t=0.3049 mean_corrupt_t=0.3049 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.4250 corrupt_frac=1.0000 acc_corrupt=0.4250 loss_corrupt=4.9539 wrong_frac=0.6500 init_acc_corrupt=0.2875 acc_corrupt_t_0p4_0p6=0.7917 corrupt_frac_t_0p4_0p6=1.0000 out_w_norm=2.0831 out_g_norm=9.7181 acc_corrupt_t_0p2_0p4=0.4167 corrupt_frac_t_0p2_0p4=1.0000 acc_corrupt_t_0p0_0p2=0.1562 corrupt_frac_t_0p0_0p2=1.0000 loss_all=7.3464 init_gold_top10=0.2500 init_gold_top100=0.2500 +step=240 epoch=120/250 epoch_step=2/2 micro_steps=240 elapsed=4.1s lr=2.000000e-03 loss=5.3579 loss_recon=5.3579 loss_meanflow=0.0000 mean_model_t=0.2693 mean_corrupt_t=0.2693 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.3500 corrupt_frac=1.0000 acc_corrupt=0.3500 loss_corrupt=5.3579 wrong_frac=0.7125 init_acc_corrupt=0.2000 acc_corrupt_t_0p0_0p2=0.1562 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.1655 out_g_norm=10.1470 acc_corrupt_t_0p4_0p6=0.6250 corrupt_frac_t_0p4_0p6=1.0000 acc_corrupt_t_0p2_0p4=0.4062 corrupt_frac_t_0p2_0p4=1.0000 loss_all=4.7973 init_gold_top10=0.2500 init_gold_top100=0.2500 +step=250 epoch=125/250 epoch_step=2/2 micro_steps=250 elapsed=4.6s lr=2.000000e-03 loss=5.4397 loss_recon=5.4397 loss_meanflow=0.0000 mean_model_t=0.1964 mean_corrupt_t=0.1964 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.3000 corrupt_frac=1.0000 acc_corrupt=0.3000 loss_corrupt=5.4397 wrong_frac=0.7500 init_acc_corrupt=0.1250 acc_corrupt_t_0p0_0p2=0.1875 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.2462 out_g_norm=10.4779 acc_corrupt_t_0p6_0p8=0.8750 corrupt_frac_t_0p6_0p8=1.0000 acc_corrupt_t_0p2_0p4=0.6250 corrupt_frac_t_0p2_0p4=1.0000 loss_all=2.5704 init_gold_top10=0.5000 init_gold_top100=0.5000 +step=260 epoch=130/250 epoch_step=2/2 micro_steps=260 elapsed=4.5s lr=2.000000e-03 loss=6.4952 loss_recon=6.4952 loss_meanflow=0.0000 mean_model_t=0.1517 mean_corrupt_t=0.1517 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.1375 corrupt_frac=1.0000 acc_corrupt=0.1375 loss_corrupt=6.4952 wrong_frac=0.8750 init_acc_corrupt=0.0250 acc_corrupt_t_0p0_0p2=0.1250 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.3208 out_g_norm=10.8209 acc_corrupt_t_0p2_0p4=0.1667 corrupt_frac_t_0p2_0p4=1.0000 loss_all=4.7949 init_gold_top10=0.1250 init_gold_top100=0.5000 +step=270 epoch=135/250 epoch_step=2/2 micro_steps=270 elapsed=4.2s lr=2.000000e-03 loss=5.5522 loss_recon=5.5522 loss_meanflow=0.0000 mean_model_t=0.1781 mean_corrupt_t=0.1781 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2625 corrupt_frac=1.0000 acc_corrupt=0.2625 loss_corrupt=5.5522 wrong_frac=0.7750 init_acc_corrupt=0.0875 acc_corrupt_t_0p0_0p2=0.1964 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.3848 out_g_norm=10.4100 acc_corrupt_t_0p2_0p4=0.4167 corrupt_frac_t_0p2_0p4=1.0000 loss_all=4.8256 init_gold_top10=0.3750 init_gold_top100=0.3750 +step=280 epoch=140/250 epoch_step=2/2 micro_steps=280 elapsed=5.0s lr=2.000000e-03 loss=5.8259 loss_recon=5.8259 loss_meanflow=0.0000 mean_model_t=0.2199 mean_corrupt_t=0.2199 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.1625 corrupt_frac=1.0000 acc_corrupt=0.1625 loss_corrupt=5.8259 wrong_frac=0.8000 init_acc_corrupt=0.0625 acc_corrupt_t_0p4_0p6=0.1250 corrupt_frac_t_0p4_0p6=1.0000 out_w_norm=2.4410 out_g_norm=10.8257 acc_corrupt_t_0p0_0p2=0.1250 corrupt_frac_t_0p0_0p2=1.0000 acc_corrupt_t_0p2_0p4=0.2188 corrupt_frac_t_0p2_0p4=1.0000 loss_all=5.9141 init_gold_top10=0.1250 init_gold_top100=0.2500 +step=290 epoch=145/250 epoch_step=2/2 micro_steps=290 elapsed=4.5s lr=2.000000e-03 loss=4.2819 loss_recon=4.2819 loss_meanflow=0.0000 mean_model_t=0.2100 mean_corrupt_t=0.2100 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.3250 corrupt_frac=1.0000 acc_corrupt=0.3250 loss_corrupt=4.2819 wrong_frac=0.7625 init_acc_corrupt=0.1625 acc_corrupt_t_0p2_0p4=0.4500 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=2.4965 out_g_norm=10.5429 acc_corrupt_t_0p0_0p2=0.2000 corrupt_frac_t_0p0_0p2=1.0000 loss_all=4.9616 init_gold_top10=0.2500 init_gold_top100=0.2500 +step=300 epoch=150/250 epoch_step=2/2 micro_steps=300 elapsed=4.2s lr=2.000000e-03 loss=4.6966 loss_recon=4.6966 loss_meanflow=0.0000 mean_model_t=0.2037 mean_corrupt_t=0.2037 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.3000 corrupt_frac=1.0000 acc_corrupt=0.3000 loss_corrupt=4.6966 wrong_frac=0.7250 init_acc_corrupt=0.0875 acc_corrupt_t_0p0_0p2=0.2500 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.5467 out_g_norm=10.7409 acc_corrupt_t_0p2_0p4=0.3500 corrupt_frac_t_0p2_0p4=1.0000 loss_all=5.9668 init_gold_top10=0.1250 init_gold_top100=0.5000 +step=310 epoch=155/250 epoch_step=2/2 micro_steps=310 elapsed=5.0s lr=2.000000e-03 loss=4.8928 loss_recon=4.8928 loss_meanflow=0.0000 mean_model_t=0.1660 mean_corrupt_t=0.1660 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2750 corrupt_frac=1.0000 acc_corrupt=0.2750 loss_corrupt=4.8928 wrong_frac=0.8500 init_acc_corrupt=0.0875 acc_corrupt_t_0p0_0p2=0.2321 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.5929 out_g_norm=10.7503 acc_corrupt_t_0p4_0p6=0.8750 corrupt_frac_t_0p4_0p6=1.0000 acc_corrupt_t_0p2_0p4=0.1250 corrupt_frac_t_0p2_0p4=1.0000 loss_all=6.3398 init_gold_top10=0.0000 init_gold_top100=0.0000 +step=320 epoch=160/250 epoch_step=2/2 micro_steps=320 elapsed=4.5s lr=2.000000e-03 loss=4.6516 loss_recon=4.6516 loss_meanflow=0.0000 mean_model_t=0.2203 mean_corrupt_t=0.2203 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2125 corrupt_frac=1.0000 acc_corrupt=0.2125 loss_corrupt=4.6516 wrong_frac=0.8250 init_acc_corrupt=0.1125 acc_corrupt_t_0p0_0p2=0.1458 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.6345 out_g_norm=10.9623 acc_corrupt_t_0p4_0p6=0.2500 corrupt_frac_t_0p4_0p6=1.0000 acc_corrupt_t_0p2_0p4=0.3333 corrupt_frac_t_0p2_0p4=1.0000 loss_all=4.9951 init_gold_top10=0.0000 init_gold_top100=0.3750 +step=330 epoch=165/250 epoch_step=2/2 micro_steps=330 elapsed=4.2s lr=2.000000e-03 loss=3.6249 loss_recon=3.6249 loss_meanflow=0.0000 mean_model_t=0.2293 mean_corrupt_t=0.2293 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.3625 corrupt_frac=1.0000 acc_corrupt=0.3625 loss_corrupt=3.6249 wrong_frac=0.6750 init_acc_corrupt=0.2125 acc_corrupt_t_0p0_0p2=0.1562 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.6737 out_g_norm=11.0498 acc_corrupt_t_0p2_0p4=0.5000 corrupt_frac_t_0p2_0p4=1.0000 loss_all=1.3865 init_gold_top10=0.6250 init_gold_top100=0.6250 +step=340 epoch=170/250 epoch_step=2/2 micro_steps=340 elapsed=5.1s lr=2.000000e-03 loss=3.6906 loss_recon=3.6906 loss_meanflow=0.0000 mean_model_t=0.2682 mean_corrupt_t=0.2682 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.4000 corrupt_frac=1.0000 acc_corrupt=0.4000 loss_corrupt=3.6906 wrong_frac=0.6875 init_acc_corrupt=0.2375 acc_corrupt_t_0p0_0p2=0.1250 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.7084 out_g_norm=10.1418 acc_corrupt_t_0p2_0p4=0.5417 corrupt_frac_t_0p2_0p4=1.0000 acc_corrupt_t_0p4_0p6=0.7500 corrupt_frac_t_0p4_0p6=1.0000 acc_corrupt_t_0p6_0p8=1.0000 corrupt_frac_t_0p6_0p8=1.0000 loss_all=5.2021 init_gold_top10=0.1250 init_gold_top100=0.1250 +step=350 epoch=175/250 epoch_step=2/2 micro_steps=350 elapsed=4.5s lr=2.000000e-03 loss=4.6338 loss_recon=4.6338 loss_meanflow=0.0000 mean_model_t=0.1929 mean_corrupt_t=0.1929 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2000 corrupt_frac=1.0000 acc_corrupt=0.2000 loss_corrupt=4.6338 wrong_frac=0.8375 init_acc_corrupt=0.0750 acc_corrupt_t_0p2_0p4=0.3250 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=2.7401 out_g_norm=11.0149 acc_corrupt_t_0p0_0p2=0.0750 corrupt_frac_t_0p0_0p2=1.0000 loss_all=3.8555 init_gold_top10=0.1250 init_gold_top100=0.2500 +step=360 epoch=180/250 epoch_step=2/2 micro_steps=360 elapsed=4.2s lr=2.000000e-03 loss=4.2104 loss_recon=4.2104 loss_meanflow=0.0000 mean_model_t=0.1923 mean_corrupt_t=0.1923 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2000 corrupt_frac=1.0000 acc_corrupt=0.2000 loss_corrupt=4.2104 wrong_frac=0.7875 init_acc_corrupt=0.1000 acc_corrupt_t_0p0_0p2=0.1250 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.7663 out_g_norm=11.0627 acc_corrupt_t_0p4_0p6=0.5000 corrupt_frac_t_0p4_0p6=1.0000 loss_all=1.9618 init_gold_top10=0.2500 init_gold_top100=0.2500 +step=370 epoch=185/250 epoch_step=2/2 micro_steps=370 elapsed=5.0s lr=2.000000e-03 loss=3.6980 loss_recon=3.6980 loss_meanflow=0.0000 mean_model_t=0.1757 mean_corrupt_t=0.1757 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.3125 corrupt_frac=1.0000 acc_corrupt=0.3125 loss_corrupt=3.6980 wrong_frac=0.8250 init_acc_corrupt=0.0750 acc_corrupt_t_0p2_0p4=0.5625 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=2.7910 out_g_norm=10.5404 acc_corrupt_t_0p0_0p2=0.2500 corrupt_frac_t_0p0_0p2=1.0000 loss_all=3.2881 init_gold_top10=0.2500 init_gold_top100=0.5000 +step=380 epoch=190/250 epoch_step=2/2 micro_steps=380 elapsed=4.5s lr=2.000000e-03 loss=3.2751 loss_recon=3.2751 loss_meanflow=0.0000 mean_model_t=0.1794 mean_corrupt_t=0.1794 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2625 corrupt_frac=1.0000 acc_corrupt=0.2625 loss_corrupt=3.2751 wrong_frac=0.8375 init_acc_corrupt=0.1000 acc_corrupt_t_0p0_0p2=0.1607 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.8162 out_g_norm=11.1712 acc_corrupt_t_0p4_0p6=0.5000 corrupt_frac_t_0p4_0p6=1.0000 acc_corrupt_t_0p2_0p4=0.5000 corrupt_frac_t_0p2_0p4=1.0000 loss_all=2.6837 init_gold_top10=0.2500 init_gold_top100=0.2500 +step=390 epoch=195/250 epoch_step=2/2 micro_steps=390 elapsed=4.2s lr=2.000000e-03 loss=4.0428 loss_recon=4.0428 loss_meanflow=0.0000 mean_model_t=0.1873 mean_corrupt_t=0.1873 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2250 corrupt_frac=1.0000 acc_corrupt=0.2250 loss_corrupt=4.0428 wrong_frac=0.8125 init_acc_corrupt=0.0750 acc_corrupt_t_0p2_0p4=0.2812 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=2.8404 out_g_norm=12.1307 acc_corrupt_t_0p0_0p2=0.1875 corrupt_frac_t_0p0_0p2=1.0000 loss_all=5.0107 init_gold_top10=0.1250 init_gold_top100=0.5000 +step=400 epoch=200/250 epoch_step=2/2 micro_steps=400 elapsed=5.0s lr=2.000000e-03 loss=3.0034 loss_recon=3.0034 loss_meanflow=0.0000 mean_model_t=0.3288 mean_corrupt_t=0.3288 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.3625 corrupt_frac=1.0000 acc_corrupt=0.3625 loss_corrupt=3.0034 wrong_frac=0.7250 init_acc_corrupt=0.2250 acc_corrupt_t_0p0_0p2=0.1250 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.8626 out_g_norm=10.6248 acc_corrupt_t_0p2_0p4=0.2188 corrupt_frac_t_0p2_0p4=1.0000 acc_corrupt_t_0p6_0p8=0.8750 corrupt_frac_t_0p6_0p8=1.0000 acc_corrupt_t_0p4_0p6=0.7500 corrupt_frac_t_0p4_0p6=1.0000 loss_all=0.2258 init_gold_top10=0.7500 init_gold_top100=0.7500 +step=410 epoch=205/250 epoch_step=2/2 micro_steps=410 elapsed=4.6s lr=2.000000e-03 loss=2.5523 loss_recon=2.5523 loss_meanflow=0.0000 mean_model_t=0.2749 mean_corrupt_t=0.2749 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.4500 corrupt_frac=1.0000 acc_corrupt=0.4500 loss_corrupt=2.5523 wrong_frac=0.6750 init_acc_corrupt=0.2375 acc_corrupt_t_0p0_0p2=0.1250 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.8829 out_g_norm=11.8955 acc_corrupt_t_0p2_0p4=0.5750 