diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..362b4c59690b9077e1dab61da72b712c104af471 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +results/figures/phase19b_qa_softqa_pd_w2000_6seeds_summary_20260403_0711_heatmaps.png filter=lfs diff=lfs merge=lfs -text diff --git a/results/csv/3seed_top3_results_20260310_2242.csv b/results/csv/3seed_top3_results_20260310_2242.csv new file mode 100644 index 0000000000000000000000000000000000000000..fd8bd51425dfb2d6d5e68a9d3f7b0f567ea35d75 --- /dev/null +++ b/results/csv/3seed_top3_results_20260310_2242.csv @@ -0,0 +1,10 @@ +Variant,Seed,Final_Val_Loss +none,1337,7.3218 +none,42,7.3918 +none,123,7.2759 +vnorm,1337,6.4886 +vnorm,42,6.6105 +vnorm,123,6.5917 +qvnorm,1337,6.4545 +qvnorm,42,6.5394 +qvnorm,123,6.3194 diff --git a/results/csv/a10_replication_runs_20260322_0639.csv b/results/csv/a10_replication_runs_20260322_0639.csv new file mode 100644 index 0000000000000000000000000000000000000000..c433e2ed6a0b6b3e73fc514446d77cd4afa8ef95 --- /dev/null +++ b/results/csv/a10_replication_runs_20260322_0639.csv @@ -0,0 +1,28 @@ +variant,short_name,category,warmup,seed,status,hardware,tokens_per_iter,runtime_sec,params_m,best_val,best_step,final_val,stable,out_dir,error +none,baseline,core,2000,42,ok,A10_24GB,4096,1658.66,123.59,4.4719,10000,4.4719,True,out-openwebtext_none_warmup2000_seed42_a100, +none,baseline,core,2000,1337,ok,A10_24GB,4096,1648.82,123.59,4.4576,10000,4.4576,True,out-openwebtext_none_warmup2000_seed1337_a100, +none,baseline,core,2000,123,ok,A10_24GB,4096,1664.42,123.59,4.4729,10000,4.4729,True,out-openwebtext_none_warmup2000_seed123_a100, +dynamic,cheap_q,core,2000,42,ok,A10_24GB,4096,1764.21,123.64,4.4168,10000,4.4168,True,out-openwebtext_dynamic_warmup2000_seed42_a100, +dynamic,cheap_q,core,2000,1337,ok,A10_24GB,4096,1768.59,123.64,4.4187,10000,4.4187,True,out-openwebtext_dynamic_warmup2000_seed1337_a100, +dynamic,cheap_q,core,2000,123,ok,A10_24GB,4096,1757.57,123.64,4.4312,9750,4.4484,False,out-openwebtext_dynamic_warmup2000_seed123_a100, +dynamic_qconditioned_mlp128,midtier_q,core,2000,42,ok,A10_24GB,4096,1813.42,123.78,4.4047,9750,4.4226,False,out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_a100, +dynamic_qconditioned_mlp128,midtier_q,core,2000,1337,ok,A10_24GB,4096,1809.87,123.78,4.4111,9500,4.4173,False,out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_a100, +dynamic_qconditioned_mlp128,midtier_q,core,2000,123,ok,A10_24GB,4096,1811.18,123.78,4.3958,9750,4.4064,False,out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_a100, 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0000000000000000000000000000000000000000..1f82872707ce344c0be3eec78af0d72183e73eb1 --- /dev/null +++ b/results/csv/a_only_screen_runs_20260320_2335.csv @@ -0,0 +1,2 @@ +variant,variant_short,warmup,seed,status,runtime_sec,params_m,best_val,best_step,final_val,stable,out_dir,error +dynamic_a_conditioned,a_only,2000,42,ok,3749.96,123.64,4.4167,10000,4.4167,True,out-openwebtext_dynamic_a_conditioned_warmup2000_seed42, diff --git a/results/csv/all_variants_results_20260310_2002.csv b/results/csv/all_variants_results_20260310_2002.csv new file mode 100644 index 0000000000000000000000000000000000000000..ce500bcfed5f5b6ad9bff2dfabd2d81c70b3591a --- /dev/null +++ b/results/csv/all_variants_results_20260310_2002.csv @@ -0,0 +1,6 @@ +Variant,Final_Val_Loss,vs_Baseline +none,5.8126,+0.0000 +vnorm,5.3412,-0.4714 +qvnorm,5.3275,-0.4851 +dynamic,5.9240,+0.1114 +dynamic_swiglu,5.8899,+0.0773 diff --git a/results/csv/chaos_confirmation_runs_20260321_2215.csv b/results/csv/chaos_confirmation_runs_20260321_2215.csv new file mode 100644 index 0000000000000000000000000000000000000000..332c483f30e4fa3bcf53b6e15a5b22438f01b67f --- /dev/null +++ b/results/csv/chaos_confirmation_runs_20260321_2215.csv @@ -0,0 +1,13 @@ +variant,short_name,warmup,seed,status,runtime_sec,params_m,best_val,best_step,final_val,stable,out_dir,error +none,baseline,2000,1337,ok,3696.59,123.59,4.4618,9750,4.5041,False,out-openwebtext_none_warmup2000_seed1337_chaos, +none,baseline,2000,123,ok,3648.77,123.59,4.4813,9500,4.5018,False,out-openwebtext_none_warmup2000_seed123_chaos, +dynamic_ones_gate,ones_identity,2000,1337,ok,3585.71,123.59,4.4612,9750,4.5046,False,out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos, +dynamic_ones_gate,ones_identity,2000,123,ok,3556.0,123.59,4.4796,9500,4.5018,False,out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos, 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+dynamic_bernoulli_gate,bernoulli_drop,2000,1337,ok,3734.81,123.59,4.65,9750,4.6947,False,out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos, +dynamic_bernoulli_gate,bernoulli_drop,2000,123,ok,3731.55,123.59,4.6417,9500,4.6618,False,out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos, diff --git a/results/csv/cheap_qa_confirmation_runs_20260320_1854.csv b/results/csv/cheap_qa_confirmation_runs_20260320_1854.csv new file mode 100644 index 0000000000000000000000000000000000000000..e7c3f4a6a0bb9470330f37f17edf72ff4bf22e0d --- /dev/null +++ b/results/csv/cheap_qa_confirmation_runs_20260320_1854.csv @@ -0,0 +1,5 @@ +variant,variant_short,warmup,seed,note,status,runtime_sec,params_m,best_val,best_step,final_val,out_dir,error +dynamic_qa_conditioned,cheap_qa,3000,42,depth probe,ok,3758.93,123.69,4.3711,10000,4.3711,out-openwebtext_dynamic_qa_conditioned_warmup3000_seed42, +dynamic_qa_conditioned,cheap_qa,3000,1337,new,ok,3738.78,123.69,4.364,9750,4.4108,out-openwebtext_dynamic_qa_conditioned_warmup3000_seed1337, +dynamic_qa_conditioned,cheap_qa,3000,123,new,ok,3773.97,123.69,4.3944,9750,4.4029,out-openwebtext_dynamic_qa_conditioned_warmup3000_seed123, +dynamic_qa_conditioned,cheap_qa,2000,42,clean rerun replaces screen anchor,ok,3743.75,123.69,4.3906,10000,4.3906,out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42, diff --git a/results/csv/cheap_qa_followup_runs_20260320_0904.csv b/results/csv/cheap_qa_followup_runs_20260320_0904.csv new file mode 100644 index 0000000000000000000000000000000000000000..b46fde02603bdafc75b51cfc3c40c8dec8d26dc6 --- /dev/null +++ b/results/csv/cheap_qa_followup_runs_20260320_0904.csv @@ -0,0 +1,4 @@ +variant,variant_short,warmup,seed,status,runtime_sec,params_m,best_val,best_step,final_val,out_dir,error +dynamic_qa_conditioned,cheap_qa,3000,42,ok,3738.68,123.69,4.3603,10000,4.3603,out-openwebtext_dynamic_qa_conditioned_warmup3000_seed42, +dynamic_qa_conditioned,cheap_qa,2000,1337,ok,3759.41,123.69,4.3824,9750,4.4278,out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337, +dynamic_qa_conditioned,cheap_qa,2000,123,ok,3769.64,123.69,4.3914,9750,4.4034,out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123, diff --git a/results/csv/dot_gate_runs_20260324_0712.csv b/results/csv/dot_gate_runs_20260324_0712.csv new file mode 100644 index 0000000000000000000000000000000000000000..9f9b2ed10284487da0097b45cad2021dd98c7f55 --- /dev/null +++ b/results/csv/dot_gate_runs_20260324_0712.csv @@ -0,0 +1,7 @@ +variant,short_name,warmup,seed,status,runtime_sec,params_m,best_val,best_step,final_val,stable,out_dir,error +dynamic_dot_elementwise,dot_elementwise,2000,42,ok,3813.58,123.59,4.4574,9750,4.4707,False,out-openwebtext_dot_gate_shared, +dynamic_dot_elementwise,dot_elementwise,2000,1337,ok,3744.31,123.59,4.4374,9750,4.4806,False,out-openwebtext_dot_gate_shared, +dynamic_dot_elementwise,dot_elementwise,2000,123,ok,3736.11,123.59,4.4662,9500,4.4847,False,out-openwebtext_dot_gate_shared, +dynamic_dot_scalar,dot_scalar,2000,42,ok,3797.6,123.59,4.4646,9750,4.478,False,out-openwebtext_dot_gate_shared, +dynamic_dot_scalar,dot_scalar,2000,1337,ok,3794.8,123.59,4.4549,9750,4.4988,False,out-openwebtext_dot_gate_shared, +dynamic_dot_scalar,dot_scalar,2000,123,ok,3794.47,123.59,4.4608,9500,4.4814,False,out-openwebtext_dot_gate_shared, diff --git a/results/csv/faithful_x_screen_runs_seed42_20260319_0733.csv b/results/csv/faithful_x_screen_runs_seed42_20260319_0733.csv new file mode 100644 index 0000000000000000000000000000000000000000..f16658b36e7814a02688a20e97b5e0ff8fe964b6 --- /dev/null +++ b/results/csv/faithful_x_screen_runs_seed42_20260319_0733.csv @@ -0,0 +1,9 @@ +variant,variant_short,warmup,seed,best_train,best_val,final_train,final_val,status,out_dir +dynamic_xconditioned_g1,global_x,500,42,4.9935,4.9626,4.9955,4.9626,ok,out-openwebtext_dynamic_xconditioned_g1_warmup500_seed42 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,500,42,4.8763,4.8963,4.8908,4.8981,ok,out-openwebtext_dynamic_x_g1_headspecific_elementwise_warmup500_seed42 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,4.9272,4.9085,4.9365,4.9722,ok,out-openwebtext_dynamic_x_g1_headspecific_headwise_warmup500_seed42 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,4.8222,4.8102,4.832,4.8102,ok,out-openwebtext_dynamic_x_g1_headshared_elementwise_warmup500_seed42 +dynamic_xconditioned_g1,global_x,2000,42,4.5486,4.5339,4.5486,4.5339,ok,out-openwebtext_dynamic_xconditioned_g1_warmup2000_seed42 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,4.6015,4.6268,4.6119,4.6268,ok,out-openwebtext_dynamic_x_g1_headspecific_elementwise_warmup2000_seed42 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,2000,42,4.5812,4.5605,4.5815,4.6138,ok,out-openwebtext_dynamic_x_g1_headspecific_headwise_warmup2000_seed42 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,2000,42,4.5746,4.5698,4.5834,4.5698,ok,out-openwebtext_dynamic_x_g1_headshared_elementwise_warmup2000_seed42 diff --git a/results/csv/faithful_x_screen_trajectories_seed42_20260319_0733.csv b/results/csv/faithful_x_screen_trajectories_seed42_20260319_0733.csv new file mode 100644 index 0000000000000000000000000000000000000000..d84195f36bc04b60ad4eeb0ef2bd3d8de9e76642 --- /dev/null +++ b/results/csv/faithful_x_screen_trajectories_seed42_20260319_0733.csv @@ -0,0 +1,329 @@ +variant,variant_short,warmup,seed,step,train_loss,val_loss +dynamic_xconditioned_g1,global_x,500,42,0,10.9285,10.9307 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+dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,500,42,4000,5.4446,5.4706 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,500,42,4250,5.4053,5.4094 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,500,42,4500,5.3368,5.3625 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,500,42,4750,5.3792,5.3398 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,500,42,5000,5.2798,5.32 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,500,42,5250,5.2654,5.2574 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,500,42,5500,5.2478,5.2209 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,500,42,5750,5.1944,5.2115 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,500,42,6000,5.1667,5.1457 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,500,42,6250,5.1057,5.1203 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,500,42,6500,5.0884,5.1097 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+dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,500,42,9500,4.8763,4.9261 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,500,42,9750,4.91,4.8963 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,500,42,10000,4.8908,4.8981 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,0,10.9127,10.9097 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,250,6.6983,6.6957 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,500,6.4544,6.4295 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,750,6.2549,6.273 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,1000,6.1686,6.131 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,1250,6.0884,6.0431 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,1500,5.9795,5.9387 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,1750,5.9041,5.8966 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,2000,5.8965,5.8677 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,2250,5.8029,5.7975 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,2500,5.7392,5.7883 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,2750,5.7187,5.7354 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,3000,5.6972,5.7018 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,3250,5.6664,5.6483 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,3500,5.6253,5.6175 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,3750,5.5886,5.6211 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,4000,5.5561,5.5274 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,4250,5.5013,5.5361 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,4500,5.4481,5.4586 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,4750,5.4349,5.442 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,5000,5.4036,5.4031 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,5250,5.3798,5.3329 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,5500,5.3013,5.2927 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,5750,5.267,5.242 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,6000,5.2117,5.2015 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,6250,5.2358,5.2368 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,6500,5.1487,5.1937 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,6750,5.1526,5.1331 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,7000,5.1137,5.1211 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,7250,5.0976,5.0746 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,7500,5.0794,5.0563 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,7750,5.0402,5.0355 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,8000,5.0414,4.9917 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,8250,5.0001,4.985 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,8500,5.0275,4.996 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,8750,4.9722,4.9661 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,9000,5.0004,4.9775 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,9250,4.9272,4.9693 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,9500,5.0059,4.988 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,9750,4.9563,4.9085 +dynamic_x_g1_headspecific_headwise,x_headspec_headwise,500,42,10000,4.9365,4.9722 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,0,10.9641,10.9611 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,250,6.6946,6.7145 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,500,6.4063,6.4462 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,750,6.2785,6.2869 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,1000,6.1378,6.1604 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,1250,6.0541,6.0947 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,1500,5.9642,6.0087 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,1750,5.9412,5.9494 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,2000,5.8454,5.8485 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,2250,5.7756,5.7915 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,2500,5.733,5.7478 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,2750,5.7128,5.7005 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,3000,5.6187,5.6704 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,3250,5.6366,5.6538 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,3500,5.5494,5.5446 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,3750,5.4983,5.528 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,4000,5.4412,5.4151 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,4250,5.3906,5.3826 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,4500,5.3254,5.3364 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,4750,5.2991,5.3062 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,5000,5.2959,5.2653 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,5250,5.2017,5.241 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,5500,5.1919,5.1748 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,5750,5.1508,5.1648 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,6000,5.1504,5.1432 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,6250,5.1222,5.1119 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,6500,5.0735,5.0881 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,6750,5.0261,5.0535 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,7000,5.0331,5.0451 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,7250,4.9561,5.0177 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,7500,4.9575,4.9418 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,7750,4.9341,4.9648 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,8000,4.9301,4.9259 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,8250,4.8973,4.9217 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,8500,4.9018,4.9151 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,42,8750,4.88,4.914 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+dynamic_xconditioned_g1,global_x,2000,42,2250,5.7053,5.7133 +dynamic_xconditioned_g1,global_x,2000,42,2500,5.6119,5.6063 +dynamic_xconditioned_g1,global_x,2000,42,2750,5.5443,5.5253 +dynamic_xconditioned_g1,global_x,2000,42,3000,5.4483,5.4465 +dynamic_xconditioned_g1,global_x,2000,42,3250,5.4092,5.4155 +dynamic_xconditioned_g1,global_x,2000,42,3500,5.3249,5.321 +dynamic_xconditioned_g1,global_x,2000,42,3750,5.2971,5.2945 +dynamic_xconditioned_g1,global_x,2000,42,4000,5.2519,5.2667 +dynamic_xconditioned_g1,global_x,2000,42,4250,5.2004,5.2006 +dynamic_xconditioned_g1,global_x,2000,42,4500,5.1653,5.1513 +dynamic_xconditioned_g1,global_x,2000,42,4750,5.0773,5.1141 +dynamic_xconditioned_g1,global_x,2000,42,5000,5.0762,5.0764 +dynamic_xconditioned_g1,global_x,2000,42,5250,5.0349,5.0262 +dynamic_xconditioned_g1,global_x,2000,42,5500,4.9893,4.99 +dynamic_xconditioned_g1,global_x,2000,42,5750,4.9523,4.9388 +dynamic_xconditioned_g1,global_x,2000,42,6000,4.9266,4.8984 +dynamic_xconditioned_g1,global_x,2000,42,6250,4.8741,4.8675 +dynamic_xconditioned_g1,global_x,2000,42,6500,4.8266,4.8669 +dynamic_xconditioned_g1,global_x,2000,42,6750,4.7998,4.8028 +dynamic_xconditioned_g1,global_x,2000,42,7000,4.7615,4.7785 +dynamic_xconditioned_g1,global_x,2000,42,7250,4.7445,4.7401 +dynamic_xconditioned_g1,global_x,2000,42,7500,4.7307,4.7219 +dynamic_xconditioned_g1,global_x,2000,42,7750,4.6776,4.7009 +dynamic_xconditioned_g1,global_x,2000,42,8000,4.6669,4.6356 +dynamic_xconditioned_g1,global_x,2000,42,8250,4.661,4.6222 +dynamic_xconditioned_g1,global_x,2000,42,8500,4.6093,4.6066 +dynamic_xconditioned_g1,global_x,2000,42,8750,4.6142,4.5991 +dynamic_xconditioned_g1,global_x,2000,42,9000,4.5913,4.5867 +dynamic_xconditioned_g1,global_x,2000,42,9250,4.5846,4.5579 +dynamic_xconditioned_g1,global_x,2000,42,9500,4.5591,4.5727 +dynamic_xconditioned_g1,global_x,2000,42,9750,4.5635,4.5715 +dynamic_xconditioned_g1,global_x,2000,42,10000,4.5486,4.5339 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,0,10.9565,10.9575 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,250,6.9816,6.9874 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,500,6.567,6.5727 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,750,6.339,6.3976 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,1000,6.2527,6.2724 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,1250,6.1274,6.1158 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,1500,6.0107,6.0441 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,1750,5.8995,5.9239 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,2000,5.8766,5.8786 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,2250,5.779,5.7616 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,2500,5.6469,5.6718 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,2750,5.6136,5.6036 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,3000,5.4992,5.5031 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,3250,5.4602,5.4335 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,3500,5.4257,5.3991 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,3750,5.3717,5.3444 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,4000,5.2787,5.3054 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,4250,5.2381,5.2438 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,4500,5.1564,5.1822 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,4750,5.1991,5.1632 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,5000,5.1017,5.1451 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,5250,5.092,5.0842 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,5500,5.0749,5.049 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,5750,5.0103,5.0291 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,6000,4.9769,4.9617 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,6250,4.9067,4.9273 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,6500,4.8786,4.9025 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,6750,4.8752,4.8605 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,7000,4.8269,4.8131 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,7250,4.8373,4.7423 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,7500,4.7856,4.7589 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,7750,4.7414,4.7772 +dynamic_x_g1_headspecific_elementwise,x_headspec_elementwise,2000,42,8000,4.749,4.7025 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b/results/csv/friday_night_runs_20260321_0104.csv new file mode 100644 index 0000000000000000000000000000000000000000..66578e1ca841a956a35e94a62d62f4a0a4b37b77 --- /dev/null +++ b/results/csv/friday_night_runs_20260321_0104.csv @@ -0,0 +1,6 @@ +variant,short_name,description,warmup,seed,status,runtime_sec,params_m,best_val,best_step,final_val,stable,out_dir,error +dynamic_a_conditioned,a_only,learned: A-only Linear(64->64),2000,42,ok,3665.81,123.64,4.4178,10000,4.4178,True,out-openwebtext_dynamic_a_conditioned_warmup2000_seed42, +dynamic_random_gate,random_uniform,"chaos: uniform random [0,1]",2000,42,ok,3576.11,123.59,4.5132,9750,4.5238,False,out-openwebtext_dynamic_random_gate_warmup2000_seed42, +dynamic_ones_gate,ones_identity,sanity: always 1.0 (= baseline),2000,42,ok,3530.33,123.59,4.4702,9750,4.4869,False,out-openwebtext_dynamic_ones_gate_warmup2000_seed42, +dynamic_random_normal,random_normal,"chaos: normal(0.5,0.2) clamped",2000,42,ok,3594.27,123.59,4.4822,9750,4.4982,False,out-openwebtext_dynamic_random_normal_warmup2000_seed42, +dynamic_bernoulli_gate,bernoulli_drop,spicy: binary 50% dropout gate,2000,42,ok,3626.68,123.59,4.6572,9750,4.6653,False,out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42, diff --git a/results/csv/fullq_followup_runs_20260318_0937.csv b/results/csv/fullq_followup_runs_20260318_0937.csv new file mode 100644 index 0000000000000000000000000000000000000000..853d3eab35cabc194a2d7bc693deb4280a91d5c6 --- /dev/null +++ b/results/csv/fullq_followup_runs_20260318_0937.csv @@ -0,0 +1,5 @@ +variant_long,variant_short,warmup,seed,best_val,final_val,status,out_dir +dynamic_qconditioned_fullwidth,full_q,500,1337,4.8264,4.8264,ok,out-openwebtext_dynamic_qconditioned_fullwidth_warmup500_seed1337 +dynamic_qconditioned_fullwidth,full_q,500,123,4.8511,4.8606,ok,out-openwebtext_dynamic_qconditioned_fullwidth_warmup500_seed123 +dynamic_qconditioned_fullwidth,full_q,200,1337,4.9532,4.9532,ok,out-openwebtext_dynamic_qconditioned_fullwidth_warmup200_seed1337 +dynamic_qconditioned_fullwidth,full_q,200,123,4.8501,4.8581,ok,out-openwebtext_dynamic_qconditioned_fullwidth_warmup200_seed123 diff --git a/results/csv/fullq_followup_summary_20260318_0937.csv b/results/csv/fullq_followup_summary_20260318_0937.csv new file mode 100644 index 0000000000000000000000000000000000000000..a6f6976b9bf7600baff506fbec146803102172e4 --- /dev/null +++ b/results/csv/fullq_followup_summary_20260318_0937.csv @@ -0,0 +1,5 @@ +variant_short,warmup,seed,best_val,final_val,delta_vs_20pct_fullq,delta_vs_baseline,delta_vs_cheapq,delta_vs_midtierq,delta_vs_fullx,delta_vs_seed42 +full_q,500,1337,4.8264,4.8264,0.2850999999999999,-0.024200000000000443,-0.011100000000000776,0.015099999999999447,-0.13570000000000082,-0.005000000000000782 +full_q,500,123,4.8511,4.8606,0.31930000000000014,0.009999999999999787,0.023099999999999454,0.04929999999999968,-0.10150000000000059,0.02919999999999945 +full_q,200,1337,4.9532,4.9532,0.41190000000000015,0.02289999999999992,0.04579999999999984,0.07160000000000011,-0.2012999999999998,0.11209999999999987 +full_q,200,123,4.8501,4.8581,0.31680000000000064,-0.0721999999999996,-0.04929999999999968,-0.02349999999999941,-0.29639999999999933,0.017000000000000348 diff --git a/results/csv/fullq_followup_trajectories_20260318_0937.csv b/results/csv/fullq_followup_trajectories_20260318_0937.csv new file mode 100644 index 0000000000000000000000000000000000000000..4d1e9ede21866151cdfb0994db0e0945ad268143 --- /dev/null +++ b/results/csv/fullq_followup_trajectories_20260318_0937.csv @@ -0,0 +1,165 @@ +variant_long,variant_short,warmup,seed,step,val_loss +dynamic_qconditioned_fullwidth,full_q,500,1337,0,10.9625 +dynamic_qconditioned_fullwidth,full_q,500,1337,250,6.7345 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a/results/csv/fullq_overnight_runs_20260317_2339.csv b/results/csv/fullq_overnight_runs_20260317_2339.csv new file mode 100644 index 0000000000000000000000000000000000000000..905f4521f9e298f754c52ab3b1236b99b3f1fa49 --- /dev/null +++ b/results/csv/fullq_overnight_runs_20260317_2339.csv @@ -0,0 +1,3 @@ +variant_long,variant_short,seed,warmup,best_val,final_val,status,out_dir +dynamic_qconditioned_fullwidth,full_q,42,500,4.8314,4.8314,ok,out-openwebtext_dynamic_qconditioned_fullwidth_warmup500_seed42 +dynamic_qconditioned_fullwidth,full_q,42,200,4.8411,4.8411,ok,out-openwebtext_dynamic_qconditioned_fullwidth_warmup200_seed42 diff --git a/results/csv/fullq_overnight_summary_20260317_2339.csv b/results/csv/fullq_overnight_summary_20260317_2339.csv new file mode 100644 index 0000000000000000000000000000000000000000..0c5dce13ffc8f1c842ae1ce769d811220c135d71 --- /dev/null +++ b/results/csv/fullq_overnight_summary_20260317_2339.csv @@ -0,0 +1,3 @@ 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+dynamic_qconditioned_fullwidth,full_q,1337,3000,4.5052,4.5052,ok,out-openwebtext_dynamic_qconditioned_fullwidth_warmup3000_seed1337 +dynamic_qconditioned_fullwidth,full_q,123,3000,4.5307,4.5307,ok,out-openwebtext_dynamic_qconditioned_fullwidth_warmup3000_seed123 diff --git a/results/csv/fullq_warmup3000_remaining_trajectories_20260318_2145.csv b/results/csv/fullq_warmup3000_remaining_trajectories_20260318_2145.csv new file mode 100644 index 0000000000000000000000000000000000000000..d247c0a1495ff3e687b5d013036a455ddcf08ec8 --- /dev/null +++ b/results/csv/fullq_warmup3000_remaining_trajectories_20260318_2145.csv @@ -0,0 +1,83 @@ +variant_long,variant_short,seed,warmup,step,val_loss +dynamic_qconditioned_fullwidth,full_q,1337,3000,0,10.9625 +dynamic_qconditioned_fullwidth,full_q,1337,3000,250,7.1126 +dynamic_qconditioned_fullwidth,full_q,1337,3000,500,6.6561 +dynamic_qconditioned_fullwidth,full_q,1337,3000,750,6.4023 +dynamic_qconditioned_fullwidth,full_q,1337,3000,1000,6.283 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0000000000000000000000000000000000000000..2a59ae200229bdcbd3d43f981c0360dc9d6e3f5f --- /dev/null +++ b/results/csv/fullq_warmup3000_seed42_runs_20260318_1839.csv @@ -0,0 +1,2 @@ +variant_long,variant_short,seed,warmup,best_val,final_val,status,out_dir +dynamic_qconditioned_fullwidth,full_q,42,3000,4.5162,4.5162,ok,out-openwebtext_dynamic_qconditioned_fullwidth_warmup3000_seed42 diff --git a/results/csv/fullq_warmup3000_seed42_summary_20260318_1839.csv b/results/csv/fullq_warmup3000_seed42_summary_20260318_1839.csv new file mode 100644 index 0000000000000000000000000000000000000000..8c973fedb40e376e086e4890a88c064bf9491d89 --- /dev/null +++ b/results/csv/fullq_warmup3000_seed42_summary_20260318_1839.csv @@ -0,0 +1,2 @@ +variant_short,seed,warmup,best_val,final_val,delta_vs_20pct_fullq,delta_vs_fullq_500,delta_vs_fullq_200,delta_vs_midtierq_3000,delta_vs_midtierq_2000,delta_vs_fullx_500,delta_vs_fullx_200 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+dynamic_qconditioned_fullwidth,full_q,42,3000,9750,4.5526 +dynamic_qconditioned_fullwidth,full_q,42,3000,10000,4.5162 diff --git a/results/csv/isolation_missing_trajectories_20260313_1428.csv b/results/csv/isolation_missing_trajectories_20260313_1428.csv new file mode 100644 index 0000000000000000000000000000000000000000..443d8ff13299ebbb3cdc4a3edea2f993c7933516 --- /dev/null +++ b/results/csv/isolation_missing_trajectories_20260313_1428.csv @@ -0,0 +1,7 @@ +Variant_Seed,step_0,step_250,step_500,step_750,step_1000,step_1250,step_1500,step_1750,step_2000,step_2250,step_2500,step_2750,step_3000,step_3250,step_3500,step_3750,step_4000,step_4250,step_4500,step_4750,step_5000,step_5250,step_5500,step_5750,step_6000,step_6250,step_6500,step_6750,step_7000,step_7250,step_7500,step_7750,step_8000,step_8250,step_8500,step_8750,step_9000,step_9250,step_9500,step_9750,step_10000 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b/results/csv/midtier_q_recovery_20260325_0738.csv new file mode 100644 index 0000000000000000000000000000000000000000..02e6a12abd28dcbd8a46af8189974d78bd6203cc --- /dev/null +++ b/results/csv/midtier_q_recovery_20260325_0738.csv @@ -0,0 +1,4 @@ +seed,status,best_val,best_step,final_val,stable,runtime_sec,error +42,ok,4.5733,10000,4.5733,True,~3600, +1337,ok,4.6151,10000,4.6151,True,3839.27, +123,ok,4.6143,10000,4.6143,True,3821.04, diff --git a/results/csv/midtierq_warmup3000_followup_runs_20260317_0943.csv b/results/csv/midtierq_warmup3000_followup_runs_20260317_0943.csv new file mode 100644 index 0000000000000000000000000000000000000000..1f4575f6482c47ff5036e85c18f831a16a56a10f --- /dev/null +++ b/results/csv/midtierq_warmup3000_followup_runs_20260317_0943.csv @@ -0,0 +1,3 @@ +variant_long,variant_short,seed,warmup,best_val,final_val,status,out_dir +dynamic_qconditioned_mlp128,midtier_q,1337,3000,4.5276,4.5276,ok,out-openwebtext_dynamic_qconditioned_mlp128_warmup3000_seed1337 +dynamic_qconditioned_mlp128,midtier_q,123,3000,4.5131,4.5131,ok,out-openwebtext_dynamic_qconditioned_mlp128_warmup3000_seed123 diff --git a/results/csv/midtierq_warmup3000_followup_summary_20260317_0943.csv b/results/csv/midtierq_warmup3000_followup_summary_20260317_0943.csv new file mode 100644 index 0000000000000000000000000000000000000000..f676bac5f04bcd9e920cd5194c0460ce62d6e246 --- /dev/null +++ b/results/csv/midtierq_warmup3000_followup_summary_20260317_0943.csv @@ -0,0 +1,3 @@ +variant_short,seed,warmup,best_val,final_val,delta_vs_200_anchor,delta_vs_500_anchor,delta_vs_2000_anchor,delta_vs_3000_seed42 +midtier_q,1337,3000,4.5276,4.5276,-0.3540000000000001,-0.2837000000000005,-0.013500000000000512,0.006799999999999251 +midtier_q,123,3000,4.5131,4.5131,-0.36850000000000005,-0.29820000000000046,-0.02800000000000047,-0.007700000000000706 diff --git a/results/csv/midtierq_warmup3000_followup_trajectories_20260317_0943.csv b/results/csv/midtierq_warmup3000_followup_trajectories_20260317_0943.csv new file mode 100644 index 0000000000000000000000000000000000000000..6fabbb7d08f60a0786cdb792ffbc9d265ca5a05d --- /dev/null +++ b/results/csv/midtierq_warmup3000_followup_trajectories_20260317_0943.csv @@ -0,0 +1,83 @@ +variant_long,variant_short,seed,warmup,step,val_loss +dynamic_qconditioned_mlp128,midtier_q,1337,3000,0,10.983 +dynamic_qconditioned_mlp128,midtier_q,1337,3000,250,7.1179 +dynamic_qconditioned_mlp128,midtier_q,1337,3000,500,6.6854 +dynamic_qconditioned_mlp128,midtier_q,1337,3000,750,6.4153 +dynamic_qconditioned_mlp128,midtier_q,1337,3000,1000,6.2912 +dynamic_qconditioned_mlp128,midtier_q,1337,3000,1250,6.1829 +dynamic_qconditioned_mlp128,midtier_q,1337,3000,1500,6.0514 +dynamic_qconditioned_mlp128,midtier_q,1337,3000,1750,5.9405 +dynamic_qconditioned_mlp128,midtier_q,1337,3000,2000,5.8013 +dynamic_qconditioned_mlp128,midtier_q,1337,3000,2250,5.7267 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+dynamic_qconditioned_mlp128,midtier_q,123,3000,8250,4.6308 +dynamic_qconditioned_mlp128,midtier_q,123,3000,8500,4.6086 +dynamic_qconditioned_mlp128,midtier_q,123,3000,8750,4.5788 +dynamic_qconditioned_mlp128,midtier_q,123,3000,9000,4.5681 +dynamic_qconditioned_mlp128,midtier_q,123,3000,9250,4.572 +dynamic_qconditioned_mlp128,midtier_q,123,3000,9500,4.5582 +dynamic_qconditioned_mlp128,midtier_q,123,3000,9750,4.5369 +dynamic_qconditioned_mlp128,midtier_q,123,3000,10000,4.5131 diff --git a/results/csv/midtierq_warmup_probe_runs_20260316_2327.csv b/results/csv/midtierq_warmup_probe_runs_20260316_2327.csv new file mode 100644 index 0000000000000000000000000000000000000000..5802ba8bd30771258100cd9f53015849b9324ae5 --- /dev/null +++ b/results/csv/midtierq_warmup_probe_runs_20260316_2327.csv @@ -0,0 +1,3 @@ +variant_long,variant_short,seed,warmup,best_val,final_val,status,out_dir +dynamic_qconditioned_mlp128,midtier_q,42,1000,4.6618,4.6618,ok,out-openwebtext_dynamic_qconditioned_mlp128_warmup1000_seed42 +dynamic_qconditioned_mlp128,midtier_q,42,3000,4.5208,4.5208,ok,out-openwebtext_dynamic_qconditioned_mlp128_warmup3000_seed42 diff --git a/results/csv/midtierq_warmup_probe_summary_20260316_2327.csv b/results/csv/midtierq_warmup_probe_summary_20260316_2327.csv new file mode 100644 index 0000000000000000000000000000000000000000..1bfb9e3b1c6391d27e8f46f1a29166365d0a9562 --- /dev/null +++ b/results/csv/midtierq_warmup_probe_summary_20260316_2327.csv @@ -0,0 +1,3 @@ +variant_short,seed,warmup,best_val,final_val,delta_vs_200_anchor,delta_vs_500_anchor,delta_vs_2000_anchor +midtier_q,42,1000,4.6618,4.6618,-0.21979999999999933,-0.14949999999999974,0.12070000000000025 +midtier_q,42,3000,4.5208,4.5208,-0.36079999999999934,-0.29049999999999976,-0.020299999999999763 diff --git a/results/csv/midtierq_warmup_probe_trajectories_20260316_2327.csv b/results/csv/midtierq_warmup_probe_trajectories_20260316_2327.csv new file mode 100644 index 0000000000000000000000000000000000000000..a2b0a3007e274f3dd810ed784513cf4d0b9ff4db --- /dev/null +++ b/results/csv/midtierq_warmup_probe_trajectories_20260316_2327.csv @@ -0,0 +1,83 @@ +variant_long,variant_short,seed,warmup,step,val_loss +dynamic_qconditioned_mlp128,midtier_q,42,1000,0,10.9324 +dynamic_qconditioned_mlp128,midtier_q,42,1000,250,6.8373 +dynamic_qconditioned_mlp128,midtier_q,42,1000,500,6.4826 +dynamic_qconditioned_mlp128,midtier_q,42,1000,750,6.3433 +dynamic_qconditioned_mlp128,midtier_q,42,1000,1000,6.2413 +dynamic_qconditioned_mlp128,midtier_q,42,1000,1250,6.0841 +dynamic_qconditioned_mlp128,midtier_q,42,1000,1500,5.9719 +dynamic_qconditioned_mlp128,midtier_q,42,1000,1750,5.8837 +dynamic_qconditioned_mlp128,midtier_q,42,1000,2000,5.8104 +dynamic_qconditioned_mlp128,midtier_q,42,1000,2250,5.7236 +dynamic_qconditioned_mlp128,midtier_q,42,1000,2500,5.6767 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+qvnorm_seed42,10.9763,6.7513,6.4533,6.3268,6.2005,6.1087,6.0574,5.9927,5.9407,5.8459,5.7725,5.7041,5.6293,5.5775,5.5723,5.5107,5.4561,5.4125,5.3904,5.3848,5.3315 +qvnorm_seed123,10.8957,6.6741,6.5069,6.3107,6.2341,6.1215,6.0384,6.0155,5.9427,5.8586,5.8004,5.7285,5.6849,5.598,5.589,5.5121,5.5007,5.4703,5.4128,5.4085,5.3583 diff --git a/results/csv/p2_rerun_runs_20260323_1714.csv b/results/csv/p2_rerun_runs_20260323_1714.csv new file mode 100644 index 0000000000000000000000000000000000000000..17c15c1c440460401849ef3772ecdff3185ae63b --- /dev/null +++ b/results/csv/p2_rerun_runs_20260323_1714.csv @@ -0,0 +1,10 @@ +variant,short_name,warmup,seed,status,runtime_sec,params_m,best_val,best_step,final_val,stable,out_dir,error +static_gate_prehead,static_prehead,2000,42,ok,3670.55,123.6,4.455,9750,4.4699,False,out-openwebtext_rerun_shared, +static_gate_prehead,static_prehead,2000,1337,ok,3687.97,123.6,4.434,9750,4.4742,False,out-openwebtext_rerun_shared, +static_gate_prehead,static_prehead,2000,123,ok,3687.0,123.6,4.4559,9500,4.4777,False,out-openwebtext_rerun_shared, +dynamic_xconditioned_g1,full_x,2000,42,ok,3860.25,130.67,4.3897,10000,4.3897,True,out-openwebtext_rerun_shared, +dynamic_xconditioned_g1,full_x,2000,1337,ok,3873.53,130.67,4.3842,10000,4.3842,True,out-openwebtext_rerun_shared, +dynamic_xconditioned_g1,full_x,2000,123,failed,15.82,130.67,,,,False,out-openwebtext_rerun_shared,exit code 3221226505 +dynamic_x_g1_headshared_elementwise,x_headshared,2000,42,ok,3733.58,123.64,4.4204,10000,4.4204,True,out-openwebtext_rerun_shared, +dynamic_x_g1_headshared_elementwise,x_headshared,2000,1337,ok,3728.6,123.64,4.4468,10000,4.4468,True,out-openwebtext_rerun_shared, +dynamic_x_g1_headshared_elementwise,x_headshared,2000,123,ok,3738.98,123.64,4.4367,10000,4.4367,True,out-openwebtext_rerun_shared, diff --git a/results/csv/phase2_coded_completeness_runs_20260325_1113.csv b/results/csv/phase2_coded_completeness_runs_20260325_1113.csv new file mode 100644 index 0000000000000000000000000000000000000000..5013b5390183b3f56aed639c0d95acd40cd57493 --- /dev/null +++ b/results/csv/phase2_coded_completeness_runs_20260325_1113.csv @@ -0,0 +1,7 @@ +variant,short_name,warmup,seed,status,runtime_sec,params_m,best_val,best_step,final_val,stable,out_dir,error +dynamic_qconditioned_fullwidth,full_q,2000,42,ok,3871.13,130.67,4.4027,10000,4.4027,True,out-openwebtext_phase2_full_q, +dynamic_qconditioned_fullwidth,full_q,2000,1337,ok,3791.18,130.67,4.4099,10000,4.4099,True,out-openwebtext_phase2_full_q, +dynamic_qconditioned_fullwidth,full_q,2000,123,ok,3786.03,130.67,4.4202,9500,4.4233,False,out-openwebtext_phase2_full_q, +dynamic_x_g1_headspecific_elementwise,x_headspec_elem,2000,42,ok,3724.01,124.18,4.4638,10000,4.4638,True,out-openwebtext_phase2_x_headspec_elem, +dynamic_x_g1_headspecific_elementwise,x_headspec_elem,2000,1337,ok,3718.84,124.18,4.4466,9750,4.4627,False,out-openwebtext_phase2_x_headspec_elem, +dynamic_x_g1_headspecific_elementwise,x_headspec_elem,2000,123,ok,3736.05,124.18,4.4247,9750,4.4393,False,out-openwebtext_phase2_x_headspec_elem, diff --git a/results/csv/phase3_bilinear_diag_runs_20260325_2226.csv b/results/csv/phase3_bilinear_diag_runs_20260325_2226.csv new file mode 100644 index 0000000000000000000000000000000000000000..a6c9b756b7ca20065d1bc4adbe5e3913836a506f --- /dev/null +++ b/results/csv/phase3_bilinear_diag_runs_20260325_2226.csv @@ -0,0 +1,4 @@ +variant,short_name,warmup,seed,status,runtime_sec,params_m,best_val,best_step,final_val,stable,out_dir,error +dynamic_qa_bilinear_diag,bilinear_diag,2000,42,ok,3883.85,123.59,4.4624,9750,4.4745,False,out-openwebtext_phase3_bilinear_diag, +dynamic_qa_bilinear_diag,bilinear_diag,2000,1337,ok,3837.97,123.59,4.4329,9750,4.4729,False,out-openwebtext_phase3_bilinear_diag, +dynamic_qa_bilinear_diag,bilinear_diag,2000,123,ok,3831.0,123.59,4.4596,9500,4.4824,False,out-openwebtext_phase3_bilinear_diag, diff --git a/results/csv/phase4_chartfill_runs_20260326_0730.csv b/results/csv/phase4_chartfill_runs_20260326_0730.csv new file mode 100644 index 0000000000000000000000000000000000000000..8ea0853c1d0694a01eeffad6013bba7d96266da4 --- /dev/null +++ b/results/csv/phase4_chartfill_runs_20260326_0730.csv @@ -0,0 +1,10 @@ +variant,short_name,warmup,seed,status,runtime_sec,params_m,best_val,best_step,final_val,stable,out_dir,error +dynamic_qconditioned_fullwidth_headspecific,q_full_headspec,2000,42,ok,3811.02,130.67,4.4151,10000,4.4151,True,out-openwebtext_phase4_q_full_headspec, +dynamic_qconditioned_fullwidth_headspecific,q_full_headspec,2000,1337,ok,3797.53,130.67,4.4058,9750,4.4101,True,out-openwebtext_phase4_q_full_headspec, +dynamic_qconditioned_fullwidth_headspecific,q_full_headspec,2000,123,ok,3798.11,130.67,4.3964,9750,4.4366,True,out-openwebtext_phase4_q_full_headspec, +dynamic_xconditioned_fullwidth_headspecific,x_full_headspec,2000,42,ok,3798.84,130.67,4.4013,10000,4.4013,True,out-openwebtext_phase4_x_full_headspec, +dynamic_xconditioned_fullwidth_headspecific,x_full_headspec,2000,1337,ok,3801.74,130.67,4.4011,9750,4.4048,True,out-openwebtext_phase4_x_full_headspec, +dynamic_xconditioned_fullwidth_headspecific,x_full_headspec,2000,123,ok,3796.39,130.67,4.3867,9750,4.4239,True,out-openwebtext_phase4_x_full_headspec, +dynamic_qa_conditioned_headspecific,qa_headspec,2000,42,ok,3757.74,124.77,4.4004,9750,4.4005,True,out-openwebtext_phase4_qa_headspec, +dynamic_qa_conditioned_headspecific,qa_headspec,2000,1337,ok,3767.28,124.77,4.3945,9750,4.4295,True,out-openwebtext_phase4_qa_headspec, +dynamic_qa_conditioned_headspecific,qa_headspec,2000,123,ok,3771.44,124.77,4.3904,10000,4.3904,True,out-openwebtext_phase4_qa_headspec, diff --git a/results/csv/phase5_warmup500_toptiers_runs_20260326_1722.csv b/results/csv/phase5_warmup500_toptiers_runs_20260326_1722.csv new file mode 100644 index 0000000000000000000000000000000000000000..bfe86d5359ce952012cf62b92ce9bc37e7c464e5 --- /dev/null +++ b/results/csv/phase5_warmup500_toptiers_runs_20260326_1722.csv @@ -0,0 +1,16 @@ +variant,short_name,warmup,seed,status,runtime_sec,params_m,best_val,best_step,final_val,stable,out_dir,error +dynamic_qa_conditioned_headspecific,qa_headspec,500,42,ok,3802.76,124.77,4.7306,10000,4.7306,False,out-openwebtext_phase5_w500_qa_headspec, +dynamic_qa_conditioned_headspecific,qa_headspec,500,1337,ok,3828.41,124.77,4.7612,9750,4.7981,False,out-openwebtext_phase5_w500_qa_headspec, +dynamic_qa_conditioned_headspecific,qa_headspec,500,123,ok,3830.43,124.77,4.6931,10000,4.6931,False,out-openwebtext_phase5_w500_qa_headspec, +dynamic_xconditioned_fullwidth_headspecific,x_full_headspec,500,42,ok,3899.73,130.67,4.8063,10000,4.8063,False,out-openwebtext_phase5_w500_x_full_headspec, +dynamic_xconditioned_fullwidth_headspecific,x_full_headspec,500,1337,ok,3871.37,130.67,4.8814,9750,4.9124,False,out-openwebtext_phase5_w500_x_full_headspec, +dynamic_xconditioned_fullwidth_headspecific,x_full_headspec,500,123,ok,3860.64,130.67,4.8183,9750,4.8416,False,out-openwebtext_phase5_w500_x_full_headspec, +dynamic_qa_conditioned_mlp128,mlp128_qa,500,42,ok,3902.14,123.88,4.6928,10000,4.6928,False,out-openwebtext_phase5_w500_mlp128_qa, +dynamic_qa_conditioned_mlp128,mlp128_qa,500,1337,ok,3905.2,123.88,4.6088,9750,4.6308,False,out-openwebtext_phase5_w500_mlp128_qa, +dynamic_qa_conditioned_mlp128,mlp128_qa,500,123,ok,3904.53,123.88,4.598,9750,4.6129,False,out-openwebtext_phase5_w500_mlp128_qa, +dynamic_qconditioned_fullwidth_headspecific,q_full_headspec,500,42,ok,3834.99,130.67,4.7184,10000,4.7184,False,out-openwebtext_phase5_w500_q_full_headspec, +dynamic_qconditioned_fullwidth_headspecific,q_full_headspec,500,1337,ok,3840.03,130.67,4.7927,9750,4.8213,False,out-openwebtext_phase5_w500_q_full_headspec, +dynamic_qconditioned_fullwidth_headspecific,q_full_headspec,500,123,ok,3839.74,130.67,4.6379,9750,4.6678,False,out-openwebtext_phase5_w500_q_full_headspec, +dynamic_qconditioned_fullwidth,full_q,500,42,ok,3853.37,130.67,4.725,10000,4.725,False,out-openwebtext_phase5_w500_full_q, +dynamic_qconditioned_fullwidth,full_q,500,1337,ok,3826.56,130.67,4.7695,10000,4.7695,False,out-openwebtext_phase5_w500_full_q, +dynamic_qconditioned_fullwidth,full_q,500,123,ok,3789.39,130.67,4.7423,9500,4.7474,False,out-openwebtext_phase5_w500_full_q, diff --git a/results/csv/phase6a_grok_qafollowups_w2000_runs_20260327_1722.csv b/results/csv/phase6a_grok_qafollowups_w2000_runs_20260327_1722.csv new file mode 100644 index 0000000000000000000000000000000000000000..0663432fdfc44422fe7a39b2e157fa4693a5cb4c --- /dev/null +++ b/results/csv/phase6a_grok_qafollowups_w2000_runs_20260327_1722.csv @@ -0,0 +1,7 @@ +variant,short_name,warmup,seed,status,runtime_sec,params_m,best_val,best_step,final_val,stable,out_dir,error +dynamic_qa_conditioned_normed,qa_normed,2000,42,ok,4614.38,123.69,4.3882,10000,4.3882,False,out-openwebtext_phase6_w2000_qa_normed, +dynamic_qa_conditioned_normed,qa_normed,2000,1337,ok,4624.65,123.69,4.3881,9750,4.4334,False,out-openwebtext_phase6_w2000_qa_normed, +dynamic_qa_conditioned_normed,qa_normed,2000,123,ok,4569.71,123.69,4.4016,9750,4.4111,False,out-openwebtext_phase6_w2000_qa_normed, +dynamic_qa_conditioned_lowrank16,qa_lowrank,2000,42,ok,3849.29,123.62,4.411,9750,4.4223,False,out-openwebtext_phase6_w2000_qa_lowrank16, +dynamic_qa_conditioned_lowrank16,qa_lowrank,2000,1337,ok,3855.14,123.62,4.4024,9750,4.4145,False,out-openwebtext_phase6_w2000_qa_lowrank16, +dynamic_qa_conditioned_lowrank16,qa_lowrank,2000,123,ok,3832.51,123.62,4.4069,10000,4.4069,False,out-openwebtext_phase6_w2000_qa_lowrank16, diff --git a/results/csv/phase6b_grok_qafollowups_w500_runs_20260328_0028.csv b/results/csv/phase6b_grok_qafollowups_w500_runs_20260328_0028.csv new file mode 100644 index 0000000000000000000000000000000000000000..5cd1adfd0b530874d19d2c7edda04d883d0d99a1 --- /dev/null +++ b/results/csv/phase6b_grok_qafollowups_w500_runs_20260328_0028.csv @@ -0,0 +1,7 @@ +variant,short_name,warmup,seed,status,runtime_sec,params_m,best_val,best_step,final_val,stable,out_dir,error +dynamic_qa_conditioned_normed,qa_normed,500,42,ok,4476.69,123.69,4.588,10000,4.588,False,out-openwebtext_phase6_w500_qa_normed, +dynamic_qa_conditioned_normed,qa_normed,500,1337,ok,4456.23,123.69,4.6118,9750,4.6508,False,out-openwebtext_phase6_w500_qa_normed, +dynamic_qa_conditioned_normed,qa_normed,500,123,ok,4460.28,123.69,4.5709,9750,4.5731,False,out-openwebtext_phase6_w500_qa_normed, +dynamic_qa_conditioned_lowrank16,qa_lowrank,500,42,ok,3813.82,123.62,4.6426,10000,4.6426,False,out-openwebtext_phase6_w500_qa_lowrank16, +dynamic_qa_conditioned_lowrank16,qa_lowrank,500,1337,ok,3837.27,123.62,4.6158,9750,4.6289,False,out-openwebtext_phase6_w500_qa_lowrank16, +dynamic_qa_conditioned_lowrank16,qa_lowrank,500,123,ok,3823.12,123.62,4.6282,10000,4.6282,False,out-openwebtext_phase6_w500_qa_lowrank16, diff --git a/results/csv/priority1_runs_20260322_1355.csv b/results/csv/priority1_runs_20260322_1355.csv new file mode 100644 index 0000000000000000000000000000000000000000..8724a665ac938ce65b9bd7569e201925b7c83576 --- /dev/null +++ b/results/csv/priority1_runs_20260322_1355.csv @@ -0,0 +1,13 @@ +variant,short_name,warmup,seed,status,runtime_sec,params_m,best_val,best_step,final_val,stable,out_dir,error +none,baseline,2000,42,ok,3635.68,123.59,4.4728,9750,4.4861,False,out-openwebtext_none_warmup2000_seed42_p1, +none,baseline,2000,1337,ok,3590.16,123.59,4.4609,9750,4.5048,False,out-openwebtext_none_warmup2000_seed1337_p1, +none,baseline,2000,123,ok,3590.42,123.59,4.4786,9500,4.501,False,out-openwebtext_none_warmup2000_seed123_p1, +dynamic_qconditioned_mlp128,midtier_q,2000,42,ok,3896.93,123.78,4.4085,10000,4.4085,True,out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1, +dynamic_qconditioned_mlp128,midtier_q,2000,1337,ok,3887.93,123.78,4.4256,10000,4.4256,True,out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1, +dynamic_qconditioned_mlp128,midtier_q,2000,123,ok,3872.02,123.78,4.4065,10000,4.4065,True,out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1, +dynamic_qa_conditioned,cheap_qa,2000,42,ok,3833.38,123.69,4.383,10000,4.383,True,out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1, +dynamic_qa_conditioned,cheap_qa,2000,1337,ok,3813.97,123.69,4.3907,9750,4.4321,False,out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1, +dynamic_qa_conditioned,cheap_qa,2000,123,ok,3790.71,123.69,4.3953,9750,4.4047,False,out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1, +dynamic,cheap_q,2000,42,ok,3730.92,123.64,4.4069,10000,4.4069,True,out-openwebtext_dynamic_warmup2000_seed42_p1, +dynamic,cheap_q,2000,1337,ok,3720.43,123.64,4.4459,9250,4.4464,False,out-openwebtext_dynamic_warmup2000_seed1337_p1, +dynamic,cheap_q,2000,123,ok,3725.86,123.64,4.4066,10000,4.4066,True,out-openwebtext_dynamic_warmup2000_seed123_p1, diff --git a/results/csv/priority2_runs_20260323_0623.csv b/results/csv/priority2_runs_20260323_0623.csv new file mode 100644 index 0000000000000000000000000000000000000000..721b900c1b165287c82f2f4b3ea80f7402cb3916 --- /dev/null +++ b/results/csv/priority2_runs_20260323_0623.csv @@ -0,0 +1,16 @@ +variant,short_name,warmup,seed,status,runtime_sec,params_m,best_val,best_step,final_val,stable,out_dir,error +none,baseline_p2,2000,42,ok,3671.63,123.59,4.4705,9750,4.4839,False,out-openwebtext_none_warmup2000_seed42_p2, +none,baseline_p2,2000,1337,ok,3640.72,123.59,4.4591,9750,4.5028,False,out-openwebtext_none_warmup2000_seed1337_p2, +none,baseline_p2,2000,123,ok,3615.74,123.59,4.4768,9500,4.4972,False,out-openwebtext_none_warmup2000_seed123_p2, +dynamic_qa_conditioned_mlp128,mlp128_qa,2000,42,ok,3913.3,123.88,4.424,10000,4.424,True,out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2, +dynamic_qa_conditioned_mlp128,mlp128_qa,2000,1337,ok,3928.35,123.88,4.3976,9750,4.4163,False,out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2, +dynamic_qa_conditioned_mlp128,mlp128_qa,2000,123,ok,3909.65,123.88,4.3879,9750,4.4036,False,out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2, +static_gate_prehead,static_prehead,2000,42,ok,3750.43,123.6,4.4568,9750,4.471,False,out-openwebtext_static_gate_prehead_warmup2000_seed42_p2, +static_gate_prehead,static_prehead,2000,1337,failed,586.87,123.6,5.9137,1500,5.9137,False,out-openwebtext_static_gate_prehead_warmup2000_seed1337_p2,exit code 1 +static_gate_prehead,static_prehead,2000,123,failed,114.3,123.6,6.9069,250,6.9069,False,out-openwebtext_static_gate_prehead_warmup2000_seed123_p2,exit code 1 +dynamic_xconditioned_g1,full_x,2000,42,failed,119.3,130.67,6.9348,250,6.9348,False,out-openwebtext_dynamic_xconditioned_g1_warmup2000_seed42_p2,exit code 1 +dynamic_xconditioned_g1,full_x,2000,1337,failed,119.55,130.67,6.937,250,6.937,False,out-openwebtext_dynamic_xconditioned_g1_warmup2000_seed1337_p2,exit code 1 +dynamic_xconditioned_g1,full_x,2000,123,failed,119.47,130.67,6.9534,250,6.9534,False,out-openwebtext_dynamic_xconditioned_g1_warmup2000_seed123_p2,exit code 1 +dynamic_x_g1_headshared_elementwise,x_headshared,2000,42,failed,115.91,123.64,6.9409,250,6.9409,False,out-openwebtext_dynamic_x_g1_headshared_elementwise_warmup2000_seed42_p2,exit code 1 +dynamic_x_g1_headshared_elementwise,x_headshared,2000,1337,failed,116.86,123.64,6.9312,250,6.9312,False,out-openwebtext_dynamic_x_g1_headshared_elementwise_warmup2000_seed1337_p2,exit code 1 +dynamic_x_g1_headshared_elementwise,x_headshared,2000,123,failed,116.7,123.64,6.9769,250,6.9769,False,out-openwebtext_dynamic_x_g1_headshared_elementwise_warmup2000_seed123_p2,exit code 1 diff --git a/results/csv/qa_screen_results.csv b/results/csv/qa_screen_results.csv new file mode 100644 index 0000000000000000000000000000000000000000..92efe0b6ff1e777ba225312dc3a0383437678d3c --- /dev/null +++ b/results/csv/qa_screen_results.csv @@ -0,0 +1,11 @@ +variant,warmup,seed,status,runtime_sec,params_m,tokens_per_iter,best_val,best_step,final_val,final_step,out_dir,checkpoint_dir,log_path,error +dynamic_qa_conditioned,2000,42,ok,3871.08,123.69,"4,096",4.3807,10000,4.3807,10000,out-openwebtext_dynamic_qa_conditioned_wu2000_seed42,out-openwebtext_dynamic_qa_conditioned_wu2000_seed42,out-openwebtext_dynamic_qa_conditioned_wu2000_seed42\train.log, +dynamic_qa_conditioned_mlp128,2000,42,ok,3893.58,123.88,"4,096",4.4282,10000,4.4282,10000,out-openwebtext_dynamic_qa_conditioned_mlp128_wu2000_seed42,out-openwebtext_dynamic_qa_conditioned_mlp128_wu2000_seed42,out-openwebtext_dynamic_qa_conditioned_mlp128_wu2000_seed42\train.log, +dynamic_qconditioned_mlp192,2000,42,ok,3967.29,123.88,"4,096",4.4337,10000,4.4337,10000,out-openwebtext_dynamic_qconditioned_mlp192_wu2000_seed42,out-openwebtext_dynamic_qconditioned_mlp192_wu2000_seed42,out-openwebtext_dynamic_qconditioned_mlp192_wu2000_seed42\train.log, +dynamic_q_headshared_elementwise,2000,42,ok,3767.82,123.64,"4,096",4.4073,10000,4.4073,10000,out-openwebtext_dynamic_q_headshared_elementwise_wu2000_seed42,out-openwebtext_dynamic_q_headshared_elementwise_wu2000_seed42,out-openwebtext_dynamic_q_headshared_elementwise_wu2000_seed42\train.log, +dynamic_qa_headshared_elementwise,2000,42,ok,3782.7,123.69,"4,096",4.3844,10000,4.3844,10000,out-openwebtext_dynamic_qa_headshared_elementwise_wu2000_seed42,out-openwebtext_dynamic_qa_headshared_elementwise_wu2000_seed42,out-openwebtext_dynamic_qa_headshared_elementwise_wu2000_seed42\train.log, +dynamic_qa_conditioned,500,42,ok,3787.43,123.69,"4,096",4.6003,10000,4.6003,10000,out-openwebtext_dynamic_qa_conditioned_wu500_seed42,out-openwebtext_dynamic_qa_conditioned_wu500_seed42,out-openwebtext_dynamic_qa_conditioned_wu500_seed42\train.log, +dynamic_qa_conditioned_mlp128,500,42,ok,3919.42,123.88,"4,096",4.6761,10000,4.6761,10000,out-openwebtext_dynamic_qa_conditioned_mlp128_wu500_seed42,out-openwebtext_dynamic_qa_conditioned_mlp128_wu500_seed42,out-openwebtext_dynamic_qa_conditioned_mlp128_wu500_seed42\train.log, +dynamic_qconditioned_mlp192,500,42,ok,3922.5,123.88,"4,096",4.6548,9750,4.6548,10000,out-openwebtext_dynamic_qconditioned_mlp192_wu500_seed42,out-openwebtext_dynamic_qconditioned_mlp192_wu500_seed42,out-openwebtext_dynamic_qconditioned_mlp192_wu500_seed42\train.log, +dynamic_q_headshared_elementwise,500,42,ok,3768.24,123.64,"4,096",4.6539,10000,4.6539,10000,out-openwebtext_dynamic_q_headshared_elementwise_wu500_seed42,out-openwebtext_dynamic_q_headshared_elementwise_wu500_seed42,out-openwebtext_dynamic_q_headshared_elementwise_wu500_seed42\train.log, +dynamic_qa_headshared_elementwise,500,42,ok,3865.7,123.69,"4,096",4.617,10000,4.617,10000,out-openwebtext_dynamic_qa_headshared_elementwise_wu500_seed42,out-openwebtext_dynamic_qa_headshared_elementwise_wu500_seed42,out-openwebtext_dynamic_qa_headshared_elementwise_wu500_seed42\train.log, diff --git a/results/csv/qv_124m_3seed_results_20260310_0041.csv b/results/csv/qv_124m_3seed_results_20260310_0041.csv new file mode 100644 index 0000000000000000000000000000000000000000..112193d84c523b507a08f463635ce73c0b636066 --- /dev/null +++ b/results/csv/qv_124m_3seed_results_20260310_0041.csv @@ -0,0 +1 @@ +Variant,Seed,Final_Val_Loss diff --git a/results/csv/qv_124m_5000_results_20260310_0900.csv b/results/csv/qv_124m_5000_results_20260310_0900.csv new file mode 100644 index 0000000000000000000000000000000000000000..e7259698b289ca3bd19f49ed207cedbe603089a6 --- /dev/null +++ b/results/csv/qv_124m_5000_results_20260310_0900.csv @@ -0,0 +1,3 @@ +Variant,Final_Val_Loss,vs_Baseline +none,1.4982,+0.0000 +dynamic,1.4879,-0.0103 diff --git a/results/csv/qv_3seed_results_20260310_0036.csv b/results/csv/qv_3seed_results_20260310_0036.csv new file mode 100644 index 0000000000000000000000000000000000000000..db38e1d4be7ab8e11602521996402bae3469d7e3 --- /dev/null +++ b/results/csv/qv_3seed_results_20260310_0036.csv @@ -0,0 +1,7 @@ +Variant,Seed,Final_Val_Loss +none,1337,1.6881 +none,42,1.7212 +none,123,1.6995 +dynamic,1337,1.5365 +dynamic,42,1.5008 +dynamic,123,1.5318 diff --git a/results/csv/qv_long_results_20260309_2224.csv b/results/csv/qv_long_results_20260309_2224.csv new file mode 100644 index 0000000000000000000000000000000000000000..6aabdf00129fbaff4a1864b5d3e279ab1f5fd007 --- /dev/null +++ b/results/csv/qv_long_results_20260309_2224.csv @@ -0,0 +1,3 @@ +Variant,Final_Val_Loss,vs_Baseline +none,1.6785,+0.0000 +dynamic,1.5407,-0.1378 diff --git a/results/csv/qv_openwebtext_results_20260311_0830.csv b/results/csv/qv_openwebtext_results_20260311_0830.csv new file mode 100644 index 0000000000000000000000000000000000000000..29d83c4c21837dcb2f39888ce43c5032d1744b92 --- /dev/null +++ b/results/csv/qv_openwebtext_results_20260311_0830.csv @@ -0,0 +1,3 @@ +Variant,Final_Val_Loss,vs_Baseline +none,6.0341,+0.0000 +qvnorm,5.8979,-0.1362 diff --git a/results/csv/qv_results_20260309_2137.csv b/results/csv/qv_results_20260309_2137.csv new file mode 100644 index 0000000000000000000000000000000000000000..e32e5bc30074ee51a0601963d8ec2d3826301556 --- /dev/null +++ b/results/csv/qv_results_20260309_2137.csv @@ -0,0 +1,5 @@ +Variant,Final_Val_Loss,vs_Baseline +none,1.5276,+0.0000 +vnorm,1.5264,-0.0012 +qvnorm,1.5347,+0.0071 +dynamic,1.5118,-0.0158 diff --git a/results/csv/qv_swiglu_results_20260310_1806.csv b/results/csv/qv_swiglu_results_20260310_1806.csv new file mode 100644 index 0000000000000000000000000000000000000000..c1885f8de890fff7823ea075cb7e4de94cc43e1c --- /dev/null +++ b/results/csv/qv_swiglu_results_20260310_1806.csv @@ -0,0 +1,3 @@ +Variant,Final_Val_Loss,vs_Baseline +none,1.5027,+0.0000 +dynamic_swiglu,1.5162,+0.0135 diff --git a/results/csv/qv_wordlevel_results_20260310_1843.csv b/results/csv/qv_wordlevel_results_20260310_1843.csv new file mode 100644 index 0000000000000000000000000000000000000000..df187e59cf589b69f8c7664c8d78c87834ded6e3 --- /dev/null +++ b/results/csv/qv_wordlevel_results_20260310_1843.csv @@ -0,0 +1,3 @@ +Variant,Final_Val_Loss,vs_Baseline +none,7.3196,+0.0000 +dynamic,7.6070,+0.2874 diff --git a/results/csv/runner_a_runs_20260321_0903.csv b/results/csv/runner_a_runs_20260321_0903.csv new file mode 100644 index 0000000000000000000000000000000000000000..4131e79d7c4c72c7edd3a05f3c7b97a1c7ad1461 --- /dev/null +++ b/results/csv/runner_a_runs_20260321_0903.csv @@ -0,0 +1,7 @@ +variant,short_name,warmup,seed,status,runtime_sec,params_m,best_val,best_step,final_val,stable,out_dir,error +none,baseline,2000,42,ok,3596.2,123.59,4.4696,9750,4.483,False,out-openwebtext_none_warmup2000_seed42_runnerA, +none,baseline,2000,1337,ok,3583.79,123.59,4.4693,9750,4.5116,False,out-openwebtext_none_warmup2000_seed1337_runnerA, +none,baseline,2000,123,ok,3584.53,123.59,4.4771,9500,4.4974,False,out-openwebtext_none_warmup2000_seed123_runnerA, +dynamic_ones_gate,ones_identity,2000,42,ok,3563.94,123.59,4.4715,9750,4.4859,False,out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA, +dynamic_ones_gate,ones_identity,2000,1337,ok,3557.13,123.59,4.464,9750,4.5081,False,out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA, +dynamic_ones_gate,ones_identity,2000,123,ok,3577.14,123.59,4.4761,9500,4.498,False,out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA, diff --git a/results/csv/runner_b_runs_20260321_1545.csv b/results/csv/runner_b_runs_20260321_1545.csv new file mode 100644 index 0000000000000000000000000000000000000000..3beb2d82e35eb65644ddf039bc61acc85440f513 --- /dev/null +++ b/results/csv/runner_b_runs_20260321_1545.csv @@ -0,0 +1,4 @@ +variant,variant_short,warmup,seed,status,runtime_sec,params_m,best_val,best_step,final_val,stable,out_dir,error +dynamic_a_conditioned,a_only,2000,42,ok,3722.22,123.64,4.418,10000,4.418,True,out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB, +dynamic_a_conditioned,a_only,2000,1337,ok,3702.43,123.64,4.4404,9250,4.4413,False,out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB, +dynamic_a_conditioned,a_only,2000,123,ok,3723.81,123.64,4.43,10000,4.43,True,out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB, diff --git a/results/csv/warmup200_runs_20260316_0349.csv b/results/csv/warmup200_runs_20260316_0349.csv new file mode 100644 index 0000000000000000000000000000000000000000..5cd5334708e47694b528b3b479d1ab93cb9d7af6 --- /dev/null +++ b/results/csv/warmup200_runs_20260316_0349.csv @@ -0,0 +1,13 @@ +variant_long,variant_short,warmup,seed,best_val,final_val,status,out_dir +none,baseline,200,1337,4.9031,4.9463,ok,out-openwebtext_none_warmup200_seed1337 +none,baseline,200,42,4.8934,4.9025,ok,out-openwebtext_none_warmup200_seed42 +none,baseline,200,123,4.9233,4.9422,ok,out-openwebtext_none_warmup200_seed123 +dynamic,cheap_q,200,1337,4.8981,4.9056,ok,out-openwebtext_dynamic_warmup200_seed1337 +dynamic,cheap_q,200,42,4.914,4.914,ok,out-openwebtext_dynamic_warmup200_seed42 +dynamic,cheap_q,200,123,4.9006,4.9027,ok,out-openwebtext_dynamic_warmup200_seed123 +dynamic_qconditioned_mlp128,midtier_q,200,1337,4.9067,4.9067,ok,out-openwebtext_dynamic_qconditioned_mlp128_warmup200_seed1337 +dynamic_qconditioned_mlp128,midtier_q,200,42,4.8443,4.8443,ok,out-openwebtext_dynamic_qconditioned_mlp128_warmup200_seed42 +dynamic_qconditioned_mlp128,midtier_q,200,123,4.8939,4.8939,ok,out-openwebtext_dynamic_qconditioned_mlp128_warmup200_seed123 +dynamic_xconditioned_g1,full_x,200,1337,5.2466,5.2466,ok,out-openwebtext_dynamic_xconditioned_g1_warmup200_seed1337 +dynamic_xconditioned_g1,full_x,200,42,5.0603,5.0603,ok,out-openwebtext_dynamic_xconditioned_g1_warmup200_seed42 +dynamic_xconditioned_g1,full_x,200,123,5.1435,5.1567,ok,out-openwebtext_dynamic_xconditioned_g1_warmup200_seed123 diff --git a/results/csv/warmup200_summary_20260316_0349.csv b/results/csv/warmup200_summary_20260316_0349.csv new file mode 100644 index 0000000000000000000000000000000000000000..6b6319d2f196cef51a11fee3ac4e55949ac21911 --- /dev/null +++ b/results/csv/warmup200_summary_20260316_0349.csv @@ -0,0 +1,5 @@ +variant_short,warmup,n,mean_final,std_final,mean_plus_std,mean_plus_half_std,delta_vs_20pct_anchor +baseline,200,3,4.9303333333333335,0.019752186939397075,4.95008552027273,4.940209426803032,0.2562333333333333 +cheap_q,200,3,4.9074333333333335,0.004791891299082269,4.912225224632416,4.909829278982874,0.3541333333333334 +midtier_q,200,3,4.881633333333333,0.02691088173129163,4.908544215064625,4.895088774198979,0.34053333333333313 +full_x,200,3,5.154533333333333,0.07607208569654332,5.230605419029876,5.192569376181605,0.6171333333333333 diff --git a/results/csv/warmup500_bundle_runs_20260324_1937.csv b/results/csv/warmup500_bundle_runs_20260324_1937.csv new file mode 100644 index 0000000000000000000000000000000000000000..cc8d8519786a6b5696dc26b96073f910ab45f6fd --- /dev/null +++ b/results/csv/warmup500_bundle_runs_20260324_1937.csv @@ -0,0 +1,10 @@ +variant,short_name,warmup,seed,status,runtime_sec,params_m,best_val,best_step,final_val,stable,out_dir,error +none,baseline,500,42,ok,3598.51,123.59,4.5773,9750,4.591,False,out-openwebtext_warmup500_shared, +none,baseline,500,1337,ok,3602.14,123.59,4.5604,9750,4.6033,False,out-openwebtext_warmup500_shared, +none,baseline,500,123,ok,3594.18,123.59,4.5611,9500,4.5797,False,out-openwebtext_warmup500_shared, +dynamic_qa_conditioned,cheap_qa,500,42,ok,3799.93,123.69,4.5797,10000,4.5797,True,out-openwebtext_warmup500_shared, +dynamic_qa_conditioned,cheap_qa,500,1337,ok,3853.65,123.69,4.6741,9750,4.7138,False,out-openwebtext_warmup500_shared, +dynamic_qa_conditioned,cheap_qa,500,123,ok,3800.5,123.69,4.5851,9750,4.5875,False,out-openwebtext_warmup500_shared, +dynamic_xconditioned_g1,full_x,500,42,ok,3896.27,130.67,4.7963,10000,4.7963,True,out-openwebtext_warmup500_shared, +dynamic_xconditioned_g1,full_x,500,1337,ok,3875.47,130.67,4.8156,10000,4.8156,True,out-openwebtext_warmup500_shared, +dynamic_xconditioned_g1,full_x,500,123,ok,3884.94,130.67,4.9434,9500,4.95,False,out-openwebtext_warmup500_shared, diff --git a/results/csv/warmup500_runs_20260316_2028.csv b/results/csv/warmup500_runs_20260316_2028.csv new file mode 100644 index 0000000000000000000000000000000000000000..d1041343ea6ce9c0b3389e3a31d31cebb85e90fa --- /dev/null +++ b/results/csv/warmup500_runs_20260316_2028.csv @@ -0,0 +1,13 @@ +variant_long,variant_short,warmup,seed,best_val,final_val,status,out_dir +none,baseline,500,1337,4.8339,4.8813,ok,out-openwebtext_none_warmup500_seed1337 +none,baseline,500,42,4.8431,4.8504,ok,out-openwebtext_none_warmup500_seed42 +none,baseline,500,123,4.8021,4.8201,ok,out-openwebtext_none_warmup500_seed123 +dynamic,cheap_q,500,1337,4.841,4.849,ok,out-openwebtext_dynamic_warmup500_seed1337 +dynamic,cheap_q,500,42,4.8038,4.8038,ok,out-openwebtext_dynamic_warmup500_seed42 +dynamic,cheap_q,500,123,4.8596,4.8596,ok,out-openwebtext_dynamic_warmup500_seed123 +dynamic_qconditioned_mlp128,midtier_q,500,1337,4.8096,4.8096,ok,out-openwebtext_dynamic_qconditioned_mlp128_warmup500_seed1337 +dynamic_qconditioned_mlp128,midtier_q,500,42,4.7941,4.7941,ok,out-openwebtext_dynamic_qconditioned_mlp128_warmup500_seed42 +dynamic_qconditioned_mlp128,midtier_q,500,123,4.8302,4.8302,ok,out-openwebtext_dynamic_qconditioned_mlp128_warmup500_seed123 +dynamic_xconditioned_g1,full_x,500,1337,4.8815,4.8815,ok,out-openwebtext_dynamic_xconditioned_g1_warmup500_seed1337 +dynamic_xconditioned_g1,full_x,500,42,5.011,5.011,ok,out-openwebtext_dynamic_xconditioned_g1_warmup500_seed42 +dynamic_xconditioned_g1,full_x,500,123,4.9844,4.9937,ok,out-openwebtext_dynamic_xconditioned_g1_warmup500_seed123 diff --git a/results/csv/warmup500_summary_20260316_2028.csv b/results/csv/warmup500_summary_20260316_2028.csv new file mode 100644 index 0000000000000000000000000000000000000000..6b2b9766af61e3c8c0865912bfd0a7966c230862 --- /dev/null +++ b/results/csv/warmup500_summary_20260316_2028.csv @@ -0,0 +1,5 @@ +variant_short,warmup,n,mean_final,std_final,mean_plus_std,mean_plus_half_std,delta_vs_20pct_anchor,delta_vs_2pct_anchor +baseline,500,3,4.8506,0.024985195616604797,4.875585195616605,4.863092597808302,0.17649999999999988,-0.07969999999999988 +cheap_q,500,3,4.837466666666667,0.024196051101138365,4.861662717767805,4.849564692217236,0.2841666666666667,-0.06993333333333318 +midtier_q,500,3,4.8113,0.014786705740855846,4.826086705740856,4.818693352870428,0.2702,-0.07029999999999959 +full_x,500,3,4.962066666666666,0.05740536173177165,5.019472028398438,4.990769347532551,0.4246666666666661,-0.19243333333333368 diff --git a/results/csv/warmup500_trajectories_20260316_2028.csv b/results/csv/warmup500_trajectories_20260316_2028.csv new file mode 100644 index 0000000000000000000000000000000000000000..3a7d6fca49dbe6b401134b6c62b7e680fe6a04ac --- /dev/null +++ b/results/csv/warmup500_trajectories_20260316_2028.csv @@ -0,0 +1,493 @@ +variant_long,variant_short,warmup,seed,step,val_loss +none,baseline,500,1337,0,10.9985 +none,baseline,500,1337,250,6.754 +none,baseline,500,1337,500,6.5251 +none,baseline,500,1337,750,6.2936 +none,baseline,500,1337,1000,6.1464 +none,baseline,500,1337,1250,6.0761 +none,baseline,500,1337,1500,5.9507 +none,baseline,500,1337,1750,5.9315 +none,baseline,500,1337,2000,5.8543 +none,baseline,500,1337,2250,5.7938 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+variant,variant_short,warmup,seed,best_val,final_val,status,out_dir +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,1337,4.8414,4.851,ok,out-openwebtext_dynamic_x_g1_headshared_elementwise_warmup500_seed1337 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,123,4.8387,4.8387,ok,out-openwebtext_dynamic_x_g1_headshared_elementwise_warmup500_seed123 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,2000,1337,4.5927,4.5942,ok,out-openwebtext_dynamic_x_g1_headshared_elementwise_warmup2000_seed1337 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,2000,123,4.5948,4.5948,ok,out-openwebtext_dynamic_x_g1_headshared_elementwise_warmup2000_seed123 diff --git a/results/csv/x_headshared_elementwise_summary_20260319_1209.csv b/results/csv/x_headshared_elementwise_summary_20260319_1209.csv new file mode 100644 index 0000000000000000000000000000000000000000..febe7389f508edbe096e2b0afb6bade626069186 --- /dev/null +++ b/results/csv/x_headshared_elementwise_summary_20260319_1209.csv @@ -0,0 +1,3 @@ +variant,variant_short,warmup,seed42,seed1337,seed123,mean_val,std_val,delta_vs_global_x_agg,delta_vs_midtier_q_agg,delta_vs_full_q_agg,delta_vs_full_q_3000_agg +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,500,4.8102,4.851,4.8387,4.8333,0.01708859268635074,-0.12880000000000003,0.02200000000000024,-0.006199999999999761, +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,2000,4.5698,4.5942,4.5948,4.586266666666667,0.011646267880980474,0.04886666666666706,0.0451666666666668,0.044966666666667265,0.06886666666666663 diff --git a/results/csv/x_headshared_elementwise_trajectories_20260319_1209.csv b/results/csv/x_headshared_elementwise_trajectories_20260319_1209.csv new file mode 100644 index 0000000000000000000000000000000000000000..1163027924aa83ef3467d914bf28a8febef81dca --- /dev/null +++ b/results/csv/x_headshared_elementwise_trajectories_20260319_1209.csv @@ -0,0 +1,165 @@ 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+dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,2000,123,4750,5.1529,5.167 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,2000,123,5000,5.1287,5.1057 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,2000,123,5250,5.0803,5.075 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,2000,123,5500,5.0488,5.0591 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,2000,123,5750,5.005,5.0002 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,2000,123,6000,4.9471,4.9548 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,2000,123,6250,4.9307,4.9064 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,2000,123,6500,4.9129,4.9133 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,2000,123,6750,4.8162,4.8904 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,2000,123,7000,4.8109,4.8541 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,2000,123,7250,4.8207,4.8204 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,2000,123,7500,4.7529,4.7598 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,2000,123,7750,4.7337,4.7465 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,2000,123,8000,4.6966,4.7093 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,2000,123,8250,4.7039,4.6973 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,2000,123,8500,4.6579,4.6465 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,2000,123,8750,4.6479,4.6682 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,2000,123,9000,4.6475,4.6497 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,2000,123,9250,4.6524,4.6065 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,2000,123,9500,4.6267,4.6024 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,2000,123,9750,4.6068,4.6262 +dynamic_x_g1_headshared_elementwise,x_headshared_elementwise,2000,123,10000,4.6023,4.5948 diff --git a/results/figures/phase19b_qa_softqa_pd_w2000_6seeds_summary_20260403_0711_heatmaps.png b/results/figures/phase19b_qa_softqa_pd_w2000_6seeds_summary_20260403_0711_heatmaps.png new file mode 100644 index 0000000000000000000000000000000000000000..4d2947393e8e0143b42cbe3b630b09435f6c7670 --- /dev/null +++ b/results/figures/phase19b_qa_softqa_pd_w2000_6seeds_summary_20260403_0711_heatmaps.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:11bd12701108fc9e00bc451ca38041f6ed9fe8ae8091c5399131b5563000b0fd +size 328045 diff --git a/results/logs/command prompt summaru end 3-24-26.txt b/results/logs/command prompt summaru end 3-24-26.txt new file mode 100644 index 0000000000000000000000000000000000000000..5a2accb9667186d8318f87e8e1cccdbfc37f3998 --- /dev/null +++ b/results/logs/command prompt summaru end 3-24-26.txt @@ -0,0 +1,8389 @@ +000_seed123 +iter 4500: loss 4.9233, time 20762.85ms, mfu 0.05% +step 4750: train loss 5.0105, val loss 4.9932 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup3000_seed123 +iter 4750: loss 5.5305, time 20378.24ms, mfu 0.05% +step 5000: train loss 4.9713, val loss 4.9384 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup3000_seed123 +iter 5000: loss 4.6104, time 20365.21ms, mfu 0.05% +step 5250: train loss 4.9421, val loss 4.9442 +iter 5250: loss 4.4217, time 18752.14ms, mfu 0.05% +step 5500: train loss 4.8394, val loss 4.8827 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup3000_seed123 +iter 5500: loss 4.6309, time 20780.91ms, mfu 0.05% +step 5750: train loss 4.8181, val loss 4.8130 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup3000_seed123 +iter 5750: loss 4.9336, time 20563.22ms, mfu 0.05% +step 6000: train loss 4.7705, val loss 4.7884 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup3000_seed123 +iter 6000: loss 4.7605, time 20795.02ms, mfu 0.05% +step 6250: train loss 4.7207, val loss 4.6969 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup3000_seed123 +iter 6250: loss 4.8031, time 20730.51ms, mfu 0.05% +step 6500: train loss 4.6852, val loss 4.7159 +iter 6500: loss 5.1505, time 18798.28ms, mfu 0.05% +step 6750: train loss 4.6250, val loss 4.6337 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup3000_seed123 +iter 6750: loss 4.3293, time 20725.09ms, mfu 0.05% +step 7000: train loss 4.6392, val loss 4.6209 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup3000_seed123 +iter 7000: loss 4.8025, time 20494.98ms, mfu 0.05% +step 7250: train loss 4.5935, val loss 4.5934 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup3000_seed123 +iter 7250: loss 4.6141, time 20447.01ms, mfu 0.05% +step 7500: train loss 4.5282, val loss 4.5512 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup3000_seed123 +iter 7500: loss 4.6815, time 20435.65ms, mfu 0.05% +step 7750: train loss 4.5004, val loss 4.5119 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup3000_seed123 +iter 7750: loss 5.0284, time 20726.53ms, mfu 0.05% +step 8000: train loss 4.5088, val loss 4.5241 +iter 8000: loss 4.6025, time 18764.30ms, mfu 0.05% +step 8250: train loss 4.4414, val loss 4.4947 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup3000_seed123 +iter 8250: loss 4.7036, time 20353.52ms, mfu 0.05% +step 8500: train loss 4.4505, val loss 4.4712 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup3000_seed123 +iter 8500: loss 3.9483, time 21189.44ms, mfu 0.05% +step 8750: train loss 4.4594, val loss 4.4734 +iter 8750: loss 4.7607, time 18799.42ms, mfu 0.05% +step 9000: train loss 4.4110, val loss 4.4400 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup3000_seed123 +iter 9000: loss 3.9174, time 20640.26ms, mfu 0.05% +step 9250: train loss 4.3899, val loss 4.4170 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup3000_seed123 +iter 9250: loss 4.1758, time 20755.06ms, mfu 0.05% +step 9500: train loss 4.3965, val loss 4.4142 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup3000_seed123 +iter 9500: loss 4.6644, time 20723.70ms, mfu 0.05% +step 9750: train loss 4.3666, val loss 4.3944 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup3000_seed123 +iter 9750: loss 4.6376, time 20500.66ms, mfu 0.05% +step 10000: train loss 4.3771, val loss 4.4029 +iter 10000: loss 4.4720, time 18809.11ms, mfu 0.05% + +--> RESULT | cheap_qa | warmup=3000 | seed=123 | BEST=4.3944 | FINAL=4.4029 | ✗ regressed (best at 9750) + + +WARMUP=3000 RUNNING AGGREGATE (3/3 seeds): + seed 42: 4.3711 + seed 123: 4.4029 + seed 1337: 4.4108 + mean so far: 4.3949 ± 0.0172 + + +#################################################################################################### +RUNNING: cheap_qa | warmup=2000 | seed=42 | clean rerun replaces screen anchor +#################################################################################################### + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.69M +C:\Users\brian\documents\nanogpt\train.py:189: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 124,059,648 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9432, val loss 10.9442 +iter 0: loss 10.9369, time 18476.94ms, mfu -100.00% +step 250: train loss 6.9464, val loss 6.9443 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 250: loss 6.8100, time 19717.64ms, mfu 0.05% +step 500: train loss 6.5256, val loss 6.5025 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 500: loss 6.3881, time 19883.16ms, mfu 0.05% +step 750: train loss 6.3280, val loss 6.3228 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 750: loss 6.5645, time 20365.92ms, mfu 0.05% +step 1000: train loss 6.1674, val loss 6.1657 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 1000: loss 5.9229, time 19790.42ms, mfu 0.05% +step 1250: train loss 6.0475, val loss 6.0753 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 1250: loss 5.9018, time 19804.69ms, mfu 0.05% +step 1500: train loss 5.9663, val loss 5.9876 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 1500: loss 5.7663, time 21378.97ms, mfu 0.05% +step 1750: train loss 5.8599, val loss 5.8649 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 1750: loss 6.8172, time 20217.75ms, mfu 0.05% +step 2000: train loss 5.7330, val loss 5.7413 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 2000: loss 5.6612, time 19740.89ms, mfu 0.05% +step 2250: train loss 5.6547, val loss 5.6771 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 2250: loss 5.5784, time 19986.96ms, mfu 0.05% +step 2500: train loss 5.5503, val loss 5.5386 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 2500: loss 5.5529, time 20059.88ms, mfu 0.05% +step 2750: train loss 5.4573, val loss 5.4626 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 2750: loss 5.6449, time 19979.06ms, mfu 0.05% +step 3000: train loss 5.4294, val loss 5.3987 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 3000: loss 5.4401, time 20349.54ms, mfu 0.05% +step 3250: train loss 5.3166, val loss 5.3260 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 3250: loss 5.4842, time 19921.93ms, mfu 0.05% +step 3500: train loss 5.2501, val loss 5.2773 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 3500: loss 5.3664, time 20004.73ms, mfu 0.05% +step 3750: train loss 5.2268, val loss 5.2082 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 3750: loss 5.3383, time 19855.62ms, mfu 0.05% +step 4000: train loss 5.1718, val loss 5.1718 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 4000: loss 4.9622, time 19834.48ms, mfu 0.05% +step 4250: train loss 5.0859, val loss 5.0829 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 4250: loss 4.8761, time 19861.80ms, mfu 0.05% +step 4500: train loss 5.0471, val loss 5.0688 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 4500: loss 5.2415, time 19805.49ms, mfu 0.05% +step 4750: train loss 5.0061, val loss 5.0211 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 4750: loss 5.2079, time 20030.45ms, mfu 0.05% +step 5000: train loss 4.9361, val loss 4.9611 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 5000: loss 4.9069, time 20408.35ms, mfu 0.05% +step 5250: train loss 4.8951, val loss 4.9137 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 5250: loss 5.1775, time 19933.63ms, mfu 0.05% +step 5500: train loss 4.8586, val loss 4.8689 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 5500: loss 4.7118, time 20016.65ms, mfu 0.05% +step 5750: train loss 4.8131, val loss 4.8397 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 5750: loss 4.7065, time 19995.45ms, mfu 0.05% +step 6000: train loss 4.7595, val loss 4.7756 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 6000: loss 4.6567, time 19752.24ms, mfu 0.05% +step 6250: train loss 4.7067, val loss 4.7475 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 6250: loss 4.8938, time 19856.54ms, mfu 0.05% +step 6500: train loss 4.6756, val loss 4.6659 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 6500: loss 4.4377, time 19893.35ms, mfu 0.05% +step 6750: train loss 4.6402, val loss 4.6371 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 6750: loss 4.5505, time 19862.43ms, mfu 0.05% +step 7000: train loss 4.6188, val loss 4.6136 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 7000: loss 4.3624, time 20926.47ms, mfu 0.05% +step 7250: train loss 4.5917, val loss 4.5815 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 7250: loss 4.7911, time 19748.06ms, mfu 0.05% +step 7500: train loss 4.5198, val loss 4.5707 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 7500: loss 4.5661, time 19833.60ms, mfu 0.05% +step 7750: train loss 4.5304, val loss 4.5401 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 7750: loss 4.3395, time 19915.58ms, mfu 0.05% +step 8000: train loss 4.4681, val loss 4.5144 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 8000: loss 4.6564, time 20098.85ms, mfu 0.05% +step 8250: train loss 4.5078, val loss 4.4995 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 8250: loss 4.0386, time 20097.53ms, mfu 0.05% +step 8500: train loss 4.5062, val loss 4.4731 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 8500: loss 4.6772, time 19863.65ms, mfu 0.05% +step 8750: train loss 4.4344, val loss 4.4843 +iter 8750: loss 4.2942, time 18282.18ms, mfu 0.05% +step 9000: train loss 4.4445, val loss 4.4503 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 