corrupt_frac_t_0p2_0p4=1.0000 acc_corrupt_t_0p4_0p6=0.6250 corrupt_frac_t_0p4_0p6=1.0000 loss_all=1.8015 init_gold_top10=0.3750 init_gold_top100=0.5000 +step=420 epoch=210/250 epoch_step=2/2 micro_steps=420 elapsed=4.1s lr=2.000000e-03 loss=3.2439 loss_recon=3.2439 loss_meanflow=0.0000 mean_model_t=0.2123 mean_corrupt_t=0.2123 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2750 corrupt_frac=1.0000 acc_corrupt=0.2750 loss_corrupt=3.2439 wrong_frac=0.7750 init_acc_corrupt=0.1000 acc_corrupt_t_0p2_0p4=0.3333 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=2.8965 out_g_norm=11.8560 acc_corrupt_t_0p0_0p2=0.1875 corrupt_frac_t_0p0_0p2=1.0000 loss_all=4.2246 init_gold_top10=0.1250 init_gold_top100=0.3750 +step=430 epoch=215/250 epoch_step=2/2 micro_steps=430 elapsed=5.0s lr=2.000000e-03 loss=3.1938 loss_recon=3.1938 loss_meanflow=0.0000 mean_model_t=0.1772 mean_corrupt_t=0.1772 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2625 corrupt_frac=1.0000 acc_corrupt=0.2625 loss_corrupt=3.1938 wrong_frac=0.8250 init_acc_corrupt=0.0875 acc_corrupt_t_0p0_0p2=0.2000 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.9060 out_g_norm=11.6931 acc_corrupt_t_0p2_0p4=0.3250 corrupt_frac_t_0p2_0p4=1.0000 loss_all=3.3418 init_gold_top10=0.1250 init_gold_top100=0.3750 +step=440 epoch=220/250 epoch_step=2/2 micro_steps=440 elapsed=4.6s lr=2.000000e-03 loss=2.7462 loss_recon=2.7462 loss_meanflow=0.0000 mean_model_t=0.2333 mean_corrupt_t=0.2333 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.3625 corrupt_frac=1.0000 acc_corrupt=0.3625 loss_corrupt=2.7462 wrong_frac=0.6875 init_acc_corrupt=0.1875 acc_corrupt_t_0p0_0p2=0.1250 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.9144 out_g_norm=11.5046 acc_corrupt_t_0p2_0p4=0.5208 corrupt_frac_t_0p2_0p4=1.0000 loss_all=3.6408 init_gold_top10=0.3750 init_gold_top100=0.6250 +step=450 epoch=225/250 epoch_step=2/2 micro_steps=450 elapsed=4.2s lr=2.000000e-03 loss=3.8439 loss_recon=3.8439 loss_meanflow=0.0000 mean_model_t=0.1578 mean_corrupt_t=0.1578 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.1625 corrupt_frac=1.0000 acc_corrupt=0.1625 loss_corrupt=3.8439 wrong_frac=0.8375 init_acc_corrupt=0.0500 acc_corrupt_t_0p0_0p2=0.1250 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.9216 out_g_norm=10.7069 acc_corrupt_t_0p2_0p4=0.2500 corrupt_frac_t_0p2_0p4=1.0000 loss_all=4.7910 init_gold_top10=0.0000 init_gold_top100=0.0000 +step=460 epoch=230/250 epoch_step=2/2 micro_steps=460 elapsed=5.0s lr=2.000000e-03 loss=3.0667 loss_recon=3.0667 loss_meanflow=0.0000 mean_model_t=0.1884 mean_corrupt_t=0.1884 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.3000 corrupt_frac=1.0000 acc_corrupt=0.3000 loss_corrupt=3.0667 wrong_frac=0.8250 init_acc_corrupt=0.1000 acc_corrupt_t_0p0_0p2=0.1429 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.9302 out_g_norm=11.1555 acc_corrupt_t_0p4_0p6=0.7500 corrupt_frac_t_0p4_0p6=1.0000 acc_corrupt_t_0p2_0p4=0.5000 corrupt_frac_t_0p2_0p4=1.0000 loss_all=4.3457 init_gold_top10=0.0000 init_gold_top100=0.1250 +step=470 epoch=235/250 epoch_step=2/2 micro_steps=470 elapsed=4.5s lr=2.000000e-03 loss=3.0813 loss_recon=3.0813 loss_meanflow=0.0000 mean_model_t=0.1817 mean_corrupt_t=0.1817 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.3000 corrupt_frac=1.0000 acc_corrupt=0.3000 loss_corrupt=3.0813 wrong_frac=0.8625 init_acc_corrupt=0.0750 acc_corrupt_t_0p0_0p2=0.1964 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.9401 out_g_norm=13.3409 acc_corrupt_t_0p2_0p4=0.5000 corrupt_frac_t_0p2_0p4=1.0000 acc_corrupt_t_0p4_0p6=0.6250 corrupt_frac_t_0p4_0p6=1.0000 loss_all=4.3584 init_gold_top10=0.0000 init_gold_top100=0.0000 +step=480 epoch=240/250 epoch_step=2/2 micro_steps=480 elapsed=4.2s lr=2.000000e-03 loss=2.3354 loss_recon=2.3354 loss_meanflow=0.0000 mean_model_t=0.2314 mean_corrupt_t=0.2314 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.4375 corrupt_frac=1.0000 acc_corrupt=0.4375 loss_corrupt=2.3354 wrong_frac=0.6750 init_acc_corrupt=0.1625 acc_corrupt_t_0p0_0p2=0.2250 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.9456 out_g_norm=12.1579 acc_corrupt_t_0p2_0p4=0.6250 corrupt_frac_t_0p2_0p4=1.0000 acc_corrupt_t_0p4_0p6=0.7500 corrupt_frac_t_0p4_0p6=1.0000 loss_all=1.3855 init_gold_top10=0.3750 init_gold_top100=0.5000 +step=490 epoch=245/250 epoch_step=2/2 micro_steps=490 elapsed=5.1s lr=2.000000e-03 loss=2.5818 loss_recon=2.5818 loss_meanflow=0.0000 mean_model_t=0.2122 mean_corrupt_t=0.2122 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.3875 corrupt_frac=1.0000 acc_corrupt=0.3875 loss_corrupt=2.5818 wrong_frac=0.7250 init_acc_corrupt=0.1375 acc_corrupt_t_0p0_0p2=0.3125 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.9460 out_g_norm=11.7760 acc_corrupt_t_0p2_0p4=0.3500 corrupt_frac_t_0p2_0p4=1.0000 acc_corrupt_t_0p4_0p6=0.8750 corrupt_frac_t_0p4_0p6=1.0000 loss_all=2.2825 init_gold_top10=0.2500 init_gold_top100=0.2500 +step=500 epoch=250/250 epoch_step=2/2 micro_steps=500 elapsed=4.5s lr=2.000000e-03 loss=3.2203 loss_recon=3.2203 loss_meanflow=0.0000 mean_model_t=0.1335 mean_corrupt_t=0.1335 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2125 corrupt_frac=1.0000 acc_corrupt=0.2125 loss_corrupt=3.2203 wrong_frac=0.8875 init_acc_corrupt=0.0500 acc_corrupt_t_0p2_0p4=0.2500 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=2.9466 out_g_norm=11.3624 acc_corrupt_t_0p0_0p2=0.2031 corrupt_frac_t_0p0_0p2=1.0000 loss_all=2.3523 init_gold_top10=0.2500 init_gold_top100=0.2500 +[allcorrupt] done train8_n8_allcorrupt_hard_ce_20260517_train8ctx8_allcorrupt Sun May 17 00:26:13 UTC 2026 +[allcorrupt] start train8_n8_allcorrupt_linear_soft_kl_20260517_train8ctx8_allcorrupt Sun May 17 00:26:13 UTC 2026 +[launch] gpt2 cached OWT soft-endpoint m/n pilot +[launch] run_name=train8_n8_allcorrupt_linear_soft_kl_20260517_train8ctx8_allcorrupt +[launch] save_dir=runs/train8_n8_allcorrupt_linear_soft_kl_20260517_train8ctx8_allcorrupt +[launch] n=8 m=0 clean_state_mode=onehot +[launch] mask_mixture lowk=0 all=1 +[launch] target_loss=linear_soft_kl conf=0.0->1.0 power=1.0 +[launch] cache=/e2e-data/evad-tech-vla/wanghan58/data/small_benchmarks/langflow_2604_11748/openwebtext_lta_cached_chunks/gpt2_len8_train8_overfit +NCCL version 2.25.1+cuda12.8 +{ + "device": "cuda:0", + "rank": 0, + "world_size": 4, + "samples": "owt_cached_chunks:8", + "vocab_size": 50257, + "tokenizer_vocab_size": 50257, + "save_dir": "runs/train8_n8_allcorrupt_linear_soft_kl_20260517_train8ctx8_allcorrupt", + "batch_size": 1, + "grad_accum": 1, + "effective_batch_size": 4, + "global_batch_size": 4, + "lr_schedule": "constant_warmup", + "optimizer": "muon", + "epochs": 0.0, + "steps_per_epoch": 2, + "total_steps": 500, + "warmup_steps": 10, + "warmup_epochs": -1.0, + "min_lr": 0.0, + "weight_decay": 0.1, + "output_weight_decay": -1.0, + "adamw_param_groups": "nanogpt", + "adam_beta1": 0.9, + "adam_beta2": 0.95, + "adam_eps": 1e-08, + "muon_impl": "legacy", + "muon_momentum": 0.95, + "muon_ns_steps": 5, + "muon_update_scale": 1.0, + "muon_nesterov": false, + "muon_width_scale": false, + "muon_grouping": "legacy_dim_ge_2", + "muon_param_count": 169453056, + "muon_adam_param_count": 122368, + "muon_param_names": [ + "vocab_embed.embedding", + "sigma_map.net.0.weight", + "sigma_map.net.2.weight", + "blocks.0.attn_qkv.weight", + "blocks.0.attn_out.weight", + 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acc_corrupt=0.1750 loss_corrupt=3.1158 wrong_frac=0.7500 init_acc_corrupt=0.1625 acc_corrupt_t_0p0_0p2=0.0938 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=0.0172 out_g_norm=1.3628 acc_corrupt_t_0p6_0p8=0.1250 corrupt_frac_t_0p6_0p8=1.0000 acc_corrupt_t_0p2_0p4=0.1875 corrupt_frac_t_0p2_0p4=1.0000 acc_corrupt_t_0p4_0p6=0.5000 corrupt_frac_t_0p4_0p6=1.0000 loss_all=10.7812 init_gold_top10=0.3750 init_gold_top100=0.3750 +step=20 epoch=10/250 epoch_step=2/2 micro_steps=20 elapsed=7.0s lr=2.000000e-03 loss=2.0841 loss_recon=2.0841 loss_meanflow=0.0000 mean_model_t=0.2379 mean_corrupt_t=0.2379 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2379 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.1750 corrupt_frac=1.0000 acc_corrupt=0.1750 loss_corrupt=3.0768 wrong_frac=0.7250 init_acc_corrupt=0.1625 acc_corrupt_t_0p0_0p2=0.1250 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=0.1058 out_g_norm=1.5830 acc_corrupt_t_0p2_0p4=0.1875 corrupt_frac_t_0p2_0p4=1.0000 acc_corrupt_t_0p6_0p8=0.5000 corrupt_frac_t_0p6_0p8=1.0000 acc_corrupt_t_0p4_0p6=0.1250 corrupt_frac_t_0p4_0p6=1.0000 loss_all=10.7266 init_gold_top10=0.1250 init_gold_top100=0.1250 +step=30 epoch=15/250 epoch_step=2/2 micro_steps=30 elapsed=6.7s lr=2.000000e-03 loss=2.1873 loss_recon=2.1873 loss_meanflow=0.0000 mean_model_t=0.2564 mean_corrupt_t=0.2564 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2564 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.1625 corrupt_frac=1.0000 acc_corrupt=0.1625 loss_corrupt=3.0765 wrong_frac=0.7750 init_acc_corrupt=0.1750 acc_corrupt_t_0p2_0p4=0.1750 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=0.2196 out_g_norm=1.5462 acc_corrupt_t_0p4_0p6=0.1250 corrupt_frac_t_0p4_0p6=1.0000 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loss_corrupt=2.3653 wrong_frac=0.8375 init_acc_corrupt=0.0375 acc_corrupt_t_0p2_0p4=0.1667 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=0.5888 out_g_norm=1.8518 acc_corrupt_t_0p0_0p2=0.1250 corrupt_frac_t_0p0_0p2=1.0000 loss_all=10.3281 init_gold_top10=0.1250 init_gold_top100=0.1250 +step=80 epoch=40/250 epoch_step=2/2 micro_steps=80 elapsed=4.5s lr=2.000000e-03 loss=1.8378 loss_recon=1.8378 loss_meanflow=0.0000 mean_model_t=0.2455 mean_corrupt_t=0.2455 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2455 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2875 corrupt_frac=1.0000 acc_corrupt=0.2875 loss_corrupt=2.5135 wrong_frac=0.7250 init_acc_corrupt=0.2000 acc_corrupt_t_0p4_0p6=0.5417 corrupt_frac_t_0p4_0p6=1.0000 out_w_norm=0.6751 out_g_norm=1.7835 acc_corrupt_t_0p0_0p2=0.1250 corrupt_frac_t_0p0_0p2=1.0000 acc_corrupt_t_0p2_0p4=0.2500 corrupt_frac_t_0p2_0p4=1.0000 loss_all=9.0312 init_gold_top10=0.5000 init_gold_top100=0.5000 +step=90 epoch=45/250 epoch_step=2/2 micro_steps=90 elapsed=4.1s lr=2.000000e-03 loss=1.6532 loss_recon=1.6532 loss_meanflow=0.0000 mean_model_t=0.2205 mean_corrupt_t=0.2205 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2205 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.1500 corrupt_frac=1.0000 acc_corrupt=0.1500 loss_corrupt=2.2576 wrong_frac=0.7750 init_acc_corrupt=0.1375 acc_corrupt_t_0p0_0p2=0.1250 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=0.7642 out_g_norm=2.1013 acc_corrupt_t_0p2_0p4=0.1667 corrupt_frac_t_0p2_0p4=1.0000 acc_corrupt_t_0p4_0p6=0.1875 corrupt_frac_t_0p4_0p6=1.0000 loss_all=9.3867 init_gold_top10=0.3750 init_gold_top100=0.3750 +step=100 epoch=50/250 epoch_step=2/2 micro_steps=100 elapsed=5.3s lr=2.000000e-03 loss=1.2407 loss_recon=1.2407 loss_meanflow=0.0000 mean_model_t=0.1800 mean_corrupt_t=0.1800 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1800 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2125 corrupt_frac=1.0000 acc_corrupt=0.2125 loss_corrupt=2.1568 wrong_frac=0.8375 init_acc_corrupt=0.0875 acc_corrupt_t_0p0_0p2=0.1458 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=0.8532 out_g_norm=1.9523 acc_corrupt_t_0p2_0p4=0.3125 corrupt_frac_t_0p2_0p4=1.0000 loss_all=10.0547 init_gold_top10=0.1250 init_gold_top100=0.2500 +step=110 epoch=55/250 epoch_step=2/2 micro_steps=110 elapsed=4.4s lr=2.000000e-03 loss=1.5690 loss_recon=1.5690 loss_meanflow=0.0000 mean_model_t=0.2276 mean_corrupt_t=0.2276 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2276 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2375 corrupt_frac=1.0000 acc_corrupt=0.2375 loss_corrupt=2.4548 wrong_frac=0.8375 init_acc_corrupt=0.1375 acc_corrupt_t_0p0_0p2=0.1458 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=0.9430 out_g_norm=2.0350 acc_corrupt_t_0p4_0p6=0.3750 corrupt_frac_t_0p4_0p6=1.0000 acc_corrupt_t_0p2_0p4=0.3750 corrupt_frac_t_0p2_0p4=1.0000 loss_all=9.4180 init_gold_top10=0.0000 init_gold_top100=0.1250 +step=120 epoch=60/250 epoch_step=2/2 micro_steps=120 elapsed=4.1s lr=2.000000e-03 loss=1.6581 loss_recon=1.6581 loss_meanflow=0.0000 mean_model_t=0.2553 mean_corrupt_t=0.2553 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2553 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.3250 corrupt_frac=1.0000 acc_corrupt=0.3250 loss_corrupt=2.4954 wrong_frac=0.6750 init_acc_corrupt=0.2250 acc_corrupt_t_0p2_0p4=0.3929 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=1.0350 out_g_norm=2.3059 acc_corrupt_t_0p0_0p2=0.1667 corrupt_frac_t_0p0_0p2=1.0000 loss_all=9.1484 init_gold_top10=0.5000 init_gold_top100=0.5000 +step=130 epoch=65/250 epoch_step=2/2 micro_steps=130 elapsed=4.6s lr=2.000000e-03 loss=1.4130 loss_recon=1.4130 loss_meanflow=0.0000 mean_model_t=0.2286 mean_corrupt_t=0.2286 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2286 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2375 corrupt_frac=1.0000 acc_corrupt=0.2375 loss_corrupt=2.1573 wrong_frac=0.7875 init_acc_corrupt=0.1375 acc_corrupt_t_0p0_0p2=0.1667 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=1.1300 out_g_norm=2.4490 acc_corrupt_t_0p2_0p4=0.2679 corrupt_frac_t_0p2_0p4=1.0000 loss_all=8.8047 init_gold_top10=0.1250 init_gold_top100=0.1250 +step=140 epoch=70/250 epoch_step=2/2 micro_steps=140 elapsed=4.4s lr=2.000000e-03 loss=1.0509 loss_recon=1.0509 loss_meanflow=0.0000 mean_model_t=0.1682 mean_corrupt_t=0.1682 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1682 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.1875 corrupt_frac=1.0000 acc_corrupt=0.1875 loss_corrupt=1.9215 wrong_frac=0.8375 init_acc_corrupt=0.0500 acc_corrupt_t_0p0_0p2=0.1250 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=1.2307 out_g_norm=2.0366 acc_corrupt_t_0p2_0p4=0.3333 corrupt_frac_t_0p2_0p4=1.0000 loss_all=9.1719 init_gold_top10=0.1250 init_gold_top100=0.1250 +step=150 epoch=75/250 epoch_step=2/2 micro_steps=150 elapsed=4.1s lr=2.000000e-03 loss=1.3402 loss_recon=1.3402 loss_meanflow=0.0000 mean_model_t=0.2326 mean_corrupt_t=0.2326 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2326 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2625 corrupt_frac=1.0000 acc_corrupt=0.2625 loss_corrupt=1.9238 wrong_frac=0.7625 init_acc_corrupt=0.1375 acc_corrupt_t_0p0_0p2=0.1750 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=1.3304 out_g_norm=1.9295 acc_corrupt_t_0p2_0p4=0.3125 corrupt_frac_t_0p2_0p4=1.0000 acc_corrupt_t_0p4_0p6=0.5000 corrupt_frac_t_0p4_0p6=1.0000 loss_all=8.0742 init_gold_top10=0.2500 init_gold_top100=0.2500 +step=160 epoch=80/250 epoch_step=2/2 micro_steps=160 elapsed=4.5s lr=2.000000e-03 loss=0.8337 loss_recon=0.8337 loss_meanflow=0.0000 mean_model_t=0.1882 mean_corrupt_t=0.1882 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1882 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.3375 corrupt_frac=1.0000 acc_corrupt=0.3375 loss_corrupt=1.6679 wrong_frac=0.7500 init_acc_corrupt=0.1750 acc_corrupt_t_0p2_0p4=0.5000 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=1.4319 out_g_norm=1.8421 acc_corrupt_t_0p0_0p2=0.1750 corrupt_frac_t_0p0_0p2=1.0000 loss_all=8.7930 init_gold_top10=0.0000 init_gold_top100=0.0000 +step=170 epoch=85/250 epoch_step=2/2 micro_steps=170 elapsed=5.3s lr=2.000000e-03 loss=0.9756 loss_recon=0.9756 loss_meanflow=0.0000 mean_model_t=0.2017 mean_corrupt_t=0.2017 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2017 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2250 corrupt_frac=1.0000 acc_corrupt=0.2250 loss_corrupt=1.3851 wrong_frac=0.7250 init_acc_corrupt=0.1000 acc_corrupt_t_0p2_0p4=0.3750 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=1.5338 out_g_norm=1.6552 acc_corrupt_t_0p0_0p2=0.0750 corrupt_frac_t_0p0_0p2=1.0000 loss_all=4.8604 init_gold_top10=0.5000 init_gold_top100=0.6250 +step=180 epoch=90/250 epoch_step=2/2 micro_steps=180 elapsed=4.1s lr=2.000000e-03 loss=1.0499 loss_recon=1.0499 loss_meanflow=0.0000 mean_model_t=0.2081 mean_corrupt_t=0.2081 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2081 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2375 corrupt_frac=1.0000 acc_corrupt=0.2375 loss_corrupt=1.7474 wrong_frac=0.7875 init_acc_corrupt=0.1250 acc_corrupt_t_0p0_0p2=0.1667 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=1.6309 out_g_norm=1.7936 acc_corrupt_t_0p2_0p4=0.3438 corrupt_frac_t_0p2_0p4=1.0000 loss_all=8.0332 init_gold_top10=0.3750 init_gold_top100=0.5000 +step=190 epoch=95/250 epoch_step=2/2 micro_steps=190 elapsed=4.5s lr=2.000000e-03 loss=0.9665 loss_recon=0.9665 loss_meanflow=0.0000 mean_model_t=0.1942 mean_corrupt_t=0.1942 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1942 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2250 corrupt_frac=1.0000 acc_corrupt=0.2250 loss_corrupt=1.7928 wrong_frac=0.8625 init_acc_corrupt=0.0625 acc_corrupt_t_0p0_0p2=0.1458 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=1.7201 out_g_norm=2.0560 acc_corrupt_t_0p2_0p4=0.3438 corrupt_frac_t_0p2_0p4=1.0000 loss_all=8.8516 init_gold_top10=0.1250 init_gold_top100=0.1250 +step=200 epoch=100/250 epoch_step=2/2 micro_steps=200 elapsed=4.4s lr=2.000000e-03 loss=1.0310 loss_recon=1.0310 loss_meanflow=0.0000 mean_model_t=0.1970 mean_corrupt_t=0.1970 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1970 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.1625 corrupt_frac=1.0000 acc_corrupt=0.1625 loss_corrupt=1.8039 wrong_frac=0.8250 init_acc_corrupt=0.0625 acc_corrupt_t_0p0_0p2=0.1250 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=1.8020 out_g_norm=2.0184 acc_corrupt_t_0p2_0p4=0.2500 corrupt_frac_t_0p2_0p4=1.0000 loss_all=7.9961 init_gold_top10=0.0000 init_gold_top100=0.3750 +step=210 epoch=105/250 epoch_step=2/2 micro_steps=210 elapsed=4.1s lr=2.000000e-03 loss=0.8071 loss_recon=0.8071 loss_meanflow=0.0000 mean_model_t=0.1880 mean_corrupt_t=0.1880 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1880 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2500 corrupt_frac=1.0000 acc_corrupt=0.2500 loss_corrupt=1.2439 wrong_frac=0.8500 init_acc_corrupt=0.0875 acc_corrupt_t_0p2_0p4=0.3125 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=1.8702 out_g_norm=2.0898 acc_corrupt_t_0p0_0p2=0.2083 corrupt_frac_t_0p0_0p2=1.0000 loss_all=4.9170 init_gold_top10=0.2500 init_gold_top100=0.3750 +step=220 epoch=110/250 epoch_step=2/2 micro_steps=220 elapsed=4.5s lr=2.000000e-03 loss=0.8528 loss_recon=0.8528 loss_meanflow=0.0000 mean_model_t=0.1718 mean_corrupt_t=0.1718 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1718 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2000 corrupt_frac=1.0000 acc_corrupt=0.2000 loss_corrupt=1.6071 wrong_frac=0.8500 init_acc_corrupt=0.0375 acc_corrupt_t_0p0_0p2=0.2083 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=1.9181 out_g_norm=1.7059 acc_corrupt_t_0p2_0p4=0.1875 corrupt_frac_t_0p2_0p4=1.0000 loss_all=8.7812 init_gold_top10=0.1250 init_gold_top100=0.1250 +step=230 epoch=115/250 epoch_step=2/2 micro_steps=230 elapsed=4.4s lr=2.000000e-03 loss=0.8167 loss_recon=0.8167 loss_meanflow=0.0000 mean_model_t=0.3049 mean_corrupt_t=0.3049 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.3049 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.4375 corrupt_frac=1.0000 acc_corrupt=0.4375 loss_corrupt=1.4772 wrong_frac=0.6500 init_acc_corrupt=0.2875 acc_corrupt_t_0p4_0p6=0.7917 corrupt_frac_t_0p4_0p6=1.0000 out_w_norm=1.9604 out_g_norm=2.2768 acc_corrupt_t_0p2_0p4=0.4583 corrupt_frac_t_0p2_0p4=1.0000 acc_corrupt_t_0p0_0p2=0.1562 corrupt_frac_t_0p0_0p2=1.0000 loss_all=7.4824 init_gold_top10=0.2500 init_gold_top100=0.2500 +step=240 epoch=120/250 epoch_step=2/2 micro_steps=240 elapsed=4.1s lr=2.000000e-03 loss=0.8203 loss_recon=0.8203 loss_meanflow=0.0000 mean_model_t=0.2693 mean_corrupt_t=0.2693 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2693 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.3375 corrupt_frac=1.0000 acc_corrupt=0.3375 loss_corrupt=1.1419 wrong_frac=0.7125 init_acc_corrupt=0.2000 acc_corrupt_t_0p0_0p2=0.1875 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.0042 out_g_norm=2.2456 acc_corrupt_t_0p4_0p6=0.6250 corrupt_frac_t_0p4_0p6=1.0000 acc_corrupt_t_0p2_0p4=0.3438 corrupt_frac_t_0p2_0p4=1.0000 loss_all=4.4014 init_gold_top10=0.2500 init_gold_top100=0.2500 +step=250 epoch=125/250 epoch_step=2/2 micro_steps=250 elapsed=4.5s lr=2.000000e-03 loss=0.5640 loss_recon=0.5640 loss_meanflow=0.0000 mean_model_t=0.1964 mean_corrupt_t=0.1964 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1964 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2875 corrupt_frac=1.0000 acc_corrupt=0.2875 loss_corrupt=0.8368 wrong_frac=0.7500 init_acc_corrupt=0.1250 acc_corrupt_t_0p0_0p2=0.1719 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.0422 out_g_norm=1.8608 acc_corrupt_t_0p6_0p8=0.8750 corrupt_frac_t_0p6_0p8=1.0000 acc_corrupt_t_0p2_0p4=0.6250 corrupt_frac_t_0p2_0p4=1.0000 loss_all=3.4316 init_gold_top10=0.5000 init_gold_top100=0.5000 +step=260 epoch=130/250 epoch_step=2/2 micro_steps=260 elapsed=4.4s lr=2.000000e-03 loss=0.6435 loss_recon=0.6435 loss_meanflow=0.0000 mean_model_t=0.1517 mean_corrupt_t=0.1517 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1517 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.1500 corrupt_frac=1.0000 acc_corrupt=0.1500 loss_corrupt=1.1853 wrong_frac=0.8750 init_acc_corrupt=0.0250 acc_corrupt_t_0p0_0p2=0.1429 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.0589 out_g_norm=1.9505 acc_corrupt_t_0p2_0p4=0.1667 corrupt_frac_t_0p2_0p4=1.0000 loss_all=5.7266 init_gold_top10=0.1250 init_gold_top100=0.5000 +step=270 epoch=135/250 epoch_step=2/2 micro_steps=270 elapsed=4.0s lr=2.000000e-03 loss=0.6368 loss_recon=0.6368 loss_meanflow=0.0000 mean_model_t=0.1781 mean_corrupt_t=0.1781 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1781 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2500 corrupt_frac=1.0000 acc_corrupt=0.2500 loss_corrupt=1.0227 wrong_frac=0.7750 init_acc_corrupt=0.0875 acc_corrupt_t_0p0_0p2=0.1786 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.0719 out_g_norm=2.0863 acc_corrupt_t_0p2_0p4=0.4167 corrupt_frac_t_0p2_0p4=1.0000 loss_all=4.4688 init_gold_top10=0.3750 init_gold_top100=0.3750 +step=280 epoch=140/250 epoch_step=2/2 micro_steps=280 elapsed=4.5s lr=2.000000e-03 loss=0.8962 loss_recon=0.8962 loss_meanflow=0.0000 mean_model_t=0.2199 mean_corrupt_t=0.2199 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2199 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.1750 corrupt_frac=1.0000 acc_corrupt=0.1750 loss_corrupt=1.4513 wrong_frac=0.8000 init_acc_corrupt=0.0625 acc_corrupt_t_0p4_0p6=0.0000 corrupt_frac_t_0p4_0p6=1.0000 out_w_norm=2.0855 out_g_norm=2.2083 acc_corrupt_t_0p0_0p2=0.1000 corrupt_frac_t_0p0_0p2=1.0000 acc_corrupt_t_0p2_0p4=0.3125 corrupt_frac_t_0p2_0p4=1.0000 loss_all=6.1973 init_gold_top10=0.1250 init_gold_top100=0.2500 +step=290 epoch=145/250 epoch_step=2/2 micro_steps=290 elapsed=4.4s lr=2.000000e-03 loss=0.6349 loss_recon=0.6349 loss_meanflow=0.0000 mean_model_t=0.2100 mean_corrupt_t=0.2100 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2100 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.3000 corrupt_frac=1.0000 acc_corrupt=0.3000 loss_corrupt=1.0752 wrong_frac=0.7625 init_acc_corrupt=0.1625 acc_corrupt_t_0p2_0p4=0.3750 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=2.1006 out_g_norm=2.0542 acc_corrupt_t_0p0_0p2=0.2250 corrupt_frac_t_0p0_0p2=1.0000 loss_all=5.5759 init_gold_top10=0.2500 init_gold_top100=0.2500 +step=300 epoch=150/250 epoch_step=2/2 micro_steps=300 elapsed=4.1s lr=2.000000e-03 loss=0.6497 loss_recon=0.6497 loss_meanflow=0.0000 mean_model_t=0.2037 mean_corrupt_t=0.2037 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2037 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2625 corrupt_frac=1.0000 acc_corrupt=0.2625 loss_corrupt=1.3369 wrong_frac=0.7250 init_acc_corrupt=0.0875 acc_corrupt_t_0p0_0p2=0.1750 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.1204 out_g_norm=2.0059 acc_corrupt_t_0p2_0p4=0.3500 corrupt_frac_t_0p2_0p4=1.0000 loss_all=7.2656 init_gold_top10=0.1250 init_gold_top100=0.5000 +step=310 epoch=155/250 epoch_step=2/2 micro_steps=310 elapsed=4.5s lr=2.000000e-03 loss=0.5915 loss_recon=0.5915 loss_meanflow=0.0000 mean_model_t=0.1660 mean_corrupt_t=0.1660 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1660 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2125 corrupt_frac=1.0000 acc_corrupt=0.2125 loss_corrupt=1.2310 wrong_frac=0.8500 init_acc_corrupt=0.0875 acc_corrupt_t_0p0_0p2=0.1607 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.1404 out_g_norm=2.0428 acc_corrupt_t_0p4_0p6=0.8750 corrupt_frac_t_0p4_0p6=1.0000 acc_corrupt_t_0p2_0p4=0.0625 corrupt_frac_t_0p2_0p4=1.0000 loss_all=8.0625 init_gold_top10=0.0000 init_gold_top100=0.0000 +step=320 epoch=160/250 epoch_step=2/2 micro_steps=320 elapsed=4.4s lr=2.000000e-03 loss=0.7207 loss_recon=0.7207 loss_meanflow=0.0000 mean_model_t=0.2203 mean_corrupt_t=0.2203 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2203 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2250 corrupt_frac=1.0000 acc_corrupt=0.2250 loss_corrupt=1.3024 wrong_frac=0.8250 init_acc_corrupt=0.1125 acc_corrupt_t_0p0_0p2=0.1458 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.1608 out_g_norm=2.2383 acc_corrupt_t_0p4_0p6=0.2500 corrupt_frac_t_0p4_0p6=1.0000 acc_corrupt_t_0p2_0p4=0.3750 corrupt_frac_t_0p2_0p4=1.0000 loss_all=6.6562 init_gold_top10=0.0000 init_gold_top100=0.3750 +step=330 epoch=165/250 epoch_step=2/2 micro_steps=330 elapsed=4.0s lr=2.000000e-03 loss=0.6010 loss_recon=0.6010 loss_meanflow=0.0000 mean_model_t=0.2293 mean_corrupt_t=0.2293 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2293 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.3000 corrupt_frac=1.0000 acc_corrupt=0.3000 loss_corrupt=0.7656 wrong_frac=0.6750 init_acc_corrupt=0.2125 acc_corrupt_t_0p0_0p2=0.0938 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.1767 out_g_norm=2.3064 acc_corrupt_t_0p2_0p4=0.4375 corrupt_frac_t_0p2_0p4=1.0000 loss_all=2.0283 init_gold_top10=0.6250 init_gold_top100=0.6250 +step=340 epoch=170/250 epoch_step=2/2 micro_steps=340 elapsed=4.4s lr=2.000000e-03 loss=0.5942 loss_recon=0.5942 loss_meanflow=0.0000 mean_model_t=0.2682 mean_corrupt_t=0.2682 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2682 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.3375 corrupt_frac=1.0000 acc_corrupt=0.3375 loss_corrupt=1.0234 wrong_frac=0.6875 init_acc_corrupt=0.2375 acc_corrupt_t_0p0_0p2=0.1250 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.1904 out_g_norm=2.4344 acc_corrupt_t_0p2_0p4=0.4167 corrupt_frac_t_0p2_0p4=1.0000 acc_corrupt_t_0p4_0p6=0.5000 corrupt_frac_t_0p4_0p6=1.0000 acc_corrupt_t_0p6_0p8=1.0000 corrupt_frac_t_0p6_0p8=1.0000 loss_all=4.6797 init_gold_top10=0.1250 init_gold_top100=0.1250 +step=350 epoch=175/250 epoch_step=2/2 micro_steps=350 elapsed=4.4s lr=2.000000e-03 loss=0.6995 loss_recon=0.6995 loss_meanflow=0.0000 mean_model_t=0.1929 mean_corrupt_t=0.1929 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1929 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.1750 corrupt_frac=1.0000 acc_corrupt=0.1750 loss_corrupt=1.1853 wrong_frac=0.8375 init_acc_corrupt=0.0750 acc_corrupt_t_0p2_0p4=0.2500 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=2.1949 out_g_norm=2.2119 acc_corrupt_t_0p0_0p2=0.1000 corrupt_frac_t_0p0_0p2=1.0000 loss_all=5.4766 init_gold_top10=0.1250 init_gold_top100=0.2500 +step=360 epoch=180/250 epoch_step=2/2 micro_steps=360 elapsed=4.1s lr=2.000000e-03 loss=0.6252 loss_recon=0.6252 loss_meanflow=0.0000 mean_model_t=0.1923 mean_corrupt_t=0.1923 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1923 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2000 corrupt_frac=1.0000 acc_corrupt=0.2000 loss_corrupt=0.9015 wrong_frac=0.7875 init_acc_corrupt=0.1000 acc_corrupt_t_0p0_0p2=0.1250 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.1944 out_g_norm=1.9623 acc_corrupt_t_0p4_0p6=0.5000 corrupt_frac_t_0p4_0p6=1.0000 loss_all=4.1462 init_gold_top10=0.2500 init_gold_top100=0.2500 +step=370 epoch=185/250 epoch_step=2/2 micro_steps=370 elapsed=4.5s lr=2.000000e-03 loss=0.4709 loss_recon=0.4709 loss_meanflow=0.0000 mean_model_t=0.1757 mean_corrupt_t=0.1757 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1757 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2750 corrupt_frac=1.0000 acc_corrupt=0.2750 loss_corrupt=0.8083 wrong_frac=0.8250 init_acc_corrupt=0.0750 acc_corrupt_t_0p2_0p4=0.3750 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=2.1991 out_g_norm=2.0457 acc_corrupt_t_0p0_0p2=0.2500 corrupt_frac_t_0p0_0p2=1.0000 loss_all=3.7109 init_gold_top10=0.2500 init_gold_top100=0.5000 +step=380 epoch=190/250 epoch_step=2/2 micro_steps=380 elapsed=4.4s lr=2.000000e-03 loss=0.4684 loss_recon=0.4684 loss_meanflow=0.0000 mean_model_t=0.1794 mean_corrupt_t=0.1794 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1794 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2375 corrupt_frac=1.0000 acc_corrupt=0.2375 loss_corrupt=0.7979 wrong_frac=0.8375 init_acc_corrupt=0.1000 acc_corrupt_t_0p0_0p2=0.1250 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.2059 out_g_norm=2.0713 acc_corrupt_t_0p4_0p6=0.5000 corrupt_frac_t_0p4_0p6=1.0000 acc_corrupt_t_0p2_0p4=0.5000 corrupt_frac_t_0p2_0p4=1.0000 loss_all=4.0498 init_gold_top10=0.2500 init_gold_top100=0.2500 +step=390 epoch=195/250 epoch_step=2/2 micro_steps=390 elapsed=4.1s lr=2.000000e-03 loss=0.6511 loss_recon=0.6511 loss_meanflow=0.0000 mean_model_t=0.1873 mean_corrupt_t=0.1873 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1873 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2500 corrupt_frac=1.0000 acc_corrupt=0.2500 loss_corrupt=1.1307 wrong_frac=0.8125 init_acc_corrupt=0.0750 acc_corrupt_t_0p2_0p4=0.3125 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=2.2128 out_g_norm=2.4076 acc_corrupt_t_0p0_0p2=0.2083 corrupt_frac_t_0p0_0p2=1.0000 loss_all=5.4785 init_gold_top10=0.1250 init_gold_top100=0.5000 +step=400 epoch=200/250 epoch_step=2/2 micro_steps=400 elapsed=4.5s lr=2.000000e-03 loss=0.6558 loss_recon=0.6558 loss_meanflow=0.0000 mean_model_t=0.3288 mean_corrupt_t=0.3288 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.3288 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.3125 corrupt_frac=1.0000 acc_corrupt=0.3125 loss_corrupt=0.7121 wrong_frac=0.7250 init_acc_corrupt=0.2250 acc_corrupt_t_0p0_0p2=0.0833 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.2082 out_g_norm=2.2056 acc_corrupt_t_0p2_0p4=0.2188 corrupt_frac_t_0p2_0p4=1.0000 acc_corrupt_t_0p6_0p8=0.6250 corrupt_frac_t_0p6_0p8=1.0000 acc_corrupt_t_0p4_0p6=0.6875 corrupt_frac_t_0p4_0p6=1.0000 loss_all=1.0971 init_gold_top10=0.7500 init_gold_top100=0.7500 +step=410 epoch=205/250 epoch_step=2/2 micro_steps=410 elapsed=5.9s lr=2.000000e-03 loss=0.6098 loss_recon=0.6098 loss_meanflow=0.0000 mean_model_t=0.2749 mean_corrupt_t=0.2749 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2749 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.4000 corrupt_frac=1.0000 acc_corrupt=0.4000 loss_corrupt=0.8961 wrong_frac=0.6750 init_acc_corrupt=0.2375 acc_corrupt_t_0p0_0p2=0.1667 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.2129 out_g_norm=2.1538 acc_corrupt_t_0p2_0p4=0.4500 corrupt_frac_t_0p2_0p4=1.0000 acc_corrupt_t_0p4_0p6=0.6250 corrupt_frac_t_0p4_0p6=1.0000 loss_all=3.2842 init_gold_top10=0.3750 init_gold_top100=0.5000 +step=420 epoch=210/250 epoch_step=2/2 micro_steps=420 elapsed=5.5s lr=2.000000e-03 loss=0.6001 loss_recon=0.6001 loss_meanflow=0.0000 mean_model_t=0.2123 mean_corrupt_t=0.2123 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2123 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2625 corrupt_frac=1.0000 acc_corrupt=0.2625 loss_corrupt=1.0973 wrong_frac=0.7750 init_acc_corrupt=0.1000 acc_corrupt_t_0p2_0p4=0.3125 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=2.2078 out_g_norm=2.0003 acc_corrupt_t_0p0_0p2=0.1875 corrupt_frac_t_0p0_0p2=1.0000 loss_all=5.4766 init_gold_top10=0.1250 init_gold_top100=0.3750 +step=430 epoch=215/250 epoch_step=2/2 micro_steps=430 elapsed=6.0s lr=2.000000e-03 loss=0.5659 loss_recon=0.5659 loss_meanflow=0.0000 mean_model_t=0.1772 mean_corrupt_t=0.1772 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1772 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2750 corrupt_frac=1.0000 acc_corrupt=0.2750 loss_corrupt=0.9336 wrong_frac=0.8250 init_acc_corrupt=0.0875 acc_corrupt_t_0p0_0p2=0.2250 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.1960 out_g_norm=1.8503 acc_corrupt_t_0p2_0p4=0.3250 corrupt_frac_t_0p2_0p4=1.0000 loss_all=4.2871 init_gold_top10=0.1250 init_gold_top100=0.3750 +step=440 epoch=220/250 epoch_step=2/2 micro_steps=440 elapsed=5.4s lr=2.000000e-03 loss=0.5390 loss_recon=0.5390 loss_meanflow=0.0000 mean_model_t=0.2333 mean_corrupt_t=0.2333 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2333 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.3625 corrupt_frac=1.0000 acc_corrupt=0.3625 loss_corrupt=0.9075 wrong_frac=0.6875 init_acc_corrupt=0.1875 acc_corrupt_t_0p0_0p2=0.1562 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.1950 out_g_norm=1.9730 acc_corrupt_t_0p2_0p4=0.5000 corrupt_frac_t_0p2_0p4=1.0000 loss_all=4.2441 init_gold_top10=0.3750 init_gold_top100=0.6250 +step=450 epoch=225/250 epoch_step=2/2 micro_steps=450 elapsed=4.3s lr=2.000000e-03 loss=0.5868 loss_recon=0.5868 loss_meanflow=0.0000 mean_model_t=0.1578 mean_corrupt_t=0.1578 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1578 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.1500 corrupt_frac=1.0000 acc_corrupt=0.1500 loss_corrupt=1.2287 wrong_frac=0.8375 init_acc_corrupt=0.0500 acc_corrupt_t_0p0_0p2=0.1250 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.1938 out_g_norm=2.1070 acc_corrupt_t_0p2_0p4=0.2083 corrupt_frac_t_0p2_0p4=1.0000 loss_all=6.6562 init_gold_top10=0.0000 init_gold_top100=0.0000 +step=460 epoch=230/250 epoch_step=2/2 micro_steps=460 elapsed=5.0s lr=2.000000e-03 loss=0.4614 loss_recon=0.4614 loss_meanflow=0.0000 mean_model_t=0.1884 mean_corrupt_t=0.1884 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1884 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.1875 corrupt_frac=1.0000 acc_corrupt=0.1875 loss_corrupt=0.9782 wrong_frac=0.8250 init_acc_corrupt=0.1000 acc_corrupt_t_0p0_0p2=0.0179 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.1963 out_g_norm=2.1456 acc_corrupt_t_0p4_0p6=0.5625 corrupt_frac_t_0p4_0p6=1.0000 acc_corrupt_t_0p2_0p4=0.6250 corrupt_frac_t_0p2_0p4=1.0000 loss_all=5.5859 init_gold_top10=0.0000 init_gold_top100=0.1250 +step=470 epoch=235/250 epoch_step=2/2 micro_steps=470 elapsed=5.8s lr=2.000000e-03 loss=0.5090 loss_recon=0.5090 loss_meanflow=0.0000 mean_model_t=0.1817 mean_corrupt_t=0.1817 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1817 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2375 corrupt_frac=1.0000 acc_corrupt=0.2375 loss_corrupt=1.1135 wrong_frac=0.8625 init_acc_corrupt=0.0750 acc_corrupt_t_0p0_0p2=0.1786 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.2051 out_g_norm=2.3825 acc_corrupt_t_0p2_0p4=0.3750 corrupt_frac_t_0p2_0p4=1.0000 acc_corrupt_t_0p4_0p6=0.3750 corrupt_frac_t_0p4_0p6=1.0000 loss_all=6.2793 init_gold_top10=0.0000 init_gold_top100=0.0000 +step=480 epoch=240/250 epoch_step=2/2 micro_steps=480 elapsed=5.6s lr=2.000000e-03 loss=0.5577 loss_recon=0.5577 loss_meanflow=0.0000 mean_model_t=0.2314 mean_corrupt_t=0.2314 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2314 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.3125 corrupt_frac=1.0000 acc_corrupt=0.3125 loss_corrupt=0.8093 wrong_frac=0.6750 init_acc_corrupt=0.1625 acc_corrupt_t_0p0_0p2=0.1000 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.2094 out_g_norm=2.1309 acc_corrupt_t_0p2_0p4=0.4688 corrupt_frac_t_0p2_0p4=1.0000 acc_corrupt_t_0p4_0p6=0.7500 corrupt_frac_t_0p4_0p6=1.0000 loss_all=2.8984 init_gold_top10=0.3750 init_gold_top100=0.5000 +step=490 epoch=245/250 epoch_step=2/2 micro_steps=490 elapsed=6.4s lr=2.000000e-03 loss=0.5394 loss_recon=0.5394 loss_meanflow=0.0000 mean_model_t=0.2122 mean_corrupt_t=0.2122 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2122 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2750 corrupt_frac=1.0000 acc_corrupt=0.2750 loss_corrupt=0.8618 wrong_frac=0.7250 init_acc_corrupt=0.1375 acc_corrupt_t_0p0_0p2=0.1875 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.2091 out_g_norm=2.0059 acc_corrupt_t_0p2_0p4=0.2250 corrupt_frac_t_0p2_0p4=1.0000 acc_corrupt_t_0p4_0p6=0.8750 corrupt_frac_t_0p4_0p6=1.0000 loss_all=3.6338 init_gold_top10=0.2500 init_gold_top100=0.2500 +step=500 epoch=250/250 epoch_step=2/2 micro_steps=500 elapsed=4.4s lr=2.000000e-03 loss=0.4201 loss_recon=0.4201 loss_meanflow=0.0000 mean_model_t=0.1335 mean_corrupt_t=0.1335 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1335 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.1875 corrupt_frac=1.0000 acc_corrupt=0.1875 loss_corrupt=0.7494 wrong_frac=0.8875 init_acc_corrupt=0.0500 acc_corrupt_t_0p2_0p4=0.1875 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=2.2072 out_g_norm=1.6554 acc_corrupt_t_0p0_0p2=0.1875 corrupt_frac_t_0p0_0p2=1.0000 loss_all=3.9180 init_gold_top10=0.2500 init_gold_top100=0.2500 +[allcorrupt] done train8_n8_allcorrupt_linear_soft_kl_20260517_train8ctx8_allcorrupt Sun May 17 00:30:22 UTC 2026 diff --git a/LTA_openwebtext_dualt/logs/watch_lta_owt_classic_fullvocab_bert_c1024_len1024_lr2e4_gbs2048_2node8gpu_1m_save10k_t-20260522043432-f7vrv_latest1k_gpu3_b4.nohup.log b/LTA_openwebtext_dualt/logs/watch_lta_owt_classic_fullvocab_bert_c1024_len1024_lr2e4_gbs2048_2node8gpu_1m_save10k_t-20260522043432-f7vrv_latest1k_gpu3_b4.nohup.log new file mode 100644 index 0000000000000000000000000000000000000000..b7df0861a67772ac36e3f3f3cea6c50d0af2c048 --- /dev/null +++ b/LTA_openwebtext_dualt/logs/watch_lta_owt_classic_fullvocab_bert_c1024_len1024_lr2e4_gbs2048_2node8gpu_1m_save10k_t-20260522043432-f7vrv_latest1k_gpu3_b4.nohup.log @@ -0,0 +1,291 @@ +[watch-owt-len1024-lr2e4] run_glob=runs/lta_owt_classic_fullvocab_bert_c1024_len1024_lr2e4_gbs2048_2node8gpu_1m_save10k_* +[watch-owt-len1024-lr2e4] explicit_run_dir=runs/lta_owt_classic_fullvocab_bert_c1024_len1024_lr2e4_gbs2048_2node8gpu_1m_save10k_t-20260522043432-f7vrv +[watch-owt-len1024-lr2e4] out_root=docs/lta_samples/metrics_20260521/owt_classic_fullvocab_len1024_lr2e4_gbs2048_latest_every1k_normal_steps_state_t1p45_c1024_n1024 +[watch-owt-len1024-lr2e4] decode=normal_steps_sweep steps=128 cmax=1024 temp=1.45 final_from=state n=1024 max_len=1024 +[watch-owt-len1024-lr2e4] source=latest.pt snapshot_each=1000 decode_batch=4 score_batch=4 +[watch-owt-len1024-lr2e4] 2026-05-21_22:00:41 snapshot latest step_0004000 -> runs/lta_owt_classic_fullvocab_bert_c1024_len1024_lr2e4_gbs2048_2node8gpu_1m_save10k_t-20260522043432-f7vrv/latest_snapshots_1k/step_0004000.pt +[watch-owt-len1024-lr2e4] 2026-05-21_22:00:44 infer runs/lta_owt_classic_fullvocab_bert_c1024_len1024_lr2e4_gbs2048_2node8gpu_1m_save10k_t-20260522043432-f7vrv/latest_snapshots_1k/step_0004000.pt -> docs/lta_samples/metrics_20260521/owt_classic_fullvocab_len1024_lr2e4_gbs2048_latest_every1k_normal_steps_state_t1p45_c1024_n1024/lta_owt_classic_fullvocab_bert_c1024_len1024_lr2e4_gbs2048_2node8gpu_1m_save10k_t-20260522043432-f7vrv/step_0004000 +[ckpt] runs/lta_owt_classic_fullvocab_bert_c1024_len1024_lr2e4_gbs2048_2node8gpu_1m_save10k_t-20260522043432-f7vrv/latest_snapshots_1k/step_0004000.pt step=4000 +[decode] steps128_c1024_t1p45 generated 4/1024 +[decode] steps128_c1024_t1p45 generated 8/1024 +[decode] steps128_c1024_t1p45 generated 12/1024 +[decode] steps128_c1024_t1p45 generated 16/1024 +[decode] steps128_c1024_t1p45 generated 20/1024 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2026-05-21_22:47:54 snapshot latest step_0006000 -> runs/lta_owt_classic_fullvocab_bert_c1024_len1024_lr2e4_gbs2048_2node8gpu_1m_save10k_t-20260522043432-f7vrv/latest_snapshots_1k/step_0006000.pt +[watch-owt-len1024-lr2e4] 2026-05-21_22:47:57 infer runs/lta_owt_classic_fullvocab_bert_c1024_len1024_lr2e4_gbs2048_2node8gpu_1m_save10k_t-20260522043432-f7vrv/latest_snapshots_1k/step_0006000.pt -> docs/lta_samples/metrics_20260521/owt_classic_fullvocab_len1024_lr2e4_gbs2048_latest_every1k_normal_steps_state_t1p45_c1024_n1024/lta_owt_classic_fullvocab_bert_c1024_len1024_lr2e4_gbs2048_2node8gpu_1m_save10k_t-20260522043432-f7vrv/step_0006000 +[ckpt] runs/lta_owt_classic_fullvocab_bert_c1024_len1024_lr2e4_gbs2048_2node8gpu_1m_save10k_t-20260522043432-f7vrv/latest_snapshots_1k/step_0006000.pt step=6000 +[decode] steps128_c1024_t1p45 generated 4/1024 +[decode] steps128_c1024_t1p45 generated 8/1024 +[decode] steps128_c1024_t1p45 generated 12/1024 +[decode] steps128_c1024_t1p45 generated 16/1024 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a/LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/numpy/matrixlib/__init__.pyi b/LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/numpy/matrixlib/__init__.pyi new file mode 100644 index 0000000000000000000000000000000000000000..b0ca8c9ca03d39efa03bede061f2a4f8ef90523a --- /dev/null +++ b/LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/numpy/matrixlib/__init__.pyi @@ -0,0 +1,15 @@ +from numpy._pytesttester import PytestTester + +from numpy import ( + matrix as matrix, +) + +from numpy.matrixlib.defmatrix import ( + bmat as bmat, + mat as mat, + asmatrix as asmatrix, +) + +__all__: list[str] +__path__: list[str] +test: PytestTester diff --git a/LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/numpy/matrixlib/defmatrix.py b/LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/numpy/matrixlib/defmatrix.py new file mode 100644 index 0000000000000000000000000000000000000000..d029b13fb8b561247fb031e44a14de285a1d9d4a --- /dev/null +++ b/LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/numpy/matrixlib/defmatrix.py @@ -0,0 +1,1114 @@ +__all__ = ['matrix', 'bmat', 'mat', 'asmatrix'] + +import sys +import warnings +import ast + +from .._utils import set_module +import numpy.core.numeric as N +from numpy.core.numeric import concatenate, isscalar +# While not in __all__, matrix_power used to be defined here, so we import +# it for backward compatibility. +from numpy.linalg import matrix_power + + +def _convert_from_string(data): + for char in '[]': + data = data.replace(char, '') + + rows = data.split(';') + newdata = [] + count = 0 + for row in rows: + trow = row.split(',') + newrow = [] + for col in trow: + temp = col.split() + newrow.extend(map(ast.literal_eval, temp)) + if count == 0: + Ncols = len(newrow) + elif len(newrow) != Ncols: + raise ValueError("Rows not the same size.") + count += 1 + newdata.append(newrow) + return newdata + + +@set_module('numpy') +def asmatrix(data, dtype=None): + """ + Interpret the input as a matrix. + + Unlike `matrix`, `asmatrix` does not make a copy if the input is already + a matrix or an ndarray. Equivalent to ``matrix(data, copy=False)``. + + Parameters + ---------- + data : array_like + Input data. + dtype : data-type + Data-type of the output matrix. + + Returns + ------- + mat : matrix + `data` interpreted as a matrix. + + Examples + -------- + >>> x = np.array([[1, 2], [3, 4]]) + + >>> m = np.asmatrix(x) + + >>> x[0,0] = 5 + + >>> m + matrix([[5, 2], + [3, 4]]) + + """ + return matrix(data, dtype=dtype, copy=False) + + +@set_module('numpy') +class matrix(N.ndarray): + """ + matrix(data, dtype=None, copy=True) + + .. note:: It is no longer recommended to use this class, even for linear + algebra. Instead use regular arrays. The class may be removed + in the future. + + Returns a matrix from an array-like object, or from a string of data. + A matrix is a specialized 2-D array that retains its 2-D nature + through operations. It has certain special operators, such as ``*`` + (matrix multiplication) and ``**`` (matrix power). + + Parameters + ---------- + data : array_like or string + If `data` is a string, it is interpreted as a matrix with commas + or spaces separating columns, and semicolons separating rows. + dtype : data-type + Data-type of the output matrix. + copy : bool + If `data` is already an `ndarray`, then this flag determines + whether the data is copied (the default), or whether a view is + constructed. + + See Also + -------- + array + + Examples + -------- + >>> a = np.matrix('1 2; 3 4') + >>> a + matrix([[1, 2], + [3, 4]]) + + >>> np.matrix([[1, 2], [3, 4]]) + matrix([[1, 2], + [3, 4]]) + + """ + __array_priority__ = 10.0 + def __new__(subtype, data, dtype=None, copy=True): + warnings.warn('the matrix subclass is not the recommended way to ' + 'represent matrices or deal with linear algebra (see ' + 'https://docs.scipy.org/doc/numpy/user/' + 'numpy-for-matlab-users.html). ' + 'Please adjust your code to use regular ndarray.', + PendingDeprecationWarning, stacklevel=2) + if isinstance(data, matrix): + dtype2 = data.dtype + if (dtype is None): + dtype = dtype2 + if (dtype2 == dtype) and (not copy): + return data + return data.astype(dtype) + + if isinstance(data, N.ndarray): + if dtype is None: + intype = data.dtype + else: + intype = N.dtype(dtype) + new = data.view(subtype) + if intype != data.dtype: + return new.astype(intype) + if copy: return new.copy() + else: return new + + if isinstance(data, str): + data = _convert_from_string(data) + + # now convert data to an array + arr = N.array(data, dtype=dtype, copy=copy) + ndim = arr.ndim + shape = arr.shape + if (ndim > 2): + raise ValueError("matrix must be 2-dimensional") + elif ndim == 0: + shape = (1, 1) + elif ndim == 1: + shape = (1, shape[0]) + + order = 'C' + if (ndim == 2) and arr.flags.fortran: + order = 'F' + + if not (order or arr.flags.contiguous): + arr = arr.copy() + + ret = N.ndarray.__new__(subtype, shape, arr.dtype, + buffer=arr, + order=order) + return ret + + def __array_finalize__(self, obj): + self._getitem = False + if (isinstance(obj, matrix) and obj._getitem): return + ndim = self.ndim + if (ndim == 2): + return + if (ndim > 2): + newshape = tuple([x for x in self.shape if x > 1]) + ndim = len(newshape) + if ndim == 2: + self.shape = newshape + return + elif (ndim > 2): + raise ValueError("shape too large to be a matrix.") + else: + newshape = self.shape + if ndim == 0: + self.shape = (1, 1) + elif ndim == 1: + self.shape = (1, newshape[0]) + return + + def __getitem__(self, index): + self._getitem = True + + try: + out = N.ndarray.__getitem__(self, index) + finally: + self._getitem = False + + if not isinstance(out, N.ndarray): + return out + + if out.ndim == 0: + return out[()] + if out.ndim == 1: + sh = out.shape[0] + # Determine when we should have a column array + try: + n = len(index) + except Exception: + n = 0 + if n > 1 and isscalar(index[1]): + out.shape = (sh, 1) + else: + out.shape = (1, sh) + return out + + def __mul__(self, other): + if isinstance(other, (N.ndarray, list, tuple)) : + # This promotes 1-D vectors to row vectors + return N.dot(self, asmatrix(other)) + if isscalar(other) or not hasattr(other, '__rmul__') : + return N.dot(self, other) + return NotImplemented + + def __rmul__(self, other): + return N.dot(other, self) + + def __imul__(self, other): + self[:] = self * other + return self + + def __pow__(self, other): + return matrix_power(self, other) + + def __ipow__(self, other): + self[:] = self ** other + return self + + def __rpow__(self, other): + return NotImplemented + + def _align(self, axis): + """A convenience function for operations that need to preserve axis + orientation. + """ + if axis is None: + return self[0, 0] + elif axis==0: + return self + elif axis==1: + return self.transpose() + else: + raise ValueError("unsupported axis") + + def _collapse(self, axis): + """A convenience function for operations that want to collapse + to a scalar like _align, but are using keepdims=True + """ + if axis is None: + return self[0, 0] + else: + return self + + # Necessary because base-class tolist expects dimension + # reduction by x[0] + def tolist(self): + """ + Return the matrix as a (possibly nested) list. + + See `ndarray.tolist` for full documentation. + + See Also + -------- + ndarray.tolist + + Examples + -------- + >>> x = np.matrix(np.arange(12).reshape((3,4))); x + matrix([[ 0, 1, 2, 3], + [ 4, 5, 6, 7], + [ 8, 9, 10, 11]]) + >>> x.tolist() + [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]] + + """ + return self.__array__().tolist() + + # To preserve orientation of result... + def sum(self, axis=None, dtype=None, out=None): + """ + Returns the sum of the matrix elements, along the given axis. + + Refer to `numpy.sum` for full documentation. + + See Also + -------- + numpy.sum + + Notes + ----- + This is the same as `ndarray.sum`, except that where an `ndarray` would + be returned, a `matrix` object is returned instead. + + Examples + -------- + >>> x = np.matrix([[1, 2], [4, 3]]) + >>> x.sum() + 10 + >>> x.sum(axis=1) + matrix([[3], + [7]]) + >>> x.sum(axis=1, dtype='float') + matrix([[3.], + [7.]]) + >>> out = np.zeros((2, 1), dtype='float') + >>> x.sum(axis=1, dtype='float', out=np.asmatrix(out)) + matrix([[3.], + [7.]]) + + """ + return N.ndarray.sum(self, axis, dtype, out, keepdims=True)._collapse(axis) + + + # To update docstring from array to matrix... + def squeeze(self, axis=None): + """ + Return a possibly reshaped matrix. + + Refer to `numpy.squeeze` for more documentation. + + Parameters + ---------- + axis : None or int or tuple of ints, optional + Selects a subset of the axes of length one in the shape. + If an axis is selected with shape entry greater than one, + an error is raised. + + Returns + ------- + squeezed : matrix + The matrix, but as a (1, N) matrix if it had shape (N, 1). + + See Also + -------- + numpy.squeeze : related function + + Notes + ----- + If `m` has a single column then that column is returned + as the single row of a matrix. Otherwise `m` is returned. + The returned matrix is always either `m` itself or a view into `m`. + Supplying an axis keyword argument will not affect the returned matrix + but it may cause an error to be raised. + + Examples + -------- + >>> c = np.matrix([[1], [2]]) + >>> c + matrix([[1], + [2]]) + >>> c.squeeze() + matrix([[1, 2]]) + >>> r = c.T + >>> r + matrix([[1, 2]]) + >>> r.squeeze() + matrix([[1, 2]]) + >>> m = np.matrix([[1, 2], [3, 4]]) + >>> m.squeeze() + matrix([[1, 2], + [3, 4]]) + + """ + return N.ndarray.squeeze(self, axis=axis) + + + # To update docstring from array to matrix... + def flatten(self, order='C'): + """ + Return a flattened copy of the matrix. + + All `N` elements of the matrix are placed into a single row. + + Parameters + ---------- + order : {'C', 'F', 'A', 'K'}, optional + 'C' means to flatten in row-major (C-style) order. 'F' means to + flatten in column-major (Fortran-style) order. 'A' means to + flatten in column-major order if `m` is Fortran *contiguous* in + memory, row-major order otherwise. 'K' means to flatten `m` in + the order the elements occur in memory. The default is 'C'. + + Returns + ------- + y : matrix + A copy of the matrix, flattened to a `(1, N)` matrix where `N` + is the number of elements in the original matrix. + + See Also + -------- + ravel : Return a flattened array. + flat : A 1-D flat iterator over the matrix. + + Examples + -------- + >>> m = np.matrix([[1,2], [3,4]]) + >>> m.flatten() + matrix([[1, 2, 3, 4]]) + >>> m.flatten('F') + matrix([[1, 3, 2, 4]]) + + """ + return N.ndarray.flatten(self, order=order) + + def mean(self, axis=None, dtype=None, out=None): + """ + Returns the average of the matrix elements along the given axis. + + Refer to `numpy.mean` for full documentation. + + See Also + -------- + numpy.mean + + Notes + ----- + Same as `ndarray.mean` except that, where that returns an `ndarray`, + this returns a `matrix` object. + + Examples + -------- + >>> x = np.matrix(np.arange(12).reshape((3, 4))) + >>> x + matrix([[ 0, 1, 2, 3], + [ 4, 5, 6, 7], + [ 8, 9, 10, 11]]) + >>> x.mean() + 5.5 + >>> x.mean(0) + matrix([[4., 5., 6., 7.]]) + >>> x.mean(1) + matrix([[ 1.5], + [ 5.5], + [ 9.5]]) + + """ + return N.ndarray.mean(self, axis, dtype, out, keepdims=True)._collapse(axis) + + def std(self, axis=None, dtype=None, out=None, ddof=0): + """ + Return the standard deviation of the array elements along the given axis. + + Refer to `numpy.std` for full documentation. + + See Also + -------- + numpy.std + + Notes + ----- + This is the same as `ndarray.std`, except that where an `ndarray` would + be returned, a `matrix` object is returned instead. + + Examples + -------- + >>> x = np.matrix(np.arange(12).reshape((3, 4))) + >>> x + matrix([[ 0, 1, 2, 3], + [ 4, 5, 6, 7], + [ 8, 9, 10, 11]]) + >>> x.std() + 3.4520525295346629 # may vary + >>> x.std(0) + matrix([[ 3.26598632, 3.26598632, 3.26598632, 3.26598632]]) # may vary + >>> x.std(1) + matrix([[ 1.11803399], + [ 1.11803399], + [ 1.11803399]]) + + """ + return N.ndarray.std(self, axis, dtype, out, ddof, keepdims=True)._collapse(axis) + + def var(self, axis=None, dtype=None, out=None, ddof=0): + """ + Returns the variance of the matrix elements, along the given axis. + + Refer to `numpy.var` for full documentation. + + See Also + -------- + numpy.var + + Notes + ----- + This is the same as `ndarray.var`, except that where an `ndarray` would + be returned, a `matrix` object is returned instead. + + Examples + -------- + >>> x = np.matrix(np.arange(12).reshape((3, 4))) + >>> x + matrix([[ 0, 1, 2, 3], + [ 4, 5, 6, 7], + [ 8, 9, 10, 11]]) + >>> x.var() + 11.916666666666666 + >>> x.var(0) + matrix([[ 10.66666667, 10.66666667, 10.66666667, 10.66666667]]) # may vary + >>> x.var(1) + matrix([[1.25], + [1.25], + [1.25]]) + + """ + return N.ndarray.var(self, axis, dtype, out, ddof, keepdims=True)._collapse(axis) + + def prod(self, axis=None, dtype=None, out=None): + """ + Return the product of the array elements over the given axis. + + Refer to `prod` for full documentation. + + See Also + -------- + prod, ndarray.prod + + Notes + ----- + Same as `ndarray.prod`, except, where that returns an `ndarray`, this + returns a `matrix` object instead. + + Examples + -------- + >>> x = np.matrix(np.arange(12).reshape((3,4))); x + matrix([[ 0, 1, 2, 3], + [ 4, 5, 6, 7], + [ 8, 9, 10, 11]]) + >>> x.prod() + 0 + >>> x.prod(0) + matrix([[ 0, 45, 120, 231]]) + >>> x.prod(1) + matrix([[ 0], + [ 840], + [7920]]) + + """ + return N.ndarray.prod(self, axis, dtype, out, keepdims=True)._collapse(axis) + + def any(self, axis=None, out=None): + """ + Test whether any array element along a given axis evaluates to True. + + Refer to `numpy.any` for full documentation. + + Parameters + ---------- + axis : int, optional + Axis along which logical OR is performed + out : ndarray, optional + Output to existing array instead of creating new one, must have + same shape as expected output + + Returns + ------- + any : bool, ndarray + Returns a single bool if `axis` is ``None``; otherwise, + returns `ndarray` + + """ + return N.ndarray.any(self, axis, out, keepdims=True)._collapse(axis) + + def all(self, axis=None, out=None): + """ + Test whether all matrix elements along a given axis evaluate to True. + + Parameters + ---------- + See `numpy.all` for complete descriptions + + See Also + -------- + numpy.all + + Notes + ----- + This is the same as `ndarray.all`, but it returns a `matrix` object. + + Examples + -------- + >>> x = np.matrix(np.arange(12).reshape((3,4))); x + matrix([[ 0, 1, 2, 3], + [ 4, 5, 6, 7], + [ 8, 9, 10, 11]]) + >>> y = x[0]; y + matrix([[0, 1, 2, 3]]) + >>> (x == y) + matrix([[ True, True, True, True], + [False, False, False, False], + [False, False, False, False]]) + >>> (x == y).all() + False + >>> (x == y).all(0) + matrix([[False, False, False, False]]) + >>> (x == y).all(1) + matrix([[ True], + [False], + [False]]) + + """ + return N.ndarray.all(self, axis, out, keepdims=True)._collapse(axis) + + def max(self, axis=None, out=None): + """ + Return the maximum value along an axis. + + Parameters + ---------- + See `amax` for complete descriptions + + See Also + -------- + amax, ndarray.max + + Notes + ----- + This is the same as `ndarray.max`, but returns a `matrix` object + where `ndarray.max` would return an ndarray. + + Examples + -------- + >>> x = np.matrix(np.arange(12).reshape((3,4))); x + matrix([[ 0, 1, 2, 3], + [ 4, 5, 6, 7], + [ 8, 9, 10, 11]]) + >>> x.max() + 11 + >>> x.max(0) + matrix([[ 8, 9, 10, 11]]) + >>> x.max(1) + matrix([[ 3], + [ 7], + [11]]) + + """ + return N.ndarray.max(self, axis, out, keepdims=True)._collapse(axis) + + def argmax(self, axis=None, out=None): + """ + Indexes of the maximum values along an axis. + + Return the indexes of the first occurrences of the maximum values + along the specified axis. If axis is None, the index is for the + flattened matrix. + + Parameters + ---------- + See `numpy.argmax` for complete descriptions + + See Also + -------- + numpy.argmax + + Notes + ----- + This is the same as `ndarray.argmax`, but returns a `matrix` object + where `ndarray.argmax` would return an `ndarray`. + + Examples + -------- + >>> x = np.matrix(np.arange(12).reshape((3,4))); x + matrix([[ 0, 1, 2, 3], + [ 4, 5, 6, 7], + [ 8, 9, 10, 11]]) + >>> x.argmax() + 11 + >>> x.argmax(0) + matrix([[2, 2, 2, 2]]) + >>> x.argmax(1) + matrix([[3], + [3], + [3]]) + + """ + return N.ndarray.argmax(self, axis, out)._align(axis) + + def min(self, axis=None, out=None): + """ + Return the minimum value along an axis. + + Parameters + ---------- + See `amin` for complete descriptions. + + See Also + -------- + amin, ndarray.min + + Notes + ----- + This is the same as `ndarray.min`, but returns a `matrix` object + where `ndarray.min` would return an ndarray. + + Examples + -------- + >>> x = -np.matrix(np.arange(12).reshape((3,4))); x + matrix([[ 0, -1, -2, -3], + [ -4, -5, -6, -7], + [ -8, -9, -10, -11]]) + >>> x.min() + -11 + >>> x.min(0) + matrix([[ -8, -9, -10, -11]]) + >>> x.min(1) + matrix([[ -3], + [ -7], + [-11]]) + + """ + return N.ndarray.min(self, axis, out, keepdims=True)._collapse(axis) + + def argmin(self, axis=None, out=None): + """ + Indexes of the minimum values along an axis. + + Return the indexes of the first occurrences of the minimum values + along the specified axis. If axis is None, the index is for the + flattened matrix. + + Parameters + ---------- + See `numpy.argmin` for complete descriptions. + + See Also + -------- + numpy.argmin + + Notes + ----- + This is the same as `ndarray.argmin`, but returns a `matrix` object + where `ndarray.argmin` would return an `ndarray`. + + Examples + -------- + >>> x = -np.matrix(np.arange(12).reshape((3,4))); x + matrix([[ 0, -1, -2, -3], + [ -4, -5, -6, -7], + [ -8, -9, -10, -11]]) + >>> x.argmin() + 11 + >>> x.argmin(0) + matrix([[2, 2, 2, 2]]) + >>> x.argmin(1) + matrix([[3], + [3], + [3]]) + + """ + return N.ndarray.argmin(self, axis, out)._align(axis) + + def ptp(self, axis=None, out=None): + """ + Peak-to-peak (maximum - minimum) value along the given axis. + + Refer to `numpy.ptp` for full documentation. + + See Also + -------- + numpy.ptp + + Notes + ----- + Same as `ndarray.ptp`, except, where that would return an `ndarray` object, + this returns a `matrix` object. + + Examples + -------- + >>> x = np.matrix(np.arange(12).reshape((3,4))); x + matrix([[ 0, 1, 2, 3], + [ 4, 5, 6, 7], + [ 8, 9, 10, 11]]) + >>> x.ptp() + 11 + >>> x.ptp(0) + matrix([[8, 8, 8, 8]]) + >>> x.ptp(1) + matrix([[3], + [3], + [3]]) + + """ + return N.ndarray.ptp(self, axis, out)._align(axis) + + @property + def I(self): + """ + Returns the (multiplicative) inverse of invertible `self`. + + Parameters + ---------- + None + + Returns + ------- + ret : matrix object + If `self` is non-singular, `ret` is such that ``ret * self`` == + ``self * ret`` == ``np.matrix(np.eye(self[0,:].size))`` all return + ``True``. + + Raises + ------ + numpy.linalg.LinAlgError: Singular matrix + If `self` is singular. + + See Also + -------- + linalg.inv + + Examples + -------- + >>> m = np.matrix('[1, 2; 3, 4]'); m + matrix([[1, 2], + [3, 4]]) + >>> m.getI() + matrix([[-2. , 1. ], + [ 1.5, -0.5]]) + >>> m.getI() * m + matrix([[ 1., 0.], # may vary + [ 0., 1.]]) + + """ + M, N = self.shape + if M == N: + from numpy.linalg import inv as func + else: + from numpy.linalg import pinv as func + return asmatrix(func(self)) + + @property + def A(self): + """ + Return `self` as an `ndarray` object. + + Equivalent to ``np.asarray(self)``. + + Parameters + ---------- + None + + Returns + ------- + ret : ndarray + `self` as an `ndarray` + + Examples + -------- + >>> x = np.matrix(np.arange(12).reshape((3,4))); x + matrix([[ 0, 1, 2, 3], + [ 4, 5, 6, 7], + [ 8, 9, 10, 11]]) + >>> x.getA() + array([[ 0, 1, 2, 3], + [ 4, 5, 6, 7], + [ 8, 9, 10, 11]]) + + """ + return self.__array__() + + @property + def A1(self): + """ + Return `self` as a flattened `ndarray`. + + Equivalent to ``np.asarray(x).ravel()`` + + Parameters + ---------- + None + + Returns + ------- + ret : ndarray + `self`, 1-D, as an `ndarray` + + Examples + -------- + >>> x = np.matrix(np.arange(12).reshape((3,4))); x + matrix([[ 0, 1, 2, 3], + [ 4, 5, 6, 7], + [ 8, 9, 10, 11]]) + >>> x.getA1() + array([ 0, 1, 2, ..., 9, 10, 11]) + + + """ + return self.__array__().ravel() + + + def ravel(self, order='C'): + """ + Return a flattened matrix. + + Refer to `numpy.ravel` for more documentation. + + Parameters + ---------- + order : {'C', 'F', 'A', 'K'}, optional + The elements of `m` are read using this index order. 'C' means to + index the elements in C-like order, with the last axis index + changing fastest, back to the first axis index changing slowest. + 'F' means to index the elements in Fortran-like index order, with + the first index changing fastest, and the last index changing + slowest. Note that the 'C' and 'F' options take no account of the + memory layout of the underlying array, and only refer to the order + of axis indexing. 'A' means to read the elements in Fortran-like + index order if `m` is Fortran *contiguous* in memory, C-like order + otherwise. 'K' means to read the elements in the order they occur + in memory, except for reversing the data when strides are negative. + By default, 'C' index order is used. + + Returns + ------- + ret : matrix + Return the matrix flattened to shape `(1, N)` where `N` + is the number of elements in the original matrix. + A copy is made only if necessary. + + See Also + -------- + matrix.flatten : returns a similar output matrix but always a copy + matrix.flat : a flat iterator on the array. + numpy.ravel : related function which returns an ndarray + + """ + return N.ndarray.ravel(self, order=order) + + @property + def T(self): + """ + Returns the transpose of the matrix. + + Does *not* conjugate! For the complex conjugate transpose, use ``.H``. + + Parameters + ---------- + None + + Returns + ------- + ret : matrix object + The (non-conjugated) transpose of the matrix. + + See Also + -------- + transpose, getH + + Examples + -------- + >>> m = np.matrix('[1, 2; 3, 4]') + >>> m + matrix([[1, 2], + [3, 4]]) + >>> m.getT() + matrix([[1, 3], + [2, 4]]) + + """ + return self.transpose() + + @property + def H(self): + """ + Returns the (complex) conjugate transpose of `self`. + + Equivalent to ``np.transpose(self)`` if `self` is real-valued. + + Parameters + ---------- + None + + Returns + ------- + ret : matrix object + complex conjugate transpose of `self` + + Examples + -------- + >>> x = np.matrix(np.arange(12).reshape((3,4))) + >>> z = x - 1j*x; z + matrix([[ 0. +0.j, 1. -1.j, 2. -2.j, 3. -3.j], + [ 4. -4.j, 5. -5.j, 6. -6.j, 7. -7.j], + [ 8. -8.j, 9. -9.j, 10.-10.j, 11.-11.j]]) + >>> z.getH() + matrix([[ 0. -0.j, 4. +4.j, 8. +8.j], + [ 1. +1.j, 5. +5.j, 9. +9.j], + [ 2. +2.j, 6. +6.j, 10.+10.j], + [ 3. +3.j, 7. +7.j, 11.+11.j]]) + + """ + if issubclass(self.dtype.type, N.complexfloating): + return self.transpose().conjugate() + else: + return self.transpose() + + # kept for compatibility + getT = T.fget + getA = A.fget + getA1 = A1.fget + getH = H.fget + getI = I.fget + +def _from_string(str, gdict, ldict): + rows = str.split(';') + rowtup = [] + for row in rows: + trow = row.split(',') + newrow = [] + for x in trow: + newrow.extend(x.split()) + trow = newrow + coltup = [] + for col in trow: + col = col.strip() + try: + thismat = ldict[col] + except KeyError: + try: + thismat = gdict[col] + except KeyError as e: + raise NameError(f"name {col!r} is not defined") from None + + coltup.append(thismat) + rowtup.append(concatenate(coltup, axis=-1)) + return concatenate(rowtup, axis=0) + + +@set_module('numpy') +def bmat(obj, ldict=None, gdict=None): + """ + Build a matrix object from a string, nested sequence, or array. + + Parameters + ---------- + obj : str or array_like + Input data. If a string, variables in the current scope may be + referenced by name. + ldict : dict, optional + A dictionary that replaces local operands in current frame. + Ignored if `obj` is not a string or `gdict` is None. + gdict : dict, optional + A dictionary that replaces global operands in current frame. + Ignored if `obj` is not a string. + + Returns + ------- + out : matrix + Returns a matrix object, which is a specialized 2-D array. + + See Also + -------- + block : + A generalization of this function for N-d arrays, that returns normal + ndarrays. + + Examples + -------- + >>> A = np.mat('1 1; 1 1') + >>> B = np.mat('2 2; 2 2') + >>> C = np.mat('3 4; 5 6') + >>> D = np.mat('7 8; 9 0') + + All the following expressions construct the same block matrix: + + >>> np.bmat([[A, B], [C, D]]) + matrix([[1, 1, 2, 2], + [1, 1, 2, 2], + [3, 4, 7, 8], + [5, 6, 9, 0]]) + >>> np.bmat(np.r_[np.c_[A, B], np.c_[C, D]]) + matrix([[1, 1, 2, 2], + [1, 1, 2, 2], + [3, 4, 7, 8], + [5, 6, 9, 0]]) + >>> np.bmat('A,B; C,D') + matrix([[1, 1, 2, 2], + [1, 1, 2, 2], + [3, 4, 7, 8], + [5, 6, 9, 0]]) + + """ + if isinstance(obj, str): + if gdict is None: + # get previous frame + frame = sys._getframe().f_back + glob_dict = frame.f_globals + loc_dict = frame.f_locals + else: + glob_dict = gdict + loc_dict = ldict + + return matrix(_from_string(obj, glob_dict, loc_dict)) + + if isinstance(obj, (tuple, list)): + # [[A,B],[C,D]] + arr_rows = [] + for row in obj: + if isinstance(row, N.ndarray): # not 2-d + return matrix(concatenate(obj, axis=-1)) + else: + arr_rows.append(concatenate(row, axis=-1)) + return matrix(concatenate(arr_rows, axis=0)) + if isinstance(obj, N.ndarray): + return matrix(obj) + +mat = asmatrix diff --git a/LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/numpy/matrixlib/defmatrix.pyi b/LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/numpy/matrixlib/defmatrix.pyi new file mode 100644 index 0000000000000000000000000000000000000000..9d0d1ee50b6600bce80f1f5b1363e5ee3102a02a --- /dev/null +++ b/LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/numpy/matrixlib/defmatrix.pyi @@ -0,0 +1,16 @@ +from collections.abc import Sequence, Mapping +from typing import Any +from numpy import matrix as matrix +from numpy._typing import ArrayLike, DTypeLike, NDArray + +__all__: list[str] + +def bmat( + obj: str | Sequence[ArrayLike] | NDArray[Any], + ldict: None | Mapping[str, Any] = ..., + gdict: None | Mapping[str, Any] = ..., +) -> matrix[Any, Any]: ... + +def asmatrix(data: ArrayLike, dtype: DTypeLike = ...) -> matrix[Any, Any]: ... + +mat = asmatrix diff --git a/LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/numpy/matrixlib/setup.py b/LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/numpy/matrixlib/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..4fed75de1cbc22357c675fd8ce2d52cbb6829b50 --- /dev/null +++ b/LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/numpy/matrixlib/setup.py @@ -0,0 +1,12 @@ +#!/usr/bin/env python3 +def configuration(parent_package='', top_path=None): + from numpy.distutils.misc_util import Configuration + config = Configuration('matrixlib', parent_package, top_path) + config.add_subpackage('tests') + config.add_data_files('*.pyi') + return config + +if __name__ == "__main__": + from numpy.distutils.core import setup + config = configuration(top_path='').todict() + setup(**config) diff --git a/LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/transformers/models/roberta/__init__.py b/LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/transformers/models/roberta/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2e8011675176b7e122895b6d6913065f89c2229e --- /dev/null +++ b/LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/transformers/models/roberta/__init__.py @@ -0,0 +1,28 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_roberta import * + from .modeling_roberta import * + from .tokenization_roberta import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/transformers/models/roberta/tokenization_roberta_old.py b/LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/transformers/models/roberta/tokenization_roberta_old.py new file mode 100644 index 0000000000000000000000000000000000000000..3dceda4c310fe3b53b97048f91ed024bc6503c1f --- /dev/null +++ b/LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/transformers/models/roberta/tokenization_roberta_old.py @@ -0,0 +1,262 @@ +# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Fast Tokenization classes for RoBERTa.""" + +import json + +from tokenizers import processors + +from ...tokenization_utils_base import AddedToken, BatchEncoding +from ...tokenization_utils_tokenizers import PreTrainedTokenizerFast +from ...utils import logging +from .tokenization_roberta import RobertaTokenizer + + +logger = logging.get_logger(__name__) + +VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"} + + +class RobertaTokenizerFast(PreTrainedTokenizerFast): + """ + Construct a "fast" RoBERTa tokenizer (backed by HuggingFace's *tokenizers* library), derived from the GPT-2 + tokenizer, using byte-level Byte-Pair-Encoding. + + This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will + be encoded differently whether it is at the beginning of the sentence (without space) or not: + + ```python + >>> from transformers import RobertaTokenizerFast + + >>> tokenizer = RobertaTokenizerFast.from_pretrained("FacebookAI/roberta-base") + >>> tokenizer("Hello world")["input_ids"] + [0, 31414, 232, 2] + + >>> tokenizer(" Hello world")["input_ids"] + [0, 20920, 232, 2] + ``` + + You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you + call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance. + + + + When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`. + + + + This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should + refer to this superclass for more information regarding those methods. + + Args: + vocab_file (`str`): + Path to the vocabulary file. + merges_file (`str`): + Path to the merges file. + errors (`str`, *optional*, defaults to `"replace"`): + Paradigm to follow when decoding bytes to UTF-8. See + [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information. + bos_token (`str`, *optional*, defaults to `""`): + The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. + + + + When building a sequence using special tokens, this is not the token that is used for the beginning of + sequence. The token used is the `cls_token`. + + + + eos_token (`str`, *optional*, defaults to `""`): + The end of sequence token. + + + + When building a sequence using special tokens, this is not the token that is used for the end of sequence. + The token used is the `sep_token`. + + + + sep_token (`str`, *optional*, defaults to `""`): + The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for + sequence classification or for a text and a question for question answering. It is also used as the last + token of a sequence built with special tokens. + cls_token (`str`, *optional*, defaults to `""`): + The classifier token which is used when doing sequence classification (classification of the whole sequence + instead of per-token classification). It is the first token of the sequence when built with special tokens. + unk_token (`str`, *optional*, defaults to `""`): + The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this + token instead. + pad_token (`str`, *optional*, defaults to `""`): + The token used for padding, for example when batching sequences of different lengths. + mask_token (`str`, *optional*, defaults to `""`): + The token used for masking values. This is the token used when training this model with masked language + modeling. This is the token which the model will try to predict. + add_prefix_space (`bool`, *optional*, defaults to `False`): + Whether or not to add an initial space to the input. This allows to treat the leading word just as any + other word. (RoBERTa tokenizer detect beginning of words by the preceding space). + trim_offsets (`bool`, *optional*, defaults to `True`): + Whether the post processing step should trim offsets to avoid including whitespaces. + """ + + vocab_files_names = VOCAB_FILES_NAMES + model_input_names = ["input_ids", "attention_mask"] + slow_tokenizer_class = RobertaTokenizer + + def __init__( + self, + vocab_file=None, + merges_file=None, + tokenizer_file=None, + errors="replace", + bos_token="", + eos_token="", + sep_token="", + cls_token="", + unk_token="", + pad_token="", + mask_token="", + add_prefix_space=False, + trim_offsets=True, + **kwargs, + ): + mask_token = ( + AddedToken(mask_token, lstrip=True, rstrip=False, normalized=False) + if isinstance(mask_token, str) + else mask_token + ) + super().__init__( + vocab_file, + merges_file, + tokenizer_file=tokenizer_file, + errors=errors, + bos_token=bos_token, + eos_token=eos_token, + sep_token=sep_token, + cls_token=cls_token, + unk_token=unk_token, + pad_token=pad_token, + mask_token=mask_token, + add_prefix_space=add_prefix_space, + trim_offsets=trim_offsets, + **kwargs, + ) + + tokenizer_component = "post_processor" + tokenizer_component_instance = getattr(self.backend_tokenizer, tokenizer_component, None) + if tokenizer_component_instance: + state = json.loads(tokenizer_component_instance.__getstate__()) + + # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class` + if "sep" in state: + state["sep"] = tuple(state["sep"]) + if "cls" in state: + state["cls"] = tuple(state["cls"]) + + changes_to_apply = False + + if state.get("add_prefix_space", add_prefix_space) != add_prefix_space: + state["add_prefix_space"] = add_prefix_space + changes_to_apply = True + + if state.get("trim_offsets", trim_offsets) != trim_offsets: + state["trim_offsets"] = trim_offsets + changes_to_apply = True + + if changes_to_apply: + component_class = getattr(processors, state.pop("type")) + new_value = component_class(**state) + setattr(self.backend_tokenizer, tokenizer_component, new_value) + + @property + def mask_token(self) -> str: + """ + `str`: Mask token, to use when training a model with masked-language modeling. Log an error if used while not + having been set. + + Roberta tokenizer has a special mask token to be usable in the fill-mask pipeline. The mask token will greedily + comprise the space before the **. + """ + if self._mask_token is None: + if self.verbose: + logger.error("Using mask_token, but it is not set yet.") + return None + return str(self._mask_token) + + @mask_token.setter + def mask_token(self, value): + """ + Overriding the default behavior of the mask token to have it eat the space before it. + + This is needed to preserve backward compatibility with all the previously used models based on Roberta. + """ + # Mask token behave like a normal word, i.e. include the space before it + # So we set lstrip to True + value = AddedToken(value, lstrip=True, rstrip=False) if isinstance(value, str) else value + self._mask_token = value + + def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding: + is_split_into_words = kwargs.get("is_split_into_words", False) + assert self.add_prefix_space or not is_split_into_words, ( + f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True " + "to use it with pretokenized inputs." + ) + + return super()._batch_encode_plus(*args, **kwargs) + + def _encode_plus(self, *args, **kwargs) -> BatchEncoding: + is_split_into_words = kwargs.get("is_split_into_words", False) + + assert self.add_prefix_space or not is_split_into_words, ( + f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True " + "to use it with pretokenized inputs." + ) + + return super()._encode_plus(*args, **kwargs) + + def save_vocabulary(self, save_directory: str, filename_prefix: str | None = None) -> tuple[str]: + files = self._tokenizer.model.save(save_directory, name=filename_prefix) + return tuple(files) + + def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): + output = [self.bos_token_id] + token_ids_0 + [self.eos_token_id] + if token_ids_1 is None: + return output + + return output + [self.eos_token_id] + token_ids_1 + [self.eos_token_id] + + def create_token_type_ids_from_sequences( + self, token_ids_0: list[int], token_ids_1: list[int] | None = None + ) -> list[int]: + """ + Create a mask from the two sequences passed to be used in a sequence-pair classification task. RoBERTa does not + make use of token type ids, therefore a list of zeros is returned. + + Args: + token_ids_0 (`list[int]`): + List of IDs. + token_ids_1 (`list[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `list[int]`: List of zeros. + """ + sep = [self.sep_token_id] + cls = [self.cls_token_id] + + if token_ids_1 is None: + return len(cls + token_ids_0 + sep) * [0] + return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0] + + +__all__ = ["RobertaTokenizerFast"] diff --git a/LTA_openwebtext_dualt/mini_owt_logdirichlet/runs/owt_t5_elftokenized_full_len1024_C1_to_1024_pow1_d768_l12_h12_gbs512_2x8gpu_50ep_lr1e3_ema0p9999_elfopt_not5_bottleneck128_unfixed_norm_stateprobadd_selfcond_ce_fast_20260609_155046/step_043000.pt 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