9000: loss 4.0782, time 19933.78ms, mfu 0.05% +step 9250: train loss 4.4372, val loss 4.4687 +iter 9250: loss 4.2486, time 18301.09ms, mfu 0.05% +step 9500: train loss 4.4277, val loss 4.4078 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 9500: loss 4.4247, time 19852.89ms, mfu 0.05% +step 9750: train loss 4.4252, val loss 4.4337 +iter 9750: loss 3.9244, time 18286.70ms, mfu 0.05% +step 10000: train loss 4.3825, val loss 4.3906 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42 +iter 10000: loss 4.2597, time 19787.73ms, mfu 0.05% + +--> RESULT | cheap_qa | warmup=2000 | seed=42 | BEST=4.3906 | FINAL=4.3906 | ✓ best=final + + +==================================================================================================== +ALL RUNS COMPLETE +==================================================================================================== +Results CSV: cheap_qa_confirmation_runs_20260320_1854.csv +Summary file: cheap_qa_confirmation_summary_20260320_1854.txt + +Paste the summary file contents here for final analysis. +PS C:\Users\brian\documents\nanogpt> python a_only_screen_runner.py +==================================================================================================== +A-ONLY GATE SCREEN +==================================================================================================== +Variant: dynamic_a_conditioned +Warmup: 2000 | Seed: 42 +Gate: per-head Linear(hs->hs) conditioned on y (SDPA output) only +Expected runtime: ~65 minutes +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.64M +C:\Users\brian\documents\nanogpt\train.py:191: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 124,010,496 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9645, val loss 10.9616 +iter 0: loss 10.9448, time 18653.37ms, mfu -100.00% +step 250: train loss 6.9237, val loss 6.9409 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 250: loss 6.8589, time 19973.58ms, mfu 0.05% +step 500: train loss 6.4783, val loss 6.5235 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 500: loss 6.2179, time 20910.32ms, mfu 0.05% +step 750: train loss 6.3193, val loss 6.3300 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 750: loss 6.2956, time 19966.83ms, mfu 0.05% +step 1000: train loss 6.1644, val loss 6.1883 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 1000: loss 6.6515, time 20743.01ms, mfu 0.05% +step 1250: train loss 6.0486, val loss 6.0849 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 1250: loss 5.4954, time 20309.41ms, mfu 0.05% +step 1500: train loss 5.9336, val loss 5.9806 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 1500: loss 6.2328, time 20805.95ms, mfu 0.05% +step 1750: train loss 5.8958, val loss 5.9003 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 1750: loss 5.2476, time 20205.93ms, mfu 0.05% +step 2000: train loss 5.7820, val loss 5.7876 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 2000: loss 5.9379, time 22405.41ms, mfu 0.05% +step 2250: train loss 5.6643, val loss 5.6818 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 2250: loss 5.5756, time 23075.08ms, mfu 0.05% +step 2500: train loss 5.5554, val loss 5.5715 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 2500: loss 5.8386, time 20167.22ms, mfu 0.05% +step 2750: train loss 5.5135, val loss 5.4969 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 2750: loss 5.1078, time 20202.66ms, mfu 0.05% +step 3000: train loss 5.3904, val loss 5.4395 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 3000: loss 5.5585, time 20291.16ms, mfu 0.05% +step 3250: train loss 5.3791, val loss 5.4001 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 3250: loss 5.1207, time 20337.71ms, mfu 0.05% +step 3500: train loss 5.3007, val loss 5.2980 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 3500: loss 5.5000, time 21820.17ms, mfu 0.05% +step 3750: train loss 5.2530, val loss 5.2797 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 3750: loss 5.0614, time 20765.52ms, mfu 0.05% +step 4000: train loss 5.1840, val loss 5.1575 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 4000: loss 5.2035, time 19940.35ms, mfu 0.05% +step 4250: train loss 5.1328, val loss 5.1200 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 4250: loss 5.3815, time 20333.22ms, mfu 0.05% +step 4500: train loss 5.0525, val loss 5.0604 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 4500: loss 5.0793, time 19711.73ms, mfu 0.05% +step 4750: train loss 5.0263, val loss 5.0356 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 4750: loss 5.3004, time 20699.62ms, mfu 0.05% +step 5000: train loss 4.9981, val loss 4.9759 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 5000: loss 5.1915, time 20158.20ms, mfu 0.05% +step 5250: train loss 4.9096, val loss 4.9425 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 5250: loss 5.0322, time 19868.33ms, mfu 0.05% +step 5500: train loss 4.8741, val loss 4.8653 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 5500: loss 4.7442, time 19815.43ms, mfu 0.05% +step 5750: train loss 4.8083, val loss 4.8309 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 5750: loss 4.8347, time 19669.30ms, mfu 0.05% +step 6000: train loss 4.8066, val loss 4.8074 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 6000: loss 4.4682, time 20590.52ms, mfu 0.05% +step 6250: train loss 4.7528, val loss 4.7469 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 6250: loss 4.7310, time 20205.76ms, mfu 0.05% +step 6500: train loss 4.7068, val loss 4.7283 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 6500: loss 4.8232, time 19800.55ms, mfu 0.05% +step 6750: train loss 4.6564, val loss 4.7040 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 6750: loss 5.0022, time 19892.81ms, mfu 0.05% +step 7000: train loss 4.6440, val loss 4.6624 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 7000: loss 4.3746, time 20200.32ms, mfu 0.05% +step 7250: train loss 4.5737, val loss 4.6371 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 7250: loss 4.1617, time 19898.52ms, mfu 0.05% +step 7500: train loss 4.5703, val loss 4.5694 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 7500: loss 4.5161, time 19877.05ms, mfu 0.05% +step 7750: train loss 4.5395, val loss 4.5799 +iter 7750: loss 4.8126, time 18022.77ms, mfu 0.05% +step 8000: train loss 4.5353, val loss 4.5466 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 8000: loss 4.2691, time 20096.62ms, mfu 0.05% +step 8250: train loss 4.4909, val loss 4.5167 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 8250: loss 4.7377, time 19519.62ms, mfu 0.05% +step 8500: train loss 4.4949, val loss 4.5086 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 8500: loss 4.5297, time 20209.84ms, mfu 0.05% +step 8750: train loss 4.4792, val loss 4.5138 +iter 8750: loss 4.5062, time 18269.85ms, mfu 0.05% +step 9000: train loss 4.4583, val loss 4.4683 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 9000: loss 4.4585, time 20193.82ms, mfu 0.05% +step 9250: train loss 4.4440, val loss 4.4678 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 9250: loss 4.5633, time 19671.55ms, mfu 0.05% +step 9500: train loss 4.4699, val loss 4.4395 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 9500: loss 4.9230, time 19808.05ms, mfu 0.05% +step 9750: train loss 4.4150, val loss 4.4555 +iter 9750: loss 4.2102, time 18661.91ms, mfu 0.05% +step 10000: train loss 4.4247, val loss 4.4167 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 10000: loss 4.5439, time 20034.44ms, mfu 0.05% + +--> RESULT | a_only | warmup=2000 | seed=42 | BEST=4.4167 | FINAL=4.4167 | ✓ best=final + +==================================================================================================== +DONE +==================================================================================================== +Results CSV: a_only_screen_runs_20260320_2335.csv +Summary file: a_only_screen_summary_20260320_2335.txt + +Paste the summary file contents here for analysis. +PS C:\Users\brian\documents\nanogpt> python friday_night_runner.py +==================================================================================================== +🎲 FRIDAY NIGHT SPICY RANDOM GATE RUNNER 🎲 +==================================================================================================== +Variants (5 total, ~325 min): + a_only → learned: A-only Linear(64->64) + random_uniform → chaos: uniform random [0,1] + ones_identity → sanity: always 1.0 (= baseline) + random_normal → chaos: normal(0.5,0.2) clamped + bernoulli_drop → spicy: binary 50% dropout gate + +Predictions: + ones_gate ≈ baseline (identity) + random_* somewhere around baseline ± noise + a_only better than random, worse than cheap Q + all lose to cheap Q+A (4.4073) + prove us wrong, random gate. prove us wrong. +==================================================================================================== + +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 +RUNNING: a_only + variant: dynamic_a_conditioned + description: learned: A-only Linear(64->64) +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.64M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 124,010,496 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9645, val loss 10.9616 +iter 0: loss 10.9448, time 18443.77ms, mfu -100.00% +step 250: train loss 6.9237, val loss 6.9409 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 250: loss 6.8590, time 19415.80ms, mfu 0.05% +step 500: train loss 6.4783, val loss 6.5235 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 500: loss 6.2173, time 19240.26ms, mfu 0.05% +step 750: train loss 6.3187, val loss 6.3292 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 750: loss 6.2933, time 19231.92ms, mfu 0.05% +step 1000: train loss 6.1758, val loss 6.1991 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 1000: loss 6.6675, time 19369.09ms, mfu 0.05% +step 1250: train loss 6.0403, val loss 6.0765 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 1250: loss 5.4893, time 19279.44ms, mfu 0.05% +step 1500: train loss 5.9305, val loss 5.9813 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 1500: loss 6.2611, time 19341.89ms, mfu 0.05% +step 1750: train loss 5.9035, val loss 5.9130 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 1750: loss 5.2699, time 19280.42ms, mfu 0.05% +step 2000: train loss 5.7756, val loss 5.7839 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 2000: loss 5.9422, time 19390.85ms, mfu 0.05% +step 2250: train loss 5.6619, val loss 5.6757 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 2250: loss 5.5553, time 19292.89ms, mfu 0.05% +step 2500: train loss 5.5502, val loss 5.5670 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 2500: loss 5.8170, time 19349.65ms, mfu 0.05% +step 2750: train loss 5.5050, val loss 5.4875 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 2750: loss 5.0915, time 19353.73ms, mfu 0.05% +step 3000: train loss 5.3882, val loss 5.4398 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 3000: loss 5.5716, time 19293.36ms, mfu 0.05% +step 3250: train loss 5.3711, val loss 5.3938 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 3250: loss 5.1399, time 19280.47ms, mfu 0.05% +step 3500: train loss 5.2958, val loss 5.2912 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 3500: loss 5.4773, time 19567.95ms, mfu 0.05% +step 3750: train loss 5.2493, val loss 5.2739 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 3750: loss 5.0562, time 19443.69ms, mfu 0.05% +step 4000: train loss 5.1875, val loss 5.1603 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 4000: loss 5.2306, time 19312.22ms, mfu 0.05% +step 4250: train loss 5.1299, val loss 5.1169 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 4250: loss 5.3579, time 19330.55ms, mfu 0.05% +step 4500: train loss 5.0527, val loss 5.0627 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 4500: loss 5.0900, time 19347.99ms, mfu 0.05% +step 4750: train loss 5.0282, val loss 5.0378 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 4750: loss 5.2617, time 20071.23ms, mfu 0.05% +step 5000: train loss 4.9899, val loss 4.9699 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 5000: loss 5.1710, time 19211.45ms, mfu 0.05% +step 5250: train loss 4.9078, val loss 4.9443 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 5250: loss 5.0068, time 19284.95ms, mfu 0.05% +step 5500: train loss 4.8680, val loss 4.8584 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 5500: loss 4.7205, time 19252.65ms, mfu 0.05% +step 5750: train loss 4.8048, val loss 4.8238 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 5750: loss 4.8346, time 19230.06ms, mfu 0.05% +step 6000: train loss 4.8122, val loss 4.8109 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 6000: loss 4.4869, time 19296.00ms, mfu 0.05% +step 6250: train loss 4.7510, val loss 4.7443 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 6250: loss 4.7312, time 19386.68ms, mfu 0.05% +step 6500: train loss 4.7077, val loss 4.7289 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 6500: loss 4.8087, time 19244.24ms, mfu 0.05% +step 6750: train loss 4.6542, val loss 4.7009 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 6750: loss 4.9745, time 19134.67ms, mfu 0.05% +step 7000: train loss 4.6435, val loss 4.6620 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 7000: loss 4.3641, time 19225.84ms, mfu 0.05% +step 7250: train loss 4.5737, val loss 4.6373 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 7250: loss 4.1618, time 19391.80ms, mfu 0.05% +step 7500: train loss 4.5672, val loss 4.5669 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 7500: loss 4.4988, time 19227.10ms, mfu 0.05% +step 7750: train loss 4.5419, val loss 4.5814 +iter 7750: loss 4.8327, time 17752.74ms, mfu 0.05% +step 8000: train loss 4.5356, val loss 4.5449 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 8000: loss 4.2673, time 19222.02ms, mfu 0.05% +step 8250: train loss 4.4925, val loss 4.5176 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 8250: loss 4.6810, time 19140.68ms, mfu 0.05% +step 8500: train loss 4.4968, val loss 4.5101 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 8500: loss 4.5280, time 19263.29ms, mfu 0.05% +step 8750: train loss 4.4822, val loss 4.5178 +iter 8750: loss 4.5256, time 17747.86ms, mfu 0.06% +step 9000: train loss 4.4594, val loss 4.4689 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 9000: loss 4.4356, time 19230.15ms, mfu 0.06% +step 9250: train loss 4.4474, val loss 4.4700 +iter 9250: loss 4.5588, time 17722.09ms, mfu 0.06% +step 9500: train loss 4.4716, val loss 4.4419 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 9500: loss 4.8731, time 19180.94ms, mfu 0.06% +step 9750: train loss 4.4196, val loss 4.4574 +iter 9750: loss 4.1847, time 17751.17ms, mfu 0.06% +step 10000: train loss 4.4258, val loss 4.4178 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42 +iter 10000: loss 4.5211, time 19199.43ms, mfu 0.06% + +🎯 RESULT: a_only + BEST=4.4178 | FINAL=4.4178 | ✓ + vs baseline: -0.2563 (BETTER) + + +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 +RUNNING: random_uniform + variant: dynamic_random_gate + description: chaos: uniform random [0,1] +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9679, val loss 10.9680 +iter 0: loss 10.9638, time 17870.55ms, mfu -100.00% +step 250: train loss 6.9872, val loss 6.9840 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 250: loss 7.1018, time 18956.26ms, mfu 0.06% +step 500: train loss 6.5113, val loss 6.5166 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 500: loss 6.1603, time 19231.93ms, mfu 0.06% +step 750: train loss 6.2962, val loss 6.3279 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 750: loss 6.6386, time 19271.97ms, mfu 0.06% +step 1000: train loss 6.1707, val loss 6.1682 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 1000: loss 5.8109, time 19450.68ms, mfu 0.05% +step 1250: train loss 6.0352, val loss 6.0505 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 1250: loss 6.5340, time 21832.23ms, mfu 0.05% +step 1500: train loss 5.9807, val loss 5.9754 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 1500: loss 5.4833, time 21363.56ms, mfu 0.05% +step 1750: train loss 5.8909, val loss 5.9039 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 1750: loss 6.1984, time 20060.93ms, mfu 0.05% +step 2000: train loss 5.8532, val loss 5.8638 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 2000: loss 5.6615, time 19177.93ms, mfu 0.05% +step 2250: train loss 5.6958, val loss 5.7078 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 2250: loss 5.7810, time 19210.51ms, mfu 0.05% +step 2500: train loss 5.6288, val loss 5.6262 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 2500: loss 4.9425, time 19036.67ms, mfu 0.05% +step 2750: train loss 5.5487, val loss 5.5499 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 2750: loss 5.5762, time 20547.46ms, mfu 0.05% +step 3000: train loss 5.4668, val loss 5.4518 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 3000: loss 5.5154, time 19110.49ms, mfu 0.05% +step 3250: train loss 5.4313, val loss 5.3890 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 3250: loss 5.5997, time 19180.71ms, mfu 0.05% +step 3500: train loss 5.3320, val loss 5.3818 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 3500: loss 5.4354, time 19228.78ms, mfu 0.05% +step 3750: train loss 5.3035, val loss 5.3266 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 3750: loss 5.3401, time 20880.00ms, mfu 0.05% +step 4000: train loss 5.2517, val loss 5.2555 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 4000: loss 5.9418, time 18801.26ms, mfu 0.05% +step 4250: train loss 5.2118, val loss 5.1966 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 4250: loss 4.9198, time 18843.98ms, mfu 0.05% +step 4500: train loss 5.1302, val loss 5.1582 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 4500: loss 5.2362, time 18838.79ms, mfu 0.05% +step 4750: train loss 5.1157, val loss 5.1250 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 4750: loss 5.2029, time 18807.17ms, mfu 0.05% +step 5000: train loss 5.0667, val loss 5.0418 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 5000: loss 5.4934, time 18817.45ms, mfu 0.05% +step 5250: train loss 5.0167, val loss 5.0178 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 5250: loss 5.1411, time 18848.17ms, mfu 0.05% +step 5500: train loss 5.0000, val loss 4.9898 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 5500: loss 4.7791, time 18956.36ms, mfu 0.05% +step 5750: train loss 4.9404, val loss 4.9363 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 5750: loss 5.1685, time 18810.64ms, mfu 0.05% +step 6000: train loss 4.8758, val loss 4.8860 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 6000: loss 4.8065, time 18815.69ms, mfu 0.05% +step 6250: train loss 4.8422, val loss 4.8530 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 6250: loss 4.5172, time 18823.03ms, mfu 0.05% +step 6500: train loss 4.8387, val loss 4.8216 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 6500: loss 4.6833, time 18784.39ms, mfu 0.05% +step 6750: train loss 4.7684, val loss 4.7551 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 6750: loss 4.7107, time 18772.43ms, mfu 0.06% +step 7000: train loss 4.7203, val loss 4.7524 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 7000: loss 4.6630, time 18856.33ms, mfu 0.06% +step 7250: train loss 4.7024, val loss 4.6969 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 7250: loss 4.8819, time 18802.60ms, mfu 0.06% +step 7500: train loss 4.6948, val loss 4.6785 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 7500: loss 4.8064, time 18837.54ms, mfu 0.06% +step 7750: train loss 4.6741, val loss 4.6468 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 7750: loss 4.7683, time 18742.00ms, mfu 0.06% +step 8000: train loss 4.6265, val loss 4.6290 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 8000: loss 4.5920, time 18803.55ms, mfu 0.06% +step 8250: train loss 4.5823, val loss 4.6010 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 8250: loss 4.4146, time 18966.15ms, mfu 0.06% +step 8500: train loss 4.5902, val loss 4.6190 +iter 8500: loss 4.4473, time 17329.61ms, mfu 0.06% +step 8750: train loss 4.5440, val loss 4.5642 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 8750: loss 4.7440, time 18776.39ms, mfu 0.06% +step 9000: train loss 4.5550, val loss 4.5755 +iter 9000: loss 4.5494, time 17529.41ms, mfu 0.06% +step 9250: train loss 4.5333, val loss 4.5247 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 9250: loss 4.6468, time 18809.47ms, mfu 0.06% +step 9500: train loss 4.5022, val loss 4.5206 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 9500: loss 4.2362, time 18852.32ms, mfu 0.06% +step 9750: train loss 4.4989, val loss 4.5132 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed42 +iter 9750: loss 4.8144, time 18787.39ms, mfu 0.06% +step 10000: train loss 4.4955, val loss 4.5238 +iter 10000: loss 4.5552, time 17386.52ms, mfu 0.06% + +🎯 RESULT: random_uniform + BEST=4.5132 | FINAL=4.5238 | ✗ (best@9750) + vs baseline: -0.1503 (BETTER) + + +🏆 CURRENT LEADERBOARD: + 1. a_only 4.4178 (vs baseline: -0.2563) + 2. random_uniform 4.5238 (vs baseline: -0.1503) + + +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 +RUNNING: ones_identity + variant: dynamic_ones_gate + description: sanity: always 1.0 (= baseline) +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9665, val loss 10.9665 +iter 0: loss 10.9808, time 17251.95ms, mfu -100.00% +step 250: train loss 7.0174, val loss 7.0114 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 250: loss 7.1149, time 18370.26ms, mfu 0.06% +step 500: train loss 6.5240, val loss 6.5303 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 500: loss 6.1701, time 18530.54ms, mfu 0.06% +step 750: train loss 6.2957, val loss 6.3275 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 750: loss 6.6318, time 18503.02ms, mfu 0.06% +step 1000: train loss 6.1623, val loss 6.1590 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 1000: loss 5.8230, time 18516.92ms, mfu 0.06% +step 1250: train loss 6.0226, val loss 6.0385 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 1250: loss 6.5363, time 18539.40ms, mfu 0.06% +step 1500: train loss 5.9856, val loss 5.9786 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 1500: loss 5.4937, time 18549.35ms, mfu 0.06% +step 1750: train loss 5.8984, val loss 5.9056 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 1750: loss 6.1882, time 18517.48ms, mfu 0.06% +step 2000: train loss 5.8284, val loss 5.8396 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 2000: loss 5.6563, time 18553.73ms, mfu 0.06% +step 2250: train loss 5.6888, val loss 5.6991 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 2250: loss 5.7713, time 18547.59ms, mfu 0.06% +step 2500: train loss 5.6234, val loss 5.6154 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 2500: loss 4.9088, time 18523.74ms, mfu 0.06% +step 2750: train loss 5.5249, val loss 5.5246 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 2750: loss 5.5503, time 18559.29ms, mfu 0.06% +step 3000: train loss 5.4603, val loss 5.4473 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 3000: loss 5.4948, time 18509.93ms, mfu 0.06% +step 3250: train loss 5.4131, val loss 5.3722 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 3250: loss 5.5908, time 18588.40ms, mfu 0.06% +step 3500: train loss 5.3228, val loss 5.3732 +iter 3500: loss 5.4455, time 17071.83ms, mfu 0.06% +step 3750: train loss 5.2851, val loss 5.3088 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 3750: loss 5.3407, time 18631.56ms, mfu 0.06% +step 4000: train loss 5.2377, val loss 5.2428 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 4000: loss 5.9134, time 18479.50ms, mfu 0.06% +step 4250: train loss 5.1908, val loss 5.1723 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 4250: loss 4.8851, time 18573.31ms, mfu 0.06% +step 4500: train loss 5.1048, val loss 5.1297 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 4500: loss 5.2002, time 18496.51ms, mfu 0.06% +step 4750: train loss 5.0813, val loss 5.0907 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 4750: loss 5.1824, time 18677.00ms, mfu 0.06% +step 5000: train loss 5.0298, val loss 5.0062 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 5000: loss 5.4089, time 18498.49ms, mfu 0.06% +step 5250: train loss 4.9632, val loss 4.9651 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 5250: loss 5.0698, time 18581.74ms, mfu 0.06% +step 5500: train loss 4.9531, val loss 4.9400 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 5500: loss 4.6889, time 18558.23ms, mfu 0.06% +step 5750: train loss 4.8740, val loss 4.8843 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 5750: loss 5.1281, time 18559.42ms, mfu 0.06% +step 6000: train loss 4.8153, val loss 4.8240 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 6000: loss 4.7921, time 18535.91ms, mfu 0.06% +step 6250: train loss 4.7731, val loss 4.7876 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 6250: loss 4.4769, time 18528.44ms, mfu 0.06% +step 6500: train loss 4.7775, val loss 4.7587 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 6500: loss 4.6342, time 18496.87ms, mfu 0.06% +step 6750: train loss 4.7127, val loss 4.7032 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 6750: loss 4.6827, time 18522.07ms, mfu 0.06% +step 7000: train loss 4.6618, val loss 4.6886 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 7000: loss 4.6305, time 18583.62ms, mfu 0.06% +step 7250: train loss 4.6496, val loss 4.6473 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 7250: loss 4.8604, time 18555.62ms, mfu 0.06% +step 7500: train loss 4.6455, val loss 4.6286 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 7500: loss 4.7550, time 18511.91ms, mfu 0.06% +step 7750: train loss 4.6211, val loss 4.6015 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 7750: loss 4.6937, time 18566.21ms, mfu 0.06% +step 8000: train loss 4.5774, val loss 4.5796 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 8000: loss 4.5482, time 18584.38ms, mfu 0.06% +step 8250: train loss 4.5313, val loss 4.5575 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 8250: loss 4.3745, time 18615.77ms, mfu 0.06% +step 8500: train loss 4.5454, val loss 4.5763 +iter 8500: loss 4.3886, time 17086.25ms, mfu 0.06% +step 8750: train loss 4.4996, val loss 4.5230 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 8750: loss 4.6602, time 18521.62ms, mfu 0.06% +step 9000: train loss 4.5141, val loss 4.5360 +iter 9000: loss 4.5047, time 17044.78ms, mfu 0.06% +step 9250: train loss 4.4903, val loss 4.4813 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 9250: loss 4.6086, time 18484.90ms, mfu 0.06% +step 9500: train loss 4.4641, val loss 4.4795 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 9500: loss 4.2081, time 18533.52ms, mfu 0.06% +step 9750: train loss 4.4574, val loss 4.4702 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42 +iter 9750: loss 4.7915, time 18505.06ms, mfu 0.06% +step 10000: train loss 4.4582, val loss 4.4869 +iter 10000: loss 4.4660, time 17071.83ms, mfu 0.06% + +🎯 RESULT: ones_identity + BEST=4.4702 | FINAL=4.4869 | ✗ (best@9750) + vs baseline: -0.1872 (BETTER) + + +🏆 CURRENT LEADERBOARD: + 1. a_only 4.4178 (vs baseline: -0.2563) + 2. ones_identity 4.4869 (vs baseline: -0.1872) + 3. random_uniform 4.5238 (vs baseline: -0.1503) + + +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 +RUNNING: random_normal + variant: dynamic_random_normal + description: chaos: normal(0.5,0.2) clamped +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9683, val loss 10.9677 +iter 0: loss 10.9638, time 18045.85ms, mfu -100.00% +step 250: train loss 6.9877, val loss 6.9845 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 250: loss 7.0998, time 18964.96ms, mfu 0.06% +step 500: train loss 6.5115, val loss 6.5163 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 500: loss 6.1623, time 19421.31ms, mfu 0.06% +step 750: train loss 6.2930, val loss 6.3249 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 750: loss 6.6255, time 19255.18ms, mfu 0.06% +step 1000: train loss 6.1622, val loss 6.1598 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 1000: loss 5.8071, time 19209.62ms, mfu 0.06% +step 1250: train loss 6.0175, val loss 6.0332 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 1250: loss 6.5145, time 19328.62ms, mfu 0.05% +step 1500: train loss 5.9643, val loss 5.9622 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 1500: loss 5.4869, time 22052.88ms, mfu 0.05% +step 1750: train loss 5.8836, val loss 5.8926 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 1750: loss 6.1663, time 19240.23ms, mfu 0.05% +step 2000: train loss 5.8295, val loss 5.8419 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 2000: loss 5.6385, time 19225.89ms, mfu 0.05% +step 2250: train loss 5.6787, val loss 5.6882 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 2250: loss 5.7722, time 19161.88ms, mfu 0.05% +step 2500: train loss 5.6025, val loss 5.5954 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 2500: loss 4.9144, time 19151.32ms, mfu 0.05% +step 2750: train loss 5.5246, val loss 5.5246 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 2750: loss 5.5575, time 19185.62ms, mfu 0.05% +step 3000: train loss 5.4456, val loss 5.4315 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 3000: loss 5.4935, time 19258.24ms, mfu 0.05% +step 3250: train loss 5.4098, val loss 5.3644 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 3250: loss 5.5693, time 19155.47ms, mfu 0.05% +step 3500: train loss 5.3203, val loss 5.3704 +iter 3500: loss 5.4317, time 17735.51ms, mfu 0.05% +step 3750: train loss 5.2838, val loss 5.3090 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 3750: loss 5.3212, time 19206.93ms, mfu 0.05% +step 4000: train loss 5.2324, val loss 5.2395 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 4000: loss 5.9226, time 19188.13ms, mfu 0.05% +step 4250: train loss 5.1905, val loss 5.1718 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 4250: loss 4.9139, time 19262.79ms, mfu 0.05% +step 4500: train loss 5.0984, val loss 5.1245 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 4500: loss 5.1889, time 19161.33ms, mfu 0.05% +step 4750: train loss 5.0884, val loss 5.0952 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 4750: loss 5.1797, time 19160.90ms, mfu 0.05% +step 5000: train loss 5.0492, val loss 5.0222 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 5000: loss 5.5888, time 19209.13ms, mfu 0.05% +step 5250: train loss 4.9840, val loss 4.9854 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 5250: loss 5.1297, time 19135.42ms, mfu 0.05% +step 5500: train loss 4.9713, val loss 4.9578 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 5500: loss 4.6953, time 19163.99ms, mfu 0.05% +step 5750: train loss 4.9064, val loss 4.9066 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 5750: loss 5.1258, time 19214.21ms, mfu 0.05% +step 6000: train loss 4.8382, val loss 4.8449 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 6000: loss 4.7639, time 19180.18ms, mfu 0.05% +step 6250: train loss 4.7994, val loss 4.8133 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 6250: loss 4.4873, time 19255.19ms, mfu 0.05% +step 6500: train loss 4.7967, val loss 4.7748 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 6500: loss 4.6301, time 19226.03ms, mfu 0.05% +step 6750: train loss 4.7285, val loss 4.7153 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 6750: loss 4.6758, time 19240.69ms, mfu 0.05% +step 7000: train loss 4.6823, val loss 4.7118 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 7000: loss 4.6362, time 19253.13ms, mfu 0.05% +step 7250: train loss 4.6679, val loss 4.6627 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 7250: loss 4.8604, time 19226.93ms, mfu 0.05% +step 7500: train loss 4.6581, val loss 4.6407 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 7500: loss 4.7506, time 19186.62ms, mfu 0.05% +step 7750: train loss 4.6350, val loss 4.6125 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 7750: loss 4.7381, time 19192.61ms, mfu 0.05% +step 8000: train loss 4.5910, val loss 4.5903 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 8000: loss 4.5547, time 19252.13ms, mfu 0.05% +step 8250: train loss 4.5473, val loss 4.5690 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 8250: loss 4.4076, time 19235.26ms, mfu 0.05% +step 8500: train loss 4.5575, val loss 4.5868 +iter 8500: loss 4.4290, time 17744.04ms, mfu 0.06% +step 8750: train loss 4.5109, val loss 4.5315 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 8750: loss 4.6977, time 19156.87ms, mfu 0.05% +step 9000: train loss 4.5234, val loss 4.5453 +iter 9000: loss 4.5098, time 17714.41ms, mfu 0.06% +step 9250: train loss 4.5005, val loss 4.4927 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 9250: loss 4.6354, time 20093.51ms, mfu 0.06% +step 9500: train loss 4.4744, val loss 4.4903 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 9500: loss 4.2209, time 19258.53ms, mfu 0.06% +step 9750: train loss 4.4685, val loss 4.4822 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed42 +iter 9750: loss 4.7620, time 19153.53ms, mfu 0.05% +step 10000: train loss 4.4670, val loss 4.4982 +iter 10000: loss 4.5222, time 17738.08ms, mfu 0.06% + +🎯 RESULT: random_normal + BEST=4.4822 | FINAL=4.4982 | ✗ (best@9750) + vs baseline: -0.1759 (BETTER) + + +🏆 CURRENT LEADERBOARD: + 1. a_only 4.4178 (vs baseline: -0.2563) + 2. ones_identity 4.4869 (vs baseline: -0.1872) + 3. random_normal 4.4982 (vs baseline: -0.1759) + 4. random_uniform 4.5238 (vs baseline: -0.1503) + + +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 +RUNNING: bernoulli_drop + variant: dynamic_bernoulli_gate + description: spicy: binary 50% dropout gate +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9658, val loss 10.9664 +iter 0: loss 10.9653, time 18230.53ms, mfu -100.00% +step 250: train loss 7.0186, val loss 7.0152 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 250: loss 7.1329, time 19301.75ms, mfu 0.05% +step 500: train loss 6.5458, val loss 6.5502 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 500: loss 6.1870, time 19417.25ms, mfu 0.05% +step 750: train loss 6.3291, val loss 6.3607 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 750: loss 6.6829, time 19465.58ms, mfu 0.05% +step 1000: train loss 6.2198, val loss 6.2146 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 1000: loss 5.8748, time 19404.05ms, mfu 0.05% +step 1250: train loss 6.0766, val loss 6.0927 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 1250: loss 6.5922, time 19536.36ms, mfu 0.05% +step 1500: train loss 6.0296, val loss 6.0267 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 1500: loss 5.5739, time 21054.73ms, mfu 0.05% +step 1750: train loss 5.9729, val loss 5.9819 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 1750: loss 6.2734, time 21157.45ms, mfu 0.05% +step 2000: train loss 5.9228, val loss 5.9370 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 2000: loss 5.7233, time 19491.93ms, mfu 0.05% +step 2250: train loss 5.7865, val loss 5.7937 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 2250: loss 5.9109, time 19460.00ms, mfu 0.05% +step 2500: train loss 5.7301, val loss 5.7271 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 2500: loss 5.0425, time 19464.04ms, mfu 0.05% +step 2750: train loss 5.6403, val loss 5.6404 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 2750: loss 5.6847, time 19458.38ms, mfu 0.05% +step 3000: train loss 5.5632, val loss 5.5481 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 3000: loss 5.5902, time 19467.37ms, mfu 0.05% +step 3250: train loss 5.5232, val loss 5.4826 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 3250: loss 5.6677, time 19727.49ms, mfu 0.05% +step 3500: train loss 5.4270, val loss 5.4794 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 3500: loss 5.5569, time 19477.15ms, mfu 0.05% +step 3750: train loss 5.3991, val loss 5.4246 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 3750: loss 5.4394, time 19524.31ms, mfu 0.05% +step 4000: train loss 5.3537, val loss 5.3579 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 4000: loss 5.9999, time 19479.48ms, mfu 0.05% +step 4250: train loss 5.3063, val loss 5.2903 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 4250: loss 5.0226, time 19450.80ms, mfu 0.05% +step 4500: train loss 5.2302, val loss 5.2559 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 4500: loss 5.3565, time 19464.75ms, mfu 0.05% +step 4750: train loss 5.2146, val loss 5.2182 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 4750: loss 5.3021, time 19419.35ms, mfu 0.05% +step 5000: train loss 5.1775, val loss 5.1568 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 5000: loss 5.6612, time 19487.35ms, mfu 0.05% +step 5250: train loss 5.1199, val loss 5.1212 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 5250: loss 5.2726, time 19479.75ms, mfu 0.05% +step 5500: train loss 5.1176, val loss 5.1041 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 5500: loss 4.8798, time 19429.81ms, mfu 0.05% +step 5750: train loss 5.0585, val loss 5.0540 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 5750: loss 5.2395, time 19546.36ms, mfu 0.05% +step 6000: train loss 4.9966, val loss 5.0034 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 6000: loss 4.9353, time 19493.41ms, mfu 0.05% +step 6250: train loss 4.9652, val loss 4.9752 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 6250: loss 4.6308, time 19449.09ms, mfu 0.05% +step 6500: train loss 4.9668, val loss 4.9493 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 6500: loss 4.8011, time 19453.26ms, mfu 0.05% +step 6750: train loss 4.8997, val loss 4.8867 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 6750: loss 4.8103, time 19520.57ms, mfu 0.05% +step 7000: train loss 4.8569, val loss 4.8901 +iter 7000: loss 4.8077, time 18014.26ms, mfu 0.05% +step 7250: train loss 4.8424, val loss 4.8322 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 7250: loss 4.9903, time 19483.09ms, mfu 0.05% +step 7500: train loss 4.8257, val loss 4.8117 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 7500: loss 4.9355, time 19595.48ms, mfu 0.05% +step 7750: train loss 4.8161, val loss 4.7876 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 7750: loss 4.8791, time 19458.79ms, mfu 0.05% +step 8000: train loss 4.7669, val loss 4.7719 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 8000: loss 4.7200, time 19473.43ms, mfu 0.05% +step 8250: train loss 4.7237, val loss 4.7416 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 8250: loss 4.5576, time 19573.40ms, mfu 0.05% +step 8500: train loss 4.7321, val loss 4.7627 +iter 8500: loss 4.5812, time 18025.46ms, mfu 0.05% +step 8750: train loss 4.6861, val loss 4.7050 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 8750: loss 4.8895, time 19462.66ms, mfu 0.05% +step 9000: train loss 4.7009, val loss 4.7187 +iter 9000: loss 4.6485, time 18017.46ms, mfu 0.05% +step 9250: train loss 4.6773, val loss 4.6688 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 9250: loss 4.7773, time 19441.69ms, mfu 0.05% +step 9500: train loss 4.6443, val loss 4.6617 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 9500: loss 4.3452, time 19461.92ms, mfu 0.05% +step 9750: train loss 4.6426, val loss 4.6572 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed42 +iter 9750: loss 4.9345, time 19503.96ms, mfu 0.05% +step 10000: train loss 4.6375, val loss 4.6653 +iter 10000: loss 4.8242, time 18149.40ms, mfu 0.05% + +🎯 RESULT: bernoulli_drop + BEST=4.6572 | FINAL=4.6653 | ✗ (best@9750) + vs baseline: -0.0088 (BETTER) + + +🏆 CURRENT LEADERBOARD: + 1. a_only 4.4178 (vs baseline: -0.2563) + 2. ones_identity 4.4869 (vs baseline: -0.1872) + 3. random_normal 4.4982 (vs baseline: -0.1759) + 4. random_uniform 4.5238 (vs baseline: -0.1503) + 5. bernoulli_drop 4.6653 (vs baseline: -0.0088) + + +==================================================================================================== +🎲 FRIDAY NIGHT SCIENCE COMPLETE 🎲 +==================================================================================================== +Results CSV: friday_night_runs_20260321_0104.csv +Summary: friday_night_summary_20260321_0104.txt + +Paste summary here. AdamW is waiting at the casino. +PS C:\Users\brian\documents\nanogpt> python runner_a.py +==================================================================================================== +RUNNER A: BASELINE vs ONES_IDENTITY +==================================================================================================== +Variants: ['baseline', 'ones_identity'] +Seeds: [42, 1337, 123] +Warmup: 2000 +Total runs: 6 (~390 min) +Key question: does ones_identity match baseline in a matched session? +==================================================================================================== + +==================================================================================================== +RUNNING: baseline | warmup=2000 | seed=42 +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9665, val loss 10.9665 +iter 0: loss 10.9808, time 17888.30ms, mfu -100.00% +step 250: train loss 7.0175, val loss 7.0115 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 250: loss 7.1149, time 19199.18ms, mfu 0.05% +step 500: train loss 6.5239, val loss 6.5303 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 500: loss 6.1699, time 19185.85ms, mfu 0.05% +step 750: train loss 6.2957, val loss 6.3274 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 750: loss 6.6361, time 21174.84ms, mfu 0.05% +step 1000: train loss 6.1675, val loss 6.1643 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 1000: loss 5.8305, time 21214.83ms, mfu 0.05% +step 1250: train loss 6.0317, val loss 6.0476 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 1250: loss 6.5294, time 20640.33ms, mfu 0.05% +step 1500: train loss 5.9851, val loss 5.9775 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 1500: loss 5.4867, time 22049.87ms, mfu 0.05% +step 1750: train loss 5.8954, val loss 5.9059 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 1750: loss 6.1793, time 23403.67ms, mfu 0.05% +step 2000: train loss 5.8273, val loss 5.8393 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 2000: loss 5.6539, time 20776.93ms, mfu 0.05% +step 2250: train loss 5.6924, val loss 5.7037 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 2250: loss 5.7703, time 20633.45ms, mfu 0.05% +step 2500: train loss 5.6188, val loss 5.6119 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 2500: loss 4.9257, time 20715.12ms, mfu 0.05% +step 2750: train loss 5.5243, val loss 5.5248 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 2750: loss 5.5550, time 21200.38ms, mfu 0.05% +step 3000: train loss 5.4575, val loss 5.4454 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 3000: loss 5.4739, time 20736.50ms, mfu 0.05% +step 3250: train loss 5.4122, val loss 5.3722 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 3250: loss 5.6011, time 20793.80ms, mfu 0.05% +step 3500: train loss 5.3165, val loss 5.3672 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 3500: loss 5.4488, time 20444.79ms, mfu 0.05% +step 3750: train loss 5.2798, val loss 5.3050 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 3750: loss 5.3120, time 20630.28ms, mfu 0.05% +step 4000: train loss 5.2333, val loss 5.2411 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 4000: loss 5.9110, time 18828.76ms, mfu 0.05% +step 4250: train loss 5.1944, val loss 5.1792 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 4250: loss 4.9281, time 19594.20ms, mfu 0.05% +step 4500: train loss 5.1066, val loss 5.1358 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 4500: loss 5.2170, time 19008.83ms, mfu 0.05% +step 4750: train loss 5.0832, val loss 5.0902 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 4750: loss 5.1838, time 19039.66ms, mfu 0.05% +step 5000: train loss 5.0364, val loss 5.0135 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 5000: loss 5.4528, time 19908.30ms, mfu 0.05% +step 5250: train loss 4.9636, val loss 4.9655 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 5250: loss 5.0849, time 18907.70ms, mfu 0.05% +step 5500: train loss 4.9479, val loss 4.9356 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 5500: loss 4.7023, time 19648.90ms, mfu 0.05% +step 5750: train loss 4.8727, val loss 4.8832 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 5750: loss 5.1146, time 19296.39ms, mfu 0.05% +step 6000: train loss 4.8184, val loss 4.8257 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 6000: loss 4.7856, time 19170.65ms, mfu 0.05% +step 6250: train loss 4.7695, val loss 4.7868 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 6250: loss 4.4698, time 19906.15ms, mfu 0.05% +step 6500: train loss 4.7726, val loss 4.7507 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 6500: loss 4.6141, time 18782.03ms, mfu 0.05% +step 6750: train loss 4.7084, val loss 4.7025 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 6750: loss 4.6901, time 18843.20ms, mfu 0.05% +step 7000: train loss 4.6585, val loss 4.6840 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 7000: loss 4.6302, time 18745.41ms, mfu 0.05% +step 7250: train loss 4.6471, val loss 4.6454 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 7250: loss 4.8546, time 18856.69ms, mfu 0.05% +step 7500: train loss 4.6437, val loss 4.6243 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 7500: loss 4.7646, time 18804.38ms, mfu 0.05% +step 7750: train loss 4.6183, val loss 4.5965 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 7750: loss 4.7059, time 18810.63ms, mfu 0.05% +step 8000: train loss 4.5758, val loss 4.5779 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 8000: loss 4.5446, time 18708.01ms, mfu 0.05% +step 8250: train loss 4.5302, val loss 4.5550 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 8250: loss 4.3689, time 18884.13ms, mfu 0.05% +step 8500: train loss 4.5437, val loss 4.5728 +iter 8500: loss 4.3966, time 17218.23ms, mfu 0.06% +step 8750: train loss 4.4975, val loss 4.5191 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 8750: loss 4.6587, time 18713.98ms, mfu 0.06% +step 9000: train loss 4.5112, val loss 4.5331 +iter 9000: loss 4.4983, time 17158.47ms, mfu 0.06% +step 9250: train loss 4.4886, val loss 4.4796 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 9250: loss 4.5997, time 18794.27ms, mfu 0.06% +step 9500: train loss 4.4615, val loss 4.4768 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 9500: loss 4.2009, time 18720.02ms, mfu 0.06% +step 9750: train loss 4.4578, val loss 4.4696 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_runnerA +iter 9750: loss 4.7709, time 18721.54ms, mfu 0.06% +step 10000: train loss 4.4567, val loss 4.4830 +iter 10000: loss 4.4498, time 17168.74ms, mfu 0.06% + +--> RESULT | baseline | seed=42 | BEST=4.4696 | FINAL=4.4830 | ✗(best@9750) + + +==================================================================================================== +RUNNING: baseline | warmup=2000 | seed=1337 +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9989, val loss 10.9985 +iter 0: loss 11.0107, time 17919.10ms, mfu -100.00% +step 250: train loss 6.9436, val loss 6.9764 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 250: loss 6.7373, time 18985.52ms, mfu 0.06% +step 500: train loss 6.4863, val loss 6.5542 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 500: loss 6.2684, time 19600.23ms, mfu 0.06% +step 750: train loss 6.3186, val loss 6.3359 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 750: loss 6.1053, time 19893.04ms, mfu 0.05% +step 1000: train loss 6.1820, val loss 6.1550 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 1000: loss 6.1547, time 19668.29ms, mfu 0.05% +step 1250: train loss 6.0811, val loss 6.0745 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 1250: loss 6.1269, time 19644.29ms, mfu 0.05% +step 1500: train loss 6.0073, val loss 5.9324 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 1500: loss 5.7266, time 19544.98ms, mfu 0.05% +step 1750: train loss 5.9400, val loss 5.9033 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 1750: loss 6.1539, time 19797.56ms, mfu 0.05% +step 2000: train loss 5.8410, val loss 5.8078 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 2000: loss 5.8693, time 23018.28ms, mfu 0.05% +step 2250: train loss 5.7181, val loss 5.7260 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 2250: loss 5.9257, time 20137.55ms, mfu 0.05% +step 2500: train loss 5.6180, val loss 5.5971 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 2500: loss 6.0807, time 19673.42ms, mfu 0.05% +step 2750: train loss 5.5307, val loss 5.5265 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 2750: loss 5.5202, time 19545.28ms, mfu 0.05% +step 3000: train loss 5.4597, val loss 5.4769 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 3000: loss 5.5519, time 19624.86ms, mfu 0.05% +step 3250: train loss 5.3828, val loss 5.3877 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 3250: loss 5.3533, time 19961.02ms, mfu 0.05% +step 3500: train loss 5.3888, val loss 5.3522 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 3500: loss 5.2849, time 19384.52ms, mfu 0.05% +step 3750: train loss 5.2976, val loss 5.2829 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 3750: loss 5.5712, time 19254.78ms, mfu 0.05% +step 4000: train loss 5.2376, val loss 5.2463 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 4000: loss 5.3361, time 19193.61ms, mfu 0.05% +step 4250: train loss 5.1905, val loss 5.2040 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 4250: loss 4.7806, time 19700.92ms, mfu 0.05% +step 4500: train loss 5.1521, val loss 5.1456 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 4500: loss 4.8837, time 19600.78ms, mfu 0.05% +step 4750: train loss 5.0818, val loss 5.0952 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 4750: loss 5.1983, time 19939.60ms, mfu 0.05% +step 5000: train loss 5.0142, val loss 5.0493 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 5000: loss 5.1795, time 19289.28ms, mfu 0.05% +step 5250: train loss 5.0073, val loss 4.9941 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 5250: loss 4.6609, time 19616.59ms, mfu 0.05% +step 5500: train loss 4.9840, val loss 4.9474 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 5500: loss 5.1627, time 19452.21ms, mfu 0.05% +step 5750: train loss 4.9144, val loss 4.8740 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 5750: loss 5.1857, time 19575.92ms, mfu 0.05% +step 6000: train loss 4.8249, val loss 4.8291 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 6000: loss 4.7614, time 19203.03ms, mfu 0.05% +step 6250: train loss 4.8047, val loss 4.8372 +iter 6250: loss 4.6160, time 17684.48ms, mfu 0.05% +step 6500: train loss 4.7693, val loss 4.8026 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 6500: loss 4.9662, time 19534.19ms, mfu 0.05% +step 6750: train loss 4.7316, val loss 4.7353 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 6750: loss 4.9062, time 19757.51ms, mfu 0.05% +step 7000: train loss 4.6962, val loss 4.6936 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 7000: loss 4.6231, time 19594.92ms, mfu 0.05% +step 7250: train loss 4.6513, val loss 4.6821 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 7250: loss 4.7550, time 20033.96ms, mfu 0.05% +step 7500: train loss 4.6578, val loss 4.6144 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 7500: loss 4.5265, time 19641.06ms, mfu 0.05% +step 7750: train loss 4.6073, val loss 4.6008 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 7750: loss 4.7873, time 19450.36ms, mfu 0.05% +step 8000: train loss 4.5666, val loss 4.5935 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 8000: loss 4.6446, time 19586.85ms, mfu 0.05% +step 8250: train loss 4.5463, val loss 4.5776 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 8250: loss 4.3413, time 19944.64ms, mfu 0.05% +step 8500: train loss 4.5447, val loss 4.5536 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 8500: loss 4.2784, time 19598.75ms, mfu 0.05% +step 8750: train loss 4.5069, val loss 4.5076 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 8750: loss 4.5440, time 19526.49ms, mfu 0.05% +step 9000: train loss 4.5231, val loss 4.5264 +iter 9000: loss 4.5776, time 17695.63ms, mfu 0.05% +step 9250: train loss 4.4822, val loss 4.5141 +iter 9250: loss 4.5899, time 17749.74ms, mfu 0.05% +step 9500: train loss 4.4881, val loss 4.4990 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 9500: loss 4.5222, time 19920.65ms, mfu 0.05% +step 9750: train loss 4.4939, val loss 4.4693 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_runnerA +iter 9750: loss 4.4063, time 19207.03ms, mfu 0.05% +step 10000: train loss 4.4866, val loss 4.5116 +iter 10000: loss 4.5015, time 17705.69ms, mfu 0.05% + +--> RESULT | baseline | seed=1337 | BEST=4.4693 | FINAL=4.5116 | ✗(best@9750) + + +baseline running mean (2/3 seeds): 4.4973 ± 0.0143 + + +==================================================================================================== +RUNNING: baseline | warmup=2000 | seed=123 +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9288, val loss 10.9279 +iter 0: loss 10.9503, time 17897.66ms, mfu -100.00% +step 250: train loss 6.9747, val loss 6.9201 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 250: loss 6.6646, time 19024.85ms, mfu 0.06% +step 500: train loss 6.4966, val loss 6.5719 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 500: loss 6.6018, time 19266.12ms, mfu 0.06% +step 750: train loss 6.3138, val loss 6.3185 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 750: loss 5.6491, time 19891.45ms, mfu 0.05% +step 1000: train loss 6.1937, val loss 6.1934 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 1000: loss 6.5097, time 19180.75ms, mfu 0.05% +step 1250: train loss 6.0261, val loss 6.0556 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 1250: loss 5.7604, time 19246.21ms, mfu 0.05% +step 1500: train loss 5.9998, val loss 5.9700 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 1500: loss 5.8054, time 19467.57ms, mfu 0.05% +step 1750: train loss 5.8855, val loss 5.9267 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 1750: loss 6.5041, time 22633.76ms, mfu 0.05% +step 2000: train loss 5.8247, val loss 5.8621 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 2000: loss 5.7394, time 20888.72ms, mfu 0.05% +step 2250: train loss 5.6716, val loss 5.7294 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 2250: loss 6.1674, time 19349.16ms, mfu 0.05% +step 2500: train loss 5.6305, val loss 5.6367 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 2500: loss 5.8790, time 19234.42ms, mfu 0.05% +step 2750: train loss 5.5697, val loss 5.5452 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 2750: loss 6.0755, time 19550.29ms, mfu 0.05% +step 3000: train loss 5.4956, val loss 5.4923 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 3000: loss 6.2319, time 19538.32ms, mfu 0.05% +step 3250: train loss 5.3982, val loss 5.3979 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 3250: loss 5.3406, time 20330.33ms, mfu 0.05% +step 3500: train loss 5.3387, val loss 5.3891 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 3500: loss 5.3791, time 19174.76ms, mfu 0.05% +step 3750: train loss 5.3007, val loss 5.2833 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 3750: loss 5.0658, time 19806.48ms, mfu 0.05% +step 4000: train loss 5.2627, val loss 5.2634 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 4000: loss 5.1609, time 19731.99ms, mfu 0.05% +step 4250: train loss 5.2080, val loss 5.2184 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 4250: loss 5.2539, time 21077.39ms, mfu 0.05% +step 4500: train loss 5.1283, val loss 5.1331 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 4500: loss 5.1794, time 19640.76ms, mfu 0.05% +step 4750: train loss 5.1080, val loss 5.1082 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 4750: loss 5.0142, time 19612.74ms, mfu 0.05% +step 5000: train loss 5.0246, val loss 5.0224 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 5000: loss 5.1338, time 19877.09ms, mfu 0.05% +step 5250: train loss 5.0009, val loss 4.9928 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 5250: loss 5.1531, time 19759.12ms, mfu 0.05% +step 5500: train loss 4.9210, val loss 4.9635 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 5500: loss 4.8428, time 19213.10ms, mfu 0.05% +step 5750: train loss 4.9316, val loss 4.9135 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 5750: loss 5.2882, time 19289.91ms, mfu 0.05% +step 6000: train loss 4.8768, val loss 4.8446 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 6000: loss 4.8927, time 19348.69ms, mfu 0.05% +step 6250: train loss 4.8049, val loss 4.8557 +iter 6250: loss 5.0599, time 17721.73ms, mfu 0.05% +step 6500: train loss 4.7696, val loss 4.7791 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 6500: loss 4.6525, time 19345.43ms, mfu 0.05% +step 6750: train loss 4.7252, val loss 4.7473 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 6750: loss 4.3385, time 19354.81ms, mfu 0.05% +step 7000: train loss 4.6937, val loss 4.6998 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 7000: loss 4.5977, time 19547.64ms, mfu 0.05% +step 7250: train loss 4.6646, val loss 4.6915 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 7250: loss 4.2494, time 19857.68ms, mfu 0.05% +step 7500: train loss 4.6701, val loss 4.6531 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 7500: loss 4.6618, time 19915.46ms, mfu 0.05% +step 7750: train loss 4.6267, val loss 4.6243 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 7750: loss 4.8065, time 19688.00ms, mfu 0.05% +step 8000: train loss 4.6000, val loss 4.5951 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 8000: loss 4.1773, time 19585.64ms, mfu 0.05% +step 8250: train loss 4.5562, val loss 4.5539 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 8250: loss 4.3692, time 19811.45ms, mfu 0.05% +step 8500: train loss 4.5394, val loss 4.5593 +iter 8500: loss 4.3102, time 17680.01ms, mfu 0.05% +step 8750: train loss 4.5187, val loss 4.5339 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 8750: loss 4.3807, time 19554.85ms, mfu 0.05% +step 9000: train loss 4.5352, val loss 4.5171 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 9000: loss 4.3033, time 19250.80ms, mfu 0.05% +step 9250: train loss 4.4806, val loss 4.5101 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 9250: loss 5.0109, time 19320.79ms, mfu 0.05% +step 9500: train loss 4.4801, val loss 4.4771 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_runnerA +iter 9500: loss 4.9264, time 19864.40ms, mfu 0.05% +step 9750: train loss 4.5066, val loss 4.5261 +iter 9750: loss 4.4015, time 17692.95ms, mfu 0.05% +step 10000: train loss 4.4889, val loss 4.4974 +iter 10000: loss 4.6353, time 17709.04ms, mfu 0.05% + +--> RESULT | baseline | seed=123 | BEST=4.4771 | FINAL=4.4974 | ✗(best@9500) + + +baseline running mean (3/3 seeds): 4.4973 ± 0.0117 + + +==================================================================================================== +RUNNING: ones_identity | warmup=2000 | seed=42 +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9665, val loss 10.9665 +iter 0: loss 10.9808, time 17318.36ms, mfu -100.00% +step 250: train loss 7.0175, val loss 7.0115 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 250: loss 7.1152, time 18574.61ms, mfu 0.06% +step 500: train loss 6.5238, val loss 6.5301 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 500: loss 6.1700, time 19177.87ms, mfu 0.06% +step 750: train loss 6.2963, val loss 6.3279 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 750: loss 6.6326, time 18874.32ms, mfu 0.06% +step 1000: train loss 6.1696, val loss 6.1656 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 1000: loss 5.8210, time 19249.99ms, mfu 0.06% +step 1250: train loss 6.0233, val loss 6.0406 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 1250: loss 6.5270, time 18818.11ms, mfu 0.06% +step 1500: train loss 5.9831, val loss 5.9763 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 1500: loss 5.4906, time 18926.31ms, mfu 0.06% +step 1750: train loss 5.8947, val loss 5.9011 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 1750: loss 6.1784, time 18673.54ms, mfu 0.06% +step 2000: train loss 5.8273, val loss 5.8414 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 2000: loss 5.6413, time 20921.21ms, mfu 0.06% +step 2250: train loss 5.6808, val loss 5.6934 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 2250: loss 5.7707, time 20356.45ms, mfu 0.05% +step 2500: train loss 5.6277, val loss 5.6212 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 2500: loss 4.9190, time 20735.19ms, mfu 0.05% +step 2750: train loss 5.5340, val loss 5.5329 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 2750: loss 5.5582, time 19887.37ms, mfu 0.05% +step 3000: train loss 5.4531, val loss 5.4414 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 3000: loss 5.5078, time 18688.86ms, mfu 0.05% +step 3250: train loss 5.4106, val loss 5.3700 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 3250: loss 5.5909, time 18779.27ms, mfu 0.05% +step 3500: train loss 5.3177, val loss 5.3686 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 3500: loss 5.4471, time 18678.59ms, mfu 0.05% +step 3750: train loss 5.2870, val loss 5.3122 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 3750: loss 5.3398, time 18817.80ms, mfu 0.05% +step 4000: train loss 5.2315, val loss 5.2373 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 4000: loss 5.8940, time 18829.20ms, mfu 0.05% +step 4250: train loss 5.1920, val loss 5.1772 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 4250: loss 4.9181, time 18721.78ms, mfu 0.06% +step 4500: train loss 5.1060, val loss 5.1320 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 4500: loss 5.2219, time 18735.44ms, mfu 0.06% +step 4750: train loss 5.0803, val loss 5.0898 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 4750: loss 5.1692, time 18731.95ms, mfu 0.06% +step 5000: train loss 5.0353, val loss 5.0107 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 5000: loss 5.3738, time 19255.37ms, mfu 0.06% +step 5250: train loss 4.9674, val loss 4.9676 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 5250: loss 5.0793, time 18740.26ms, mfu 0.06% +step 5500: train loss 4.9443, val loss 4.9333 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 5500: loss 4.6795, time 18777.98ms, mfu 0.06% +step 5750: train loss 4.8694, val loss 4.8817 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 5750: loss 5.1074, time 18698.16ms, mfu 0.06% +step 6000: train loss 4.8187, val loss 4.8289 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 6000: loss 4.7846, time 18706.92ms, mfu 0.06% +step 6250: train loss 4.7726, val loss 4.7899 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 6250: loss 4.4879, time 18811.35ms, mfu 0.06% +step 6500: train loss 4.7745, val loss 4.7576 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 6500: loss 4.6343, time 18811.26ms, mfu 0.06% +step 6750: train loss 4.7084, val loss 4.7017 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 6750: loss 4.7059, time 18826.56ms, mfu 0.06% +step 7000: train loss 4.6581, val loss 4.6845 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 7000: loss 4.6542, time 18736.72ms, mfu 0.06% +step 7250: train loss 4.6517, val loss 4.6479 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 7250: loss 4.8615, time 18768.70ms, mfu 0.06% +step 7500: train loss 4.6448, val loss 4.6260 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 7500: loss 4.7541, time 18854.78ms, mfu 0.06% +step 7750: train loss 4.6198, val loss 4.5982 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 7750: loss 4.7304, time 18745.11ms, mfu 0.06% +step 8000: train loss 4.5788, val loss 4.5801 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 8000: loss 4.5479, time 19119.05ms, mfu 0.06% +step 8250: train loss 4.5330, val loss 4.5568 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 8250: loss 4.3730, time 18823.79ms, mfu 0.06% +step 8500: train loss 4.5447, val loss 4.5747 +iter 8500: loss 4.3948, time 17165.27ms, mfu 0.06% +step 8750: train loss 4.4979, val loss 4.5218 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 8750: loss 4.6721, time 18822.39ms, mfu 0.06% +step 9000: train loss 4.5135, val loss 4.5339 +iter 9000: loss 4.5131, time 17247.33ms, mfu 0.06% +step 9250: train loss 4.4902, val loss 4.4809 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 9250: loss 4.6120, time 19067.60ms, mfu 0.06% +step 9500: train loss 4.4641, val loss 4.4801 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 9500: loss 4.2152, time 18770.20ms, mfu 0.06% +step 9750: train loss 4.4603, val loss 4.4715 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed42_runnerA +iter 9750: loss 4.7712, time 18677.65ms, mfu 0.06% +step 10000: train loss 4.4583, val loss 4.4859 +iter 10000: loss 4.4641, time 17186.99ms, mfu 0.06% + +--> RESULT | ones_identity | seed=42 | BEST=4.4715 | FINAL=4.4859 | ✗(best@9750) + + +==================================================================================================== +RUNNING: ones_identity | warmup=2000 | seed=1337 +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9989, val loss 10.9985 +iter 0: loss 11.0107, time 17378.46ms, mfu -100.00% +step 250: train loss 6.9435, val loss 6.9764 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 250: loss 6.7373, time 18469.50ms, mfu 0.06% +step 500: train loss 6.4863, val loss 6.5542 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 500: loss 6.2682, time 19096.41ms, mfu 0.06% +step 750: train loss 6.3163, val loss 6.3333 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 750: loss 6.1007, time 18714.76ms, mfu 0.06% +step 1000: train loss 6.1806, val loss 6.1519 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 1000: loss 6.1608, time 18757.67ms, mfu 0.06% +step 1250: train loss 6.0765, val loss 6.0706 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 1250: loss 6.1242, time 19466.95ms, mfu 0.06% +step 1500: train loss 6.0172, val loss 5.9419 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 1500: loss 5.7323, time 19881.55ms, mfu 0.06% +step 1750: train loss 5.9296, val loss 5.8947 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 1750: loss 6.1025, time 19664.96ms, mfu 0.06% +step 2000: train loss 5.8300, val loss 5.8006 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 2000: loss 5.8389, time 19803.31ms, mfu 0.06% +step 2250: train loss 5.6985, val loss 5.7049 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 2250: loss 5.9211, time 20164.44ms, mfu 0.05% +step 2500: train loss 5.6123, val loss 5.5917 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 2500: loss 6.0983, time 18756.07ms, mfu 0.06% +step 2750: train loss 5.5338, val loss 5.5287 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 2750: loss 5.5367, time 19118.56ms, mfu 0.06% +step 3000: train loss 5.4527, val loss 5.4684 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 3000: loss 5.5251, time 18794.79ms, mfu 0.06% +step 3250: train loss 5.3732, val loss 5.3791 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 3250: loss 5.3459, time 18645.50ms, mfu 0.06% +step 3500: train loss 5.3744, val loss 5.3376 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 3500: loss 5.2605, time 19818.52ms, mfu 0.05% +step 3750: train loss 5.2848, val loss 5.2727 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 3750: loss 5.5826, time 18811.83ms, mfu 0.06% +step 4000: train loss 5.2379, val loss 5.2458 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 4000: loss 5.3189, time 18819.47ms, mfu 0.06% +step 4250: train loss 5.1859, val loss 5.1994 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 4250: loss 4.7712, time 19105.92ms, mfu 0.06% +step 4500: train loss 5.1471, val loss 5.1405 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 4500: loss 4.8223, time 18975.82ms, mfu 0.06% +step 4750: train loss 5.0776, val loss 5.0894 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 4750: loss 5.2042, time 18677.60ms, mfu 0.06% +step 5000: train loss 5.0081, val loss 5.0438 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 5000: loss 5.2094, time 18859.00ms, mfu 0.06% +step 5250: train loss 5.0008, val loss 4.9899 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 5250: loss 4.6705, time 18861.31ms, mfu 0.06% +step 5500: train loss 4.9659, val loss 4.9330 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 5500: loss 5.1567, time 19067.99ms, mfu 0.06% +step 5750: train loss 4.9039, val loss 4.8680 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 5750: loss 5.2146, time 18842.06ms, mfu 0.06% +step 6000: train loss 4.8166, val loss 4.8262 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 6000: loss 4.7329, time 18730.05ms, mfu 0.06% +step 6250: train loss 4.7958, val loss 4.8288 +iter 6250: loss 4.6008, time 17169.53ms, mfu 0.06% +step 6500: train loss 4.7596, val loss 4.7957 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 6500: loss 4.9598, time 18755.73ms, mfu 0.06% +step 6750: train loss 4.7226, val loss 4.7290 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 6750: loss 4.8957, time 18945.42ms, mfu 0.06% +step 7000: train loss 4.6911, val loss 4.6864 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 7000: loss 4.5940, time 18952.58ms, mfu 0.06% +step 7250: train loss 4.6482, val loss 4.6783 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 7250: loss 4.7443, time 18863.09ms, mfu 0.06% +step 7500: train loss 4.6543, val loss 4.6125 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 7500: loss 4.5061, time 18865.59ms, mfu 0.06% +step 7750: train loss 4.6026, val loss 4.5979 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 7750: loss 4.7710, time 18958.31ms, mfu 0.06% +step 8000: train loss 4.5602, val loss 4.5850 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 8000: loss 4.6363, time 18799.97ms, mfu 0.06% +step 8250: train loss 4.5404, val loss 4.5728 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 8250: loss 4.3202, time 18666.94ms, mfu 0.06% +step 8500: train loss 4.5413, val loss 4.5523 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 8500: loss 4.2764, time 18801.14ms, mfu 0.06% +step 8750: train loss 4.5030, val loss 4.5062 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 8750: loss 4.5469, time 18766.89ms, mfu 0.06% +step 9000: train loss 4.5165, val loss 4.5220 +iter 9000: loss 4.5953, time 17183.10ms, mfu 0.06% +step 9250: train loss 4.4792, val loss 4.5079 +iter 9250: loss 4.5484, time 17195.93ms, mfu 0.06% +step 9500: train loss 4.4842, val loss 4.4953 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 9500: loss 4.5176, time 18704.48ms, mfu 0.06% +step 9750: train loss 4.4896, val loss 4.4640 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_runnerA +iter 9750: loss 4.3785, time 18840.58ms, mfu 0.06% +step 10000: train loss 4.4822, val loss 4.5081 +iter 10000: loss 4.5107, time 17163.39ms, mfu 0.06% + +--> RESULT | ones_identity | seed=1337 | BEST=4.4640 | FINAL=4.5081 | ✗(best@9750) + + +ones_identity running mean (2/3 seeds): 4.4970 ± 0.0111 + + +==================================================================================================== +RUNNING: ones_identity | warmup=2000 | seed=123 +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9288, val loss 10.9279 +iter 0: loss 10.9503, time 17363.93ms, mfu -100.00% +step 250: train loss 6.9746, val loss 6.9200 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 250: loss 6.6643, time 18443.39ms, mfu 0.06% +step 500: train loss 6.4966, val loss 6.5719 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 500: loss 6.6017, time 19124.48ms, mfu 0.06% +step 750: train loss 6.3137, val loss 6.3184 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 750: loss 5.6491, time 18643.11ms, mfu 0.06% +step 1000: train loss 6.1891, val loss 6.1887 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 1000: loss 6.5081, time 18844.73ms, mfu 0.06% +step 1250: train loss 6.0336, val loss 6.0654 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 1250: loss 5.7690, time 18940.39ms, mfu 0.06% +step 1500: train loss 6.0038, val loss 5.9754 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 1500: loss 5.8202, time 19562.90ms, mfu 0.06% +step 1750: train loss 5.8878, val loss 5.9295 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 1750: loss 6.5336, time 19872.76ms, mfu 0.06% +step 2000: train loss 5.8370, val loss 5.8739 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 2000: loss 5.7710, time 18719.12ms, mfu 0.06% +step 2250: train loss 5.6822, val loss 5.7384 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 2250: loss 6.1838, time 18941.46ms, mfu 0.06% +step 2500: train loss 5.6433, val loss 5.6500 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 2500: loss 5.8612, time 18767.87ms, mfu 0.06% +step 2750: train loss 5.5808, val loss 5.5517 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 2750: loss 6.0680, time 19067.09ms, mfu 0.06% +step 3000: train loss 5.5020, val loss 5.5060 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 3000: loss 6.2222, time 18778.49ms, mfu 0.06% +step 3250: train loss 5.4077, val loss 5.4034 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 3250: loss 5.3482, time 18761.60ms, mfu 0.06% +step 3500: train loss 5.3399, val loss 5.3900 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 3500: loss 5.3865, time 19100.33ms, mfu 0.06% +step 3750: train loss 5.2960, val loss 5.2812 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 3750: loss 5.0760, time 18744.22ms, mfu 0.06% +step 4000: train loss 5.2544, val loss 5.2615 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 4000: loss 5.1356, time 19176.43ms, mfu 0.06% +step 4250: train loss 5.2172, val loss 5.2258 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 4250: loss 5.2564, time 19524.76ms, mfu 0.06% +step 4500: train loss 5.1357, val loss 5.1376 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 4500: loss 5.1874, time 18716.62ms, mfu 0.06% +step 4750: train loss 5.1039, val loss 5.1020 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 4750: loss 5.0024, time 19630.28ms, mfu 0.06% +step 5000: train loss 5.0217, val loss 5.0199 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 5000: loss 5.1541, time 19770.70ms, mfu 0.05% +step 5250: train loss 4.9991, val loss 4.9921 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 5250: loss 5.1729, time 19256.87ms, mfu 0.05% +step 5500: train loss 4.9153, val loss 4.9573 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 5500: loss 4.8550, time 19269.48ms, mfu 0.05% +step 5750: train loss 4.9348, val loss 4.9178 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 5750: loss 5.2525, time 19069.83ms, mfu 0.05% +step 6000: train loss 4.8757, val loss 4.8445 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 6000: loss 4.8825, time 19867.52ms, mfu 0.05% +step 6250: train loss 4.8043, val loss 4.8549 +iter 6250: loss 5.0592, time 17615.98ms, mfu 0.06% +step 6500: train loss 4.7726, val loss 4.7804 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 6500: loss 4.6401, time 19300.65ms, mfu 0.06% +step 6750: train loss 4.7285, val loss 4.7507 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 6750: loss 4.3599, time 20083.18ms, mfu 0.05% +step 7000: train loss 4.6931, val loss 4.7008 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 7000: loss 4.6262, time 19284.98ms, mfu 0.05% +step 7250: train loss 4.6657, val loss 4.6949 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 7250: loss 4.2593, time 19736.91ms, mfu 0.05% +step 7500: train loss 4.6706, val loss 4.6535 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 7500: loss 4.6464, time 20518.45ms, mfu 0.05% +step 7750: train loss 4.6229, val loss 4.6229 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 7750: loss 4.8218, time 18899.05ms, mfu 0.05% +step 8000: train loss 4.5980, val loss 4.5920 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 8000: loss 4.1532, time 18897.64ms, mfu 0.05% +step 8250: train loss 4.5547, val loss 4.5522 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 8250: loss 4.3467, time 19021.85ms, mfu 0.05% +step 8500: train loss 4.5411, val loss 4.5613 +iter 8500: loss 4.3247, time 17303.84ms, mfu 0.06% +step 8750: train loss 4.5174, val loss 4.5308 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 8750: loss 4.3966, time 18866.05ms, mfu 0.06% +step 9000: train loss 4.5330, val loss 4.5142 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 9000: loss 4.3154, time 18899.94ms, mfu 0.06% +step 9250: train loss 4.4808, val loss 4.5090 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 9250: loss 5.0252, time 18993.53ms, mfu 0.06% +step 9500: train loss 4.4793, val loss 4.4761 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_runnerA +iter 9500: loss 4.9437, time 19130.47ms, mfu 0.06% +step 9750: train loss 4.5031, val loss 4.5271 +iter 9750: loss 4.4170, time 17269.21ms, mfu 0.06% +step 10000: train loss 4.4881, val loss 4.4980 +iter 10000: loss 4.6129, time 17230.90ms, mfu 0.06% + +--> RESULT | ones_identity | seed=123 | BEST=4.4761 | FINAL=4.4980 | ✗(best@9500) + + +ones_identity running mean (3/3 seeds): 4.4973 ± 0.0091 + + +==================================================================================================== +RUNNER A COMPLETE +==================================================================================================== +Results CSV: runner_a_runs_20260321_0903.csv +Summary: runner_a_summary_20260321_0903.txt +Paste summary here. +PS C:\Users\brian\documents\nanogpt> python runner_b.py +==================================================================================================== +RUNNER B: A-ONLY 3-SEED CONFIRMATION +==================================================================================================== +Variant: dynamic_a_conditioned +Seeds: [42, 1337, 123] +Warmup: 2000 +Total runs: 3 (~195 min) + +Known seed-42 results from prior clean runs: + run 1: 4.4167 + run 2: 4.4178 + mean: 4.4172 + +Key questions: + Does A-only beat cheap Q (4.5533) across all seeds? + Does cheap Q+A (4.4073) still win? + What is the canonical 3-seed mean? +==================================================================================================== + +==================================================================================================== +RUNNING: a_only | warmup=2000 | seed=42 +Gate: per-head Linear(hs->hs) on y (SDPA output) only +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.64M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 124,010,496 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9645, val loss 10.9616 +iter 0: loss 10.9448, time 18303.14ms, mfu -100.00% +step 250: train loss 6.9237, val loss 6.9409 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 250: loss 6.8588, time 19735.91ms, mfu 0.05% +step 500: train loss 6.4782, val loss 6.5235 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 500: loss 6.2176, time 20143.11ms, mfu 0.05% +step 750: train loss 6.3198, val loss 6.3307 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 750: loss 6.2963, time 20368.78ms, mfu 0.05% +step 1000: train loss 6.1628, val loss 6.1867 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 1000: loss 6.6581, time 19896.12ms, mfu 0.05% +step 1250: train loss 6.0425, val loss 6.0801 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 1250: loss 5.4783, time 20124.19ms, mfu 0.05% +step 1500: train loss 5.9335, val loss 5.9834 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 1500: loss 6.2472, time 20975.19ms, mfu 0.05% +step 1750: train loss 5.9029, val loss 5.9050 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 1750: loss 5.2478, time 23404.36ms, mfu 0.05% +step 2000: train loss 5.7802, val loss 5.7839 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 2000: loss 5.9543, time 22461.80ms, mfu 0.05% +step 2250: train loss 5.6618, val loss 5.6727 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 2250: loss 5.5409, time 20810.76ms, mfu 0.05% +step 2500: train loss 5.5572, val loss 5.5740 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 2500: loss 5.8345, time 19690.35ms, mfu 0.05% +step 2750: train loss 5.5106, val loss 5.4931 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 2750: loss 5.0950, time 20122.93ms, mfu 0.05% +step 3000: train loss 5.3823, val loss 5.4329 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 3000: loss 5.5531, time 19995.16ms, mfu 0.05% +step 3250: train loss 5.3716, val loss 5.3931 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 3250: loss 5.1314, time 19711.10ms, mfu 0.05% +step 3500: train loss 5.2893, val loss 5.2874 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 3500: loss 5.4981, time 19567.28ms, mfu 0.05% +step 3750: train loss 5.2526, val loss 5.2785 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 3750: loss 5.0668, time 19443.89ms, mfu 0.05% +step 4000: train loss 5.1852, val loss 5.1571 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 4000: loss 5.2085, time 19517.37ms, mfu 0.05% +step 4250: train loss 5.1313, val loss 5.1182 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 4250: loss 5.3727, time 19591.65ms, mfu 0.05% +step 4500: train loss 5.0535, val loss 5.0615 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 4500: loss 5.0715, time 19450.99ms, mfu 0.05% +step 4750: train loss 5.0224, val loss 5.0340 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 4750: loss 5.2975, time 19587.87ms, mfu 0.05% +step 5000: train loss 5.0008, val loss 4.9823 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 5000: loss 5.2659, time 19597.83ms, mfu 0.05% +step 5250: train loss 4.9092, val loss 4.9436 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 5250: loss 5.0341, time 19762.57ms, mfu 0.05% +step 5500: train loss 4.8701, val loss 4.8597 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 5500: loss 4.7210, time 19676.56ms, mfu 0.05% +step 5750: train loss 4.8073, val loss 4.8277 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 5750: loss 4.8527, time 20002.97ms, mfu 0.05% +step 6000: train loss 4.8083, val loss 4.8127 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 6000: loss 4.4561, time 19834.34ms, mfu 0.05% +step 6250: train loss 4.7543, val loss 4.7466 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 6250: loss 4.7370, time 20040.34ms, mfu 0.05% +step 6500: train loss 4.7083, val loss 4.7261 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 6500: loss 4.8085, time 20299.85ms, mfu 0.05% +step 6750: train loss 4.6595, val loss 4.7045 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 6750: loss 4.9510, time 20310.20ms, mfu 0.05% +step 7000: train loss 4.6469, val loss 4.6661 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 7000: loss 4.3730, time 20458.68ms, mfu 0.05% +step 7250: train loss 4.5758, val loss 4.6390 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 7250: loss 4.1737, time 20100.50ms, mfu 0.05% +step 7500: train loss 4.5705, val loss 4.5702 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 7500: loss 4.5182, time 19933.22ms, mfu 0.05% +step 7750: train loss 4.5425, val loss 4.5825 +iter 7750: loss 4.8334, time 18418.59ms, mfu 0.05% +step 8000: train loss 4.5351, val loss 4.5472 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 8000: loss 4.2734, time 20190.45ms, mfu 0.05% +step 8250: train loss 4.4940, val loss 4.5185 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 8250: loss 4.6872, time 20596.67ms, mfu 0.05% +step 8500: train loss 4.4973, val loss 4.5106 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 8500: loss 4.5323, time 20500.15ms, mfu 0.05% +step 8750: train loss 4.4827, val loss 4.5172 +iter 8750: loss 4.5105, time 18416.75ms, mfu 0.05% +step 9000: train loss 4.4602, val loss 4.4708 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 9000: loss 4.4308, time 20595.67ms, mfu 0.05% +step 9250: train loss 4.4481, val loss 4.4702 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 9250: loss 4.5604, time 20190.19ms, mfu 0.05% +step 9500: train loss 4.4724, val loss 4.4431 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 9500: loss 4.9022, time 20594.97ms, mfu 0.05% +step 9750: train loss 4.4192, val loss 4.4592 +iter 9750: loss 4.1904, time 18369.85ms, mfu 0.05% +step 10000: train loss 4.4266, val loss 4.4180 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed42_runnerB +iter 10000: loss 4.5140, time 20491.63ms, mfu 0.05% + +--> RESULT | a_only | seed=42 | BEST=4.4180 | FINAL=4.4180 | ✓ + + +==================================================================================================== +RUNNING: a_only | warmup=2000 | seed=1337 +Gate: per-head Linear(hs->hs) on y (SDPA output) only +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.64M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 124,010,496 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9316, val loss 10.9312 +iter 0: loss 10.9362, time 18182.78ms, mfu -100.00% +step 250: train loss 6.9043, val loss 6.9314 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB +iter 250: loss 6.7946, time 19400.48ms, mfu 0.05% +step 500: train loss 6.4896, val loss 6.4933 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB +iter 500: loss 6.6438, time 19533.37ms, mfu 0.05% +step 750: train loss 6.3021, val loss 6.3299 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB +iter 750: loss 6.0948, time 19857.48ms, mfu 0.05% +step 1000: train loss 6.1598, val loss 6.1458 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB +iter 1000: loss 6.1078, time 19493.80ms, mfu 0.05% +step 1250: train loss 6.0612, val loss 6.0295 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB +iter 1250: loss 5.6847, time 20284.78ms, mfu 0.05% +step 1500: train loss 5.9362, val loss 5.9616 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB +iter 1500: loss 6.2494, time 19560.41ms, mfu 0.05% +step 1750: train loss 5.8469, val loss 5.8596 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB +iter 1750: loss 5.9621, time 22633.72ms, mfu 0.05% +step 2000: train loss 5.8165, val loss 5.7937 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB +iter 2000: loss 5.8294, time 21837.97ms, mfu 0.05% +step 2250: train loss 5.6944, val loss 5.6426 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB +iter 2250: loss 6.0966, time 19805.24ms, mfu 0.05% +step 2500: train loss 5.6279, val loss 5.5939 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB +iter 2500: loss 5.1778, time 20295.95ms, mfu 0.05% +step 2750: train loss 5.4913, val loss 5.5066 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB +iter 2750: loss 5.8274, time 19476.46ms, mfu 0.05% +step 3000: train loss 5.4009, val loss 5.4166 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB +iter 3000: loss 5.1056, time 20062.31ms, mfu 0.05% +step 3250: train loss 5.3671, val loss 5.3712 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB +iter 3250: loss 5.0795, time 19967.87ms, mfu 0.05% +step 3500: train loss 5.3009, val loss 5.2824 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB +iter 3500: loss 5.3980, time 19666.46ms, mfu 0.05% +step 3750: train loss 5.2431, val loss 5.2571 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB +iter 3750: loss 5.3293, time 19550.29ms, mfu 0.05% +step 4000: train loss 5.1742, val loss 5.1954 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB +iter 4000: loss 5.0732, time 19560.02ms, mfu 0.05% +step 4250: train loss 5.1643, val loss 5.1658 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB +iter 4250: loss 4.9756, time 19966.97ms, mfu 0.05% +step 4500: train loss 5.0850, val loss 5.1161 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB +iter 4500: loss 5.5509, time 19987.54ms, mfu 0.05% +step 4750: train loss 5.0088, val loss 5.0466 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB +iter 4750: loss 5.4157, time 19758.54ms, mfu 0.05% +step 5000: train loss 4.9841, val loss 4.9957 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB +iter 5000: loss 4.9262, time 20608.98ms, mfu 0.05% +step 5250: train loss 4.9390, val loss 4.9296 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB +iter 5250: loss 4.6263, time 19461.42ms, mfu 0.05% +step 5500: train loss 4.8792, val loss 4.8833 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB +iter 5500: loss 4.7258, time 19574.29ms, mfu 0.05% +step 5750: train loss 4.8535, val loss 4.8663 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB +iter 5750: loss 4.7199, time 19616.70ms, mfu 0.05% +step 6000: train loss 4.7817, val loss 4.7981 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB +iter 6000: loss 4.9038, time 19538.38ms, mfu 0.05% +step 6250: train loss 4.7672, val loss 4.7794 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB +iter 6250: loss 4.6126, time 20105.67ms, mfu 0.05% +step 6500: train loss 4.7089, val loss 4.7449 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB +iter 6500: loss 4.6871, time 20248.32ms, mfu 0.05% +step 6750: train loss 4.6679, val loss 4.6955 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB +iter 6750: loss 4.8140, time 20490.61ms, mfu 0.05% +step 7000: train loss 4.6520, val loss 4.6027 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB +iter 7000: loss 4.1358, time 19949.84ms, mfu 0.05% +step 7250: train loss 4.5790, val loss 4.6449 +iter 7250: loss 4.3707, time 18399.56ms, mfu 0.05% +step 7500: train loss 4.5809, val loss 4.5927 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB +iter 7500: loss 4.9231, time 20035.17ms, mfu 0.05% +step 7750: train loss 4.5707, val loss 4.5458 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB +iter 7750: loss 4.9169, time 20173.57ms, mfu 0.05% +step 8000: train loss 4.5681, val loss 4.5523 +iter 8000: loss 4.4946, time 18443.62ms, mfu 0.05% +step 8250: train loss 4.5244, val loss 4.5082 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB +iter 8250: loss 4.6362, time 20372.94ms, mfu 0.05% +step 8500: train loss 4.4863, val loss 4.4795 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB +iter 8500: loss 4.2299, time 21239.30ms, mfu 0.05% +step 8750: train loss 4.5128, val loss 4.5281 +iter 8750: loss 4.4062, time 18460.46ms, mfu 0.05% +step 9000: train loss 4.4715, val loss 4.5139 +iter 9000: loss 4.7649, time 18500.52ms, mfu 0.05% +step 9250: train loss 4.4652, val loss 4.4404 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_runnerB +iter 9250: loss 4.4272, time 20374.85ms, mfu 0.05% +step 9500: train loss 4.4629, val loss 4.4574 +iter 9500: loss 4.5219, time 18447.32ms, mfu 0.05% +step 9750: train loss 4.4307, val loss 4.4537 +iter 9750: loss 4.5071, time 18443.02ms, mfu 0.05% +step 10000: train loss 4.4210, val loss 4.4413 +iter 10000: loss 4.6795, time 18440.69ms, mfu 0.05% + +--> RESULT | a_only | seed=1337 | BEST=4.4404 | FINAL=4.4413 | ✗(best@9250) + + +A-ONLY RUNNING AGGREGATE (2/3 seeds): + seed 42: 4.4180 + seed 1337: 4.4413 + mean: 4.4297 ± 0.0116 + + +==================================================================================================== +RUNNING: a_only | warmup=2000 | seed=123 +Gate: per-head Linear(hs->hs) on y (SDPA output) only +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.64M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 124,010,496 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9089, val loss 10.9095 +iter 0: loss 10.9231, time 18267.05ms, mfu -100.00% +step 250: train loss 6.9507, val loss 6.9767 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 250: loss 7.0426, time 19449.60ms, mfu 0.05% +step 500: train loss 6.5223, val loss 6.5134 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 500: loss 6.2356, time 19866.26ms, mfu 0.05% +step 750: train loss 6.3293, val loss 6.3572 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 750: loss 5.8380, time 19426.60ms, mfu 0.05% +step 1000: train loss 6.1641, val loss 6.1698 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 1000: loss 6.1430, time 19441.79ms, mfu 0.05% +step 1250: train loss 6.0516, val loss 6.0501 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 1250: loss 6.1797, time 20234.20ms, mfu 0.05% +step 1500: train loss 5.9693, val loss 5.9686 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 1500: loss 6.2936, time 22060.35ms, mfu 0.05% +step 1750: train loss 5.8746, val loss 5.8794 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 1750: loss 5.6316, time 20207.57ms, mfu 0.05% +step 2000: train loss 5.7709, val loss 5.7692 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 2000: loss 5.8249, time 19774.67ms, mfu 0.05% +step 2250: train loss 5.6932, val loss 5.7086 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 2250: loss 5.8582, time 19666.78ms, mfu 0.05% +step 2500: train loss 5.6417, val loss 5.6253 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 2500: loss 5.5264, time 19945.87ms, mfu 0.05% +step 2750: train loss 5.4733, val loss 5.5081 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 2750: loss 5.1815, time 19969.62ms, mfu 0.05% +step 3000: train loss 5.4108, val loss 5.3961 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 3000: loss 5.5692, time 20971.44ms, mfu 0.05% +step 3250: train loss 5.3278, val loss 5.3539 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 3250: loss 5.3430, time 20223.36ms, mfu 0.05% +step 3500: train loss 5.3113, val loss 5.3043 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 3500: loss 4.9255, time 20190.48ms, mfu 0.05% +step 3750: train loss 5.2665, val loss 5.2702 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 3750: loss 5.5660, time 19464.21ms, mfu 0.05% +step 4000: train loss 5.1725, val loss 5.1955 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 4000: loss 4.9853, time 20117.07ms, mfu 0.05% +step 4250: train loss 5.1301, val loss 5.1251 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 4250: loss 5.1730, time 20340.95ms, mfu 0.05% +step 4500: train loss 5.0601, val loss 5.0632 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 4500: loss 4.5094, time 20626.86ms, mfu 0.05% +step 4750: train loss 5.0241, val loss 5.0385 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 4750: loss 4.7810, time 20474.00ms, mfu 0.05% +step 5000: train loss 4.9960, val loss 4.9828 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 5000: loss 5.3556, time 21514.39ms, mfu 0.05% +step 5250: train loss 4.9400, val loss 4.9389 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 5250: loss 4.8423, time 19922.83ms, mfu 0.05% +step 5500: train loss 4.9016, val loss 4.9073 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 5500: loss 4.7535, time 20217.15ms, mfu 0.05% +step 5750: train loss 4.8322, val loss 4.8355 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 5750: loss 5.5663, time 20157.28ms, mfu 0.05% +step 6000: train loss 4.7821, val loss 4.7979 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 6000: loss 4.6626, time 20199.84ms, mfu 0.05% +step 6250: train loss 4.7522, val loss 4.7293 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 6250: loss 4.9835, time 20447.45ms, mfu 0.05% +step 6500: train loss 4.7362, val loss 4.7377 +iter 6500: loss 4.6439, time 18426.11ms, mfu 0.05% +step 6750: train loss 4.6343, val loss 4.7134 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 6750: loss 4.5501, time 20449.67ms, mfu 0.05% +step 7000: train loss 4.6285, val loss 4.6730 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 7000: loss 5.0833, time 20456.77ms, mfu 0.05% +step 7250: train loss 4.6452, val loss 4.6482 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 7250: loss 4.6258, time 20408.89ms, mfu 0.05% +step 7500: train loss 4.5751, val loss 4.5878 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 7500: loss 5.0176, time 20652.49ms, mfu 0.05% +step 7750: train loss 4.5632, val loss 4.5773 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 7750: loss 4.7276, time 20501.27ms, mfu 0.05% +step 8000: train loss 4.5284, val loss 4.5419 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 8000: loss 4.3647, time 21696.40ms, mfu 0.05% +step 8250: train loss 4.5361, val loss 4.5291 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 8250: loss 4.4992, time 20211.30ms, mfu 0.05% +step 8500: train loss 4.4854, val loss 4.4825 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 8500: loss 4.4913, time 20440.58ms, mfu 0.05% +step 8750: train loss 4.4814, val loss 4.4988 +iter 8750: loss 4.2762, time 18536.16ms, mfu 0.05% +step 9000: train loss 4.4777, val loss 4.4851 +iter 9000: loss 4.5874, time 18467.06ms, mfu 0.05% +step 9250: train loss 4.4881, val loss 4.4466 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 9250: loss 4.7071, time 20483.86ms, mfu 0.05% +step 9500: train loss 4.4615, val loss 4.4438 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 9500: loss 4.5442, time 20471.19ms, mfu 0.05% +step 9750: train loss 4.4396, val loss 4.4599 +iter 9750: loss 4.1918, time 18442.66ms, mfu 0.05% +step 10000: train loss 4.4314, val loss 4.4300 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_runnerB +iter 10000: loss 4.1076, time 20609.13ms, mfu 0.05% + +--> RESULT | a_only | seed=123 | BEST=4.4300 | FINAL=4.4300 | ✓ + + +A-ONLY RUNNING AGGREGATE (3/3 seeds): + seed 42: 4.4180 + seed 1337: 4.4413 + seed 123: 4.4300 + mean: 4.4298 ± 0.0095 + + +==================================================================================================== +RUNNER B COMPLETE +==================================================================================================== +Results CSV: runner_b_runs_20260321_1545.csv +Summary: runner_b_summary_20260321_1545.txt +Paste summary here for final analysis. +PS C:\Users\brian\documents\nanogpt> python chaos_confirmation_runner.py +==================================================================================================== +🎲 CHAOS CONFIRMATION RUNNER 🎲 +==================================================================================================== +Seeds: [1337, 123] (seed 42 already done) +Warmup: 2000 +Total runs: 12 (~780 min / ~13h) + +Variants: + baseline -- no gate — fresh matched reference seed42=4.4830 + ones_identity -- always 1.0 — identity control seed42=4.4859 + a_only -- learned: per-head Linear(64->64) on y seed42=4.4180 + random_uniform -- chaos: uniform random [0,1] seed42=4.5238 + random_normal -- chaos: normal(0.5,0.2) clamped [0,1] seed42=4.4982 + bernoulli_drop -- spicy: binary 50% dropout x2 gate seed42=4.6653 + +Key question: do random gates beat CLEAN baseline (4.4973)? +friday_night compared to dirty anchor (4.6741) -- results were misleading. +This run settles it properly. + +Predictions: + ones_identity = baseline (confirmed by Runner A, expect same here) + random_* probably ties or loses to clean baseline + a_only confirms ~4.43 mean (Runner B showed 4.4298) + bernoulli_drop probably worse than baseline + prove us wrong, random gate. prove us wrong. +==================================================================================================== + +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 +RUNNING: baseline | seed=1337 + no gate — fresh matched reference +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9989, val loss 10.9985 +iter 0: loss 11.0107, time 17748.30ms, mfu -100.00% +step 250: train loss 6.9435, val loss 6.9764 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 250: loss 6.7377, time 20122.27ms, mfu 0.05% +step 500: train loss 6.4863, val loss 6.5542 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 500: loss 6.2682, time 20517.09ms, mfu 0.05% +step 750: train loss 6.3185, val loss 6.3358 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 750: loss 6.1049, time 20365.95ms, mfu 0.05% +step 1000: train loss 6.1909, val loss 6.1622 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 1000: loss 6.1722, time 21186.93ms, mfu 0.05% +step 1250: train loss 6.0744, val loss 6.0674 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 1250: loss 6.1168, time 24107.59ms, mfu 0.05% +step 1500: train loss 6.0170, val loss 5.9425 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 1500: loss 5.7308, time 26909.78ms, mfu 0.05% +step 1750: train loss 5.9283, val loss 5.8917 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 1750: loss 6.0931, time 24323.73ms, mfu 0.05% +step 2000: train loss 5.8295, val loss 5.8024 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 2000: loss 5.8730, time 21824.42ms, mfu 0.05% +step 2250: train loss 5.7303, val loss 5.7346 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 2250: loss 5.9421, time 21583.50ms, mfu 0.05% +step 2500: train loss 5.6141, val loss 5.5930 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 2500: loss 6.0541, time 21647.43ms, mfu 0.05% +step 2750: train loss 5.5388, val loss 5.5368 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 2750: loss 5.5252, time 21571.55ms, mfu 0.05% +step 3000: train loss 5.4606, val loss 5.4759 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 3000: loss 5.5441, time 20944.21ms, mfu 0.05% +step 3250: train loss 5.3813, val loss 5.3882 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 3250: loss 5.3727, time 21916.53ms, mfu 0.05% +step 3500: train loss 5.3813, val loss 5.3451 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 3500: loss 5.2736, time 21741.07ms, mfu 0.05% +step 3750: train loss 5.2919, val loss 5.2806 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 3750: loss 5.5847, time 22057.56ms, mfu 0.05% +step 4000: train loss 5.2421, val loss 5.2483 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 4000: loss 5.3261, time 21605.41ms, mfu 0.05% +step 4250: train loss 5.1814, val loss 5.1968 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 4250: loss 4.7865, time 21369.36ms, mfu 0.05% +step 4500: train loss 5.1375, val loss 5.1298 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 4500: loss 4.8208, time 21647.69ms, mfu 0.05% +step 4750: train loss 5.0771, val loss 5.0875 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 4750: loss 5.1896, time 21795.88ms, mfu 0.05% +step 5000: train loss 5.0063, val loss 5.0445 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 5000: loss 5.1970, time 22164.13ms, mfu 0.05% +step 5250: train loss 5.0038, val loss 4.9892 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 5250: loss 4.6436, time 22070.47ms, mfu 0.05% +step 5500: train loss 4.9649, val loss 4.9359 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 5500: loss 5.1590, time 22818.79ms, mfu 0.05% +step 5750: train loss 4.9096, val loss 4.8725 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 5750: loss 5.2010, time 21732.73ms, mfu 0.05% +step 6000: train loss 4.8128, val loss 4.8172 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 6000: loss 4.7420, time 21809.67ms, mfu 0.05% +step 6250: train loss 4.7957, val loss 4.8286 +iter 6250: loss 4.5967, time 18458.06ms, mfu 0.05% +step 6500: train loss 4.7557, val loss 4.7905 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 6500: loss 4.9367, time 21500.82ms, mfu 0.05% +step 6750: train loss 4.7224, val loss 4.7285 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 6750: loss 4.8831, time 22257.81ms, mfu 0.05% +step 7000: train loss 4.6865, val loss 4.6852 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 7000: loss 4.6025, time 22120.74ms, mfu 0.05% +step 7250: train loss 4.6396, val loss 4.6697 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 7250: loss 4.7143, time 21586.87ms, mfu 0.05% +step 7500: train loss 4.6492, val loss 4.6071 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 7500: loss 4.5508, time 21421.94ms, mfu 0.05% +step 7750: train loss 4.5987, val loss 4.5930 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 7750: loss 4.7652, time 21346.24ms, mfu 0.05% +step 8000: train loss 4.5580, val loss 4.5827 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 8000: loss 4.6266, time 21362.62ms, mfu 0.05% +step 8250: train loss 4.5393, val loss 4.5701 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 8250: loss 4.3301, time 21749.51ms, mfu 0.05% +step 8500: train loss 4.5377, val loss 4.5470 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 8500: loss 4.2937, time 21327.85ms, mfu 0.05% +step 8750: train loss 4.4985, val loss 4.5036 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 8750: loss 4.5082, time 21151.05ms, mfu 0.05% +step 9000: train loss 4.5150, val loss 4.5199 +iter 9000: loss 4.5857, time 18035.54ms, mfu 0.05% +step 9250: train loss 4.4756, val loss 4.5051 +iter 9250: loss 4.5535, time 18153.89ms, mfu 0.05% +step 9500: train loss 4.4807, val loss 4.4928 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 9500: loss 4.5198, time 21417.34ms, mfu 0.05% +step 9750: train loss 4.4873, val loss 4.4618 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_chaos +iter 9750: loss 4.3921, time 21315.87ms, mfu 0.05% +step 10000: train loss 4.4792, val loss 4.5041 +iter 10000: loss 4.5203, time 18142.38ms, mfu 0.05% + +🎯 RESULT | baseline | seed=1337 | BEST=4.4618 | FINAL=4.5041 | ✗(best@9750) seed42=4.4830 + + +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 +RUNNING: baseline | seed=123 + no gate — fresh matched reference +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9288, val loss 10.9279 +iter 0: loss 10.9503, time 18187.33ms, mfu -100.00% +step 250: train loss 6.9746, val loss 6.9200 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 250: loss 6.6646, time 21151.35ms, mfu 0.05% +step 500: train loss 6.4965, val loss 6.5719 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 500: loss 6.6015, time 21268.23ms, mfu 0.05% +step 750: train loss 6.3137, val loss 6.3185 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 750: loss 5.6493, time 20890.26ms, mfu 0.05% +step 1000: train loss 6.1956, val loss 6.1953 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 1000: loss 6.5053, time 21088.14ms, mfu 0.05% +step 1250: train loss 6.0337, val loss 6.0635 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 1250: loss 5.7652, time 21388.94ms, mfu 0.05% +step 1500: train loss 6.0011, val loss 5.9724 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 1500: loss 5.8149, time 22372.83ms, mfu 0.05% +step 1750: train loss 5.8903, val loss 5.9331 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 1750: loss 6.6432, time 22699.41ms, mfu 0.05% +step 2000: train loss 5.8309, val loss 5.8679 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 2000: loss 5.7509, time 22146.52ms, mfu 0.05% +step 2250: train loss 5.6782, val loss 5.7339 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 2250: loss 6.1668, time 23366.83ms, mfu 0.05% +step 2500: train loss 5.6417, val loss 5.6473 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 2500: loss 5.8501, time 23407.13ms, mfu 0.05% +step 2750: train loss 5.5846, val loss 5.5609 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 2750: loss 6.0889, time 21300.05ms, mfu 0.05% +step 3000: train loss 5.5066, val loss 5.5064 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 3000: loss 6.2311, time 21576.50ms, mfu 0.05% +step 3250: train loss 5.4109, val loss 5.4046 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 3250: loss 5.3594, time 21180.90ms, mfu 0.05% +step 3500: train loss 5.3396, val loss 5.3914 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 3500: loss 5.3799, time 21675.59ms, mfu 0.05% +step 3750: train loss 5.3011, val loss 5.2891 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 3750: loss 5.0724, time 21690.25ms, mfu 0.05% +step 4000: train loss 5.2499, val loss 5.2632 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 4000: loss 5.1416, time 21739.36ms, mfu 0.05% +step 4250: train loss 5.2126, val loss 5.2212 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 4250: loss 5.2836, time 21435.16ms, mfu 0.05% +step 4500: train loss 5.1303, val loss 5.1333 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 4500: loss 5.1738, time 21492.04ms, mfu 0.05% +step 4750: train loss 5.1098, val loss 5.1093 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 4750: loss 5.0215, time 21565.69ms, mfu 0.05% +step 5000: train loss 5.0303, val loss 5.0291 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 5000: loss 5.1538, time 23470.09ms, mfu 0.05% +step 5250: train loss 5.0086, val loss 4.9989 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 5250: loss 5.1765, time 23576.20ms, mfu 0.05% +step 5500: train loss 4.9271, val loss 4.9673 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 5500: loss 4.8597, time 21052.73ms, mfu 0.05% +step 5750: train loss 4.9447, val loss 4.9278 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 5750: loss 5.2792, time 21038.93ms, mfu 0.05% +step 6000: train loss 4.8837, val loss 4.8528 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 6000: loss 4.9104, time 21214.40ms, mfu 0.05% +step 6250: train loss 4.8095, val loss 4.8640 +iter 6250: loss 5.0551, time 18164.71ms, mfu 0.05% +step 6500: train loss 4.7748, val loss 4.7850 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 6500: loss 4.6467, time 21295.77ms, mfu 0.05% +step 6750: train loss 4.7325, val loss 4.7543 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 6750: loss 4.4028, time 21968.54ms, mfu 0.05% +step 7000: train loss 4.7024, val loss 4.7102 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 7000: loss 4.6306, time 19626.71ms, mfu 0.05% +step 7250: train loss 4.6692, val loss 4.6988 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 7250: loss 4.2580, time 21443.55ms, mfu 0.05% +step 7500: train loss 4.6760, val loss 4.6580 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 7500: loss 4.6497, time 19658.12ms, mfu 0.05% +step 7750: train loss 4.6292, val loss 4.6273 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 7750: loss 4.8134, time 19535.02ms, mfu 0.05% +step 8000: train loss 4.6045, val loss 4.5992 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 8000: loss 4.2042, time 20001.16ms, mfu 0.05% +step 8250: train loss 4.5602, val loss 4.5580 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 8250: loss 4.3563, time 19399.72ms, mfu 0.05% +step 8500: train loss 4.5476, val loss 4.5671 +iter 8500: loss 4.3288, time 17628.03ms, mfu 0.05% +step 8750: train loss 4.5227, val loss 4.5367 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 8750: loss 4.4051, time 19273.19ms, mfu 0.05% +step 9000: train loss 4.5379, val loss 4.5193 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 9000: loss 4.3035, time 19534.32ms, mfu 0.05% +step 9250: train loss 4.4860, val loss 4.5132 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 9250: loss 5.0520, time 19402.39ms, mfu 0.05% +step 9500: train loss 4.4829, val loss 4.4813 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_chaos +iter 9500: loss 4.9311, time 19793.15ms, mfu 0.05% +step 9750: train loss 4.5083, val loss 4.5289 +iter 9750: loss 4.3854, time 17637.12ms, mfu 0.05% +step 10000: train loss 4.4901, val loss 4.5018 +iter 10000: loss 4.6307, time 17655.03ms, mfu 0.05% + +🎯 RESULT | baseline | seed=123 | BEST=4.4813 | FINAL=4.5018 | ✗(best@9500) seed42=4.4830 + + +🏆 RUNNING LEADERBOARD (this session, seeds 1337+123 only): + 1. baseline 4.5030 vs clean baseline: +0.0057 + + +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 +RUNNING: ones_identity | seed=1337 + always 1.0 — identity control +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9989, val loss 10.9985 +iter 0: loss 11.0107, time 17295.87ms, mfu -100.00% +step 250: train loss 6.9434, val loss 6.9764 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 250: loss 6.7374, time 18456.74ms, mfu 0.06% +step 500: train loss 6.4863, val loss 6.5541 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 500: loss 6.2682, time 19879.22ms, mfu 0.06% +step 750: train loss 6.3185, val loss 6.3358 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 750: loss 6.1052, time 19881.22ms, mfu 0.06% +step 1000: train loss 6.1833, val loss 6.1561 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 1000: loss 6.1559, time 19205.40ms, mfu 0.06% +step 1250: train loss 6.0925, val loss 6.0847 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 1250: loss 6.1252, time 19971.38ms, mfu 0.06% +step 1500: train loss 6.0041, val loss 5.9301 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 1500: loss 5.7184, time 22066.40ms, mfu 0.05% +step 1750: train loss 5.9229, val loss 5.8883 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 1750: loss 6.1128, time 22341.79ms, mfu 0.05% +step 2000: train loss 5.8213, val loss 5.7939 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 2000: loss 5.8729, time 21357.89ms, mfu 0.05% +step 2250: train loss 5.7229, val loss 5.7315 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 2250: loss 5.9146, time 21158.41ms, mfu 0.05% +step 2500: train loss 5.6100, val loss 5.5895 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 2500: loss 6.0838, time 19488.47ms, mfu 0.05% +step 2750: train loss 5.5403, val loss 5.5361 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 2750: loss 5.5360, time 19313.48ms, mfu 0.05% +step 3000: train loss 5.4556, val loss 5.4740 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 3000: loss 5.5379, time 19256.21ms, mfu 0.05% +step 3250: train loss 5.3676, val loss 5.3713 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 3250: loss 5.3401, time 19313.64ms, mfu 0.05% +step 3500: train loss 5.3769, val loss 5.3421 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 3500: loss 5.2874, time 19565.42ms, mfu 0.05% +step 3750: train loss 5.2917, val loss 5.2779 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 3750: loss 5.5770, time 19653.52ms, mfu 0.05% +step 4000: train loss 5.2413, val loss 5.2421 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 4000: loss 5.3148, time 19574.83ms, mfu 0.05% +step 4250: train loss 5.1801, val loss 5.1951 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 4250: loss 4.7806, time 19604.50ms, mfu 0.05% +step 4500: train loss 5.1406, val loss 5.1336 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 4500: loss 4.8216, time 19888.61ms, mfu 0.05% +step 4750: train loss 5.0723, val loss 5.0850 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 4750: loss 5.1986, time 19392.47ms, mfu 0.05% +step 5000: train loss 5.0021, val loss 5.0403 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 5000: loss 5.1836, time 19758.61ms, mfu 0.05% +step 5250: train loss 4.9909, val loss 4.9784 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 5250: loss 4.6641, time 19265.14ms, mfu 0.05% +step 5500: train loss 4.9598, val loss 4.9286 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 5500: loss 5.1454, time 19482.69ms, mfu 0.05% +step 5750: train loss 4.8998, val loss 4.8646 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 5750: loss 5.1733, time 19677.52ms, mfu 0.05% +step 6000: train loss 4.8078, val loss 4.8147 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 6000: loss 4.7242, time 19689.42ms, mfu 0.05% +step 6250: train loss 4.7964, val loss 4.8270 +iter 6250: loss 4.5946, time 17600.54ms, mfu 0.05% +step 6500: train loss 4.7556, val loss 4.7910 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 6500: loss 4.9331, time 19841.46ms, mfu 0.05% +step 6750: train loss 4.7214, val loss 4.7269 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 6750: loss 4.8897, time 19530.25ms, mfu 0.05% +step 7000: train loss 4.6883, val loss 4.6854 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 7000: loss 4.6138, time 20079.19ms, mfu 0.05% +step 7250: train loss 4.6452, val loss 4.6753 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 7250: loss 4.7606, time 19764.48ms, mfu 0.05% +step 7500: train loss 4.6472, val loss 4.6039 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 7500: loss 4.5202, time 19676.42ms, mfu 0.05% +step 7750: train loss 4.5968, val loss 4.5926 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 7750: loss 4.7722, time 19547.28ms, mfu 0.05% +step 8000: train loss 4.5592, val loss 4.5833 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 8000: loss 4.6358, time 19201.46ms, mfu 0.05% +step 8250: train loss 4.5404, val loss 4.5708 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 8250: loss 4.3292, time 19352.70ms, mfu 0.05% +step 8500: train loss 4.5375, val loss 4.5494 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 8500: loss 4.2747, time 19214.55ms, mfu 0.05% +step 8750: train loss 4.4999, val loss 4.5025 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 8750: loss 4.5047, time 20295.29ms, mfu 0.05% +step 9000: train loss 4.5168, val loss 4.5190 +iter 9000: loss 4.5969, time 17911.48ms, mfu 0.05% +step 9250: train loss 4.4751, val loss 4.5052 +iter 9250: loss 4.5628, time 18090.67ms, mfu 0.05% +step 9500: train loss 4.4813, val loss 4.4933 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 9500: loss 4.5013, time 19727.02ms, mfu 0.05% +step 9750: train loss 4.4870, val loss 4.4612 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed1337_chaos +iter 9750: loss 4.3761, time 19849.28ms, mfu 0.05% +step 10000: train loss 4.4785, val loss 4.5046 +iter 10000: loss 4.4949, time 17601.35ms, mfu 0.05% + +🎯 RESULT | ones_identity | seed=1337 | BEST=4.4612 | FINAL=4.5046 | ✗(best@9750) seed42=4.4859 + + +🏆 RUNNING LEADERBOARD (this session, seeds 1337+123 only): + 1. baseline 4.5030 vs clean baseline: +0.0057 + 2. ones_identity 4.5046 vs clean baseline: +0.0073 + + +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 +RUNNING: ones_identity | seed=123 + always 1.0 — identity control +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9288, val loss 10.9279 +iter 0: loss 10.9503, time 17363.42ms, mfu -100.00% +step 250: train loss 6.9746, val loss 6.9200 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 250: loss 6.6645, time 18435.63ms, mfu 0.06% +step 500: train loss 6.4965, val loss 6.5718 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 500: loss 6.6019, time 18887.74ms, mfu 0.06% +step 750: train loss 6.3136, val loss 6.3183 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 750: loss 5.6491, time 19022.18ms, mfu 0.06% +step 1000: train loss 6.1973, val loss 6.1970 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 1000: loss 6.5112, time 18903.02ms, mfu 0.06% +step 1250: train loss 6.0343, val loss 6.0630 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 1250: loss 5.7712, time 18722.53ms, mfu 0.06% +step 1500: train loss 6.0137, val loss 5.9839 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 1500: loss 5.8058, time 21288.64ms, mfu 0.06% +step 1750: train loss 5.8861, val loss 5.9253 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 1750: loss 6.5400, time 21808.19ms, mfu 0.05% +step 2000: train loss 5.8371, val loss 5.8698 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 2000: loss 5.7722, time 20529.39ms, mfu 0.05% +step 2250: train loss 5.6678, val loss 5.7234 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 2250: loss 6.1489, time 21295.77ms, mfu 0.05% +step 2500: train loss 5.6371, val loss 5.6453 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 2500: loss 5.8579, time 21229.24ms, mfu 0.05% +step 2750: train loss 5.5760, val loss 5.5533 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 2750: loss 6.0985, time 23220.76ms, mfu 0.05% +step 3000: train loss 5.4956, val loss 5.4983 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 3000: loss 6.2229, time 18780.26ms, mfu 0.05% +step 3250: train loss 5.3967, val loss 5.3955 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 3250: loss 5.3356, time 18923.60ms, mfu 0.05% +step 3500: train loss 5.3382, val loss 5.3866 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 3500: loss 5.3610, time 18688.43ms, mfu 0.05% +step 3750: train loss 5.3021, val loss 5.2901 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 3750: loss 5.0768, time 18775.64ms, mfu 0.05% +step 4000: train loss 5.2513, val loss 5.2661 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 4000: loss 5.1447, time 18709.72ms, mfu 0.05% +step 4250: train loss 5.2166, val loss 5.2269 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 4250: loss 5.2681, time 18649.43ms, mfu 0.05% +step 4500: train loss 5.1312, val loss 5.1354 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 4500: loss 5.1823, time 18642.04ms, mfu 0.05% +step 4750: train loss 5.1174, val loss 5.1133 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 4750: loss 5.0104, time 18733.36ms, mfu 0.05% +step 5000: train loss 5.0315, val loss 5.0308 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 5000: loss 5.2127, time 18715.50ms, mfu 0.05% +step 5250: train loss 5.0127, val loss 5.0034 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 5250: loss 5.1690, time 18825.02ms, mfu 0.05% +step 5500: train loss 4.9260, val loss 4.9685 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 5500: loss 4.8480, time 18688.23ms, mfu 0.05% +step 5750: train loss 4.9461, val loss 4.9262 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 5750: loss 5.2477, time 18692.54ms, mfu 0.06% +step 6000: train loss 4.8870, val loss 4.8557 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 6000: loss 4.8944, time 18924.40ms, mfu 0.06% +step 6250: train loss 4.8155, val loss 4.8677 +iter 6250: loss 5.0632, time 17099.91ms, mfu 0.06% +step 6500: train loss 4.7745, val loss 4.7850 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 6500: loss 4.6700, time 19154.22ms, mfu 0.06% +step 6750: train loss 4.7352, val loss 4.7568 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 6750: loss 4.4195, time 18664.81ms, mfu 0.06% +step 7000: train loss 4.6953, val loss 4.7046 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 7000: loss 4.6287, time 18737.22ms, mfu 0.06% +step 7250: train loss 4.6710, val loss 4.6990 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 7250: loss 4.3189, time 18705.45ms, mfu 0.06% +step 7500: train loss 4.6766, val loss 4.6622 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 7500: loss 4.6457, time 19478.11ms, mfu 0.06% +step 7750: train loss 4.6267, val loss 4.6257 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 7750: loss 4.8125, time 18579.25ms, mfu 0.06% +step 8000: train loss 4.6021, val loss 4.5968 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 8000: loss 4.1618, time 19811.22ms, mfu 0.06% +step 8250: train loss 4.5588, val loss 4.5560 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 8250: loss 4.3516, time 19532.60ms, mfu 0.06% +step 8500: train loss 4.5442, val loss 4.5654 +iter 8500: loss 4.3282, time 17538.42ms, mfu 0.06% +step 8750: train loss 4.5219, val loss 4.5377 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 8750: loss 4.4060, time 19534.66ms, mfu 0.06% +step 9000: train loss 4.5386, val loss 4.5196 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 9000: loss 4.3036, time 19713.81ms, mfu 0.06% +step 9250: train loss 4.4843, val loss 4.5149 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 9250: loss 5.0437, time 19134.32ms, mfu 0.06% +step 9500: train loss 4.4833, val loss 4.4796 +saving checkpoint to out-openwebtext_dynamic_ones_gate_warmup2000_seed123_chaos +iter 9500: loss 4.9402, time 19230.48ms, mfu 0.06% +step 9750: train loss 4.5071, val loss 4.5296 +iter 9750: loss 4.4065, time 17562.30ms, mfu 0.06% +step 10000: train loss 4.4913, val loss 4.5018 +iter 10000: loss 4.6200, time 17594.31ms, mfu 0.06% + +🎯 RESULT | ones_identity | seed=123 | BEST=4.4796 | FINAL=4.5018 | ✗(best@9500) seed42=4.4859 + + +🏆 RUNNING LEADERBOARD (this session, seeds 1337+123 only): + 1. baseline 4.5030 vs clean baseline: +0.0057 + 2. ones_identity 4.5032 vs clean baseline: +0.0059 + + +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 +RUNNING: a_only | seed=1337 + learned: per-head Linear(64->64) on y +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.64M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 124,010,496 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9316, val loss 10.9312 +iter 0: loss 10.9362, time 18100.29ms, mfu -100.00% +step 250: train loss 6.9044, val loss 6.9314 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_chaos +iter 250: loss 6.7946, time 19246.00ms, mfu 0.05% +step 500: train loss 6.4895, val loss 6.4933 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_chaos +iter 500: loss 6.6436, time 19648.35ms, mfu 0.05% +step 750: train loss 6.3016, val loss 6.3295 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_chaos +iter 750: loss 6.0942, time 19965.91ms, mfu 0.05% +step 1000: train loss 6.1556, val loss 6.1420 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_chaos +iter 1000: loss 6.1048, time 19427.20ms, mfu 0.05% +step 1250: train loss 6.0665, val loss 6.0363 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_chaos +iter 1250: loss 5.7170, time 20054.81ms, mfu 0.05% +step 1500: train loss 5.9527, val loss 5.9777 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_chaos +iter 1500: loss 6.2703, time 21931.61ms, mfu 0.05% +step 1750: train loss 5.8452, val loss 5.8598 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_chaos +iter 1750: loss 5.9742, time 22665.30ms, mfu 0.05% +step 2000: train loss 5.8146, val loss 5.7949 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_chaos +iter 2000: loss 5.8063, time 22454.41ms, mfu 0.05% +step 2250: train loss 5.6992, val loss 5.6501 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_chaos +iter 2250: loss 6.1022, time 23698.41ms, mfu 0.05% +step 2500: train loss 5.6275, val loss 5.5926 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_chaos +iter 2500: loss 5.1697, time 22901.58ms, mfu 0.05% +step 2750: train loss 5.4872, val loss 5.5038 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_chaos +iter 2750: loss 5.8522, time 19515.33ms, mfu 0.05% +step 3000: train loss 5.4020, val loss 5.4217 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_chaos +iter 3000: loss 5.1208, time 19678.01ms, mfu 0.05% +step 3250: train loss 5.3674, val loss 5.3720 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_chaos +iter 3250: loss 5.0812, time 19642.94ms, mfu 0.05% +step 3500: train loss 5.3086, val loss 5.2906 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_chaos +iter 3500: loss 5.3832, time 19380.88ms, mfu 0.05% +step 3750: train loss 5.2357, val loss 5.2491 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_chaos +iter 3750: loss 5.3425, time 19501.24ms, mfu 0.05% +step 4000: train loss 5.1780, val loss 5.2061 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_chaos +iter 4000: loss 5.0887, time 19378.39ms, mfu 0.05% +step 4250: train loss 5.1609, val loss 5.1632 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_chaos +iter 4250: loss 4.9396, time 19501.58ms, mfu 0.05% +step 4500: train loss 5.0857, val loss 5.1190 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_chaos +iter 4500: loss 5.6029, time 19761.21ms, mfu 0.05% +step 4750: train loss 5.0103, val loss 5.0480 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_chaos +iter 4750: loss 5.4162, time 19667.00ms, mfu 0.05% +step 5000: train loss 4.9853, val loss 4.9979 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_chaos +iter 5000: loss 4.9146, time 19795.44ms, mfu 0.05% +step 5250: train loss 4.9378, val loss 4.9269 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_chaos +iter 5250: loss 4.6144, time 19382.70ms, mfu 0.05% +step 5500: train loss 4.8818, val loss 4.8832 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_chaos +iter 5500: loss 4.7916, time 20271.89ms, mfu 0.05% +step 5750: train loss 4.8545, val loss 4.8687 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_chaos +iter 5750: loss 4.7016, time 19328.20ms, mfu 0.05% +step 6000: train loss 4.7804, val loss 4.7976 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_chaos +iter 6000: loss 4.8703, time 19643.38ms, mfu 0.05% +step 6250: train loss 4.7627, val loss 4.7752 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_chaos +iter 6250: loss 4.6034, time 19794.77ms, mfu 0.05% +step 6500: train loss 4.7075, val loss 4.7412 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_chaos +iter 6500: loss 4.6965, time 20250.14ms, mfu 0.05% +step 6750: train loss 4.6669, val loss 4.6921 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_chaos +iter 6750: loss 4.8027, time 20270.06ms, mfu 0.05% +step 7000: train loss 4.6527, val loss 4.6029 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_chaos +iter 7000: loss 4.1516, time 20496.94ms, mfu 0.05% +step 7250: train loss 4.5813, val loss 4.6485 +iter 7250: loss 4.3945, time 18237.31ms, mfu 0.05% +step 7500: train loss 4.5786, val loss 4.5949 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_chaos +iter 7500: loss 4.9485, time 20500.44ms, mfu 0.05% +step 7750: train loss 4.5703, val loss 4.5464 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_chaos +iter 7750: loss 4.9246, time 20648.81ms, mfu 0.05% +step 8000: train loss 4.5675, val loss 4.5506 +iter 8000: loss 4.4924, time 18326.30ms, mfu 0.05% +step 8250: train loss 4.5243, val loss 4.5048 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_chaos +iter 8250: loss 4.6364, time 19958.84ms, mfu 0.05% +step 8500: train loss 4.4837, val loss 4.4796 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_chaos +iter 8500: loss 4.2249, time 20119.25ms, mfu 0.05% +step 8750: train loss 4.5086, val loss 4.5270 +iter 8750: loss 4.4344, time 18357.81ms, mfu 0.05% +step 9000: train loss 4.4706, val loss 4.5118 +iter 9000: loss 4.7788, time 18415.66ms, mfu 0.05% +step 9250: train loss 4.4643, val loss 4.4399 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed1337_chaos +iter 9250: loss 4.4502, time 20272.76ms, mfu 0.05% +step 9500: train loss 4.4621, val loss 4.4550 +iter 9500: loss 4.5146, time 18350.61ms, mfu 0.05% +step 9750: train loss 4.4293, val loss 4.4536 +iter 9750: loss 4.4732, time 18303.71ms, mfu 0.05% +step 10000: train loss 4.4199, val loss 4.4415 +iter 10000: loss 4.6660, time 18273.22ms, mfu 0.05% + +🎯 RESULT | a_only | seed=1337 | BEST=4.4399 | FINAL=4.4415 | ✗(best@9250) seed42=4.4180 + + +🏆 RUNNING LEADERBOARD (this session, seeds 1337+123 only): + 1. a_only 4.4415 vs clean baseline: -0.0558 + 2. baseline 4.5030 vs clean baseline: +0.0057 + 3. ones_identity 4.5032 vs clean baseline: +0.0059 + + +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 +RUNNING: a_only | seed=123 + learned: per-head Linear(64->64) on y +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.64M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 124,010,496 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9089, val loss 10.9095 +iter 0: loss 10.9231, time 18430.92ms, mfu -100.00% +step 250: train loss 6.9507, val loss 6.9767 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 250: loss 7.0428, time 19870.62ms, mfu 0.05% +step 500: train loss 6.5223, val loss 6.5134 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 500: loss 6.2352, time 20481.84ms, mfu 0.05% +step 750: train loss 6.3294, val loss 6.3574 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 750: loss 5.8386, time 19888.81ms, mfu 0.05% +step 1000: train loss 6.1690, val loss 6.1773 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 1000: loss 6.1514, time 20040.07ms, mfu 0.05% +step 1250: train loss 6.0545, val loss 6.0539 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 1250: loss 6.1609, time 21178.53ms, mfu 0.05% +step 1500: train loss 5.9686, val loss 5.9656 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 1500: loss 6.2998, time 21721.07ms, mfu 0.05% +step 1750: train loss 5.8790, val loss 5.8880 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 1750: loss 5.6421, time 22718.30ms, mfu 0.05% +step 2000: train loss 5.7756, val loss 5.7720 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 2000: loss 5.8388, time 21436.53ms, mfu 0.05% +step 2250: train loss 5.6846, val loss 5.7008 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 2250: loss 5.8653, time 22196.65ms, mfu 0.05% +step 2500: train loss 5.6315, val loss 5.6156 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 2500: loss 5.5047, time 23960.12ms, mfu 0.05% +step 2750: train loss 5.4709, val loss 5.5081 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 2750: loss 5.1971, time 22065.12ms, mfu 0.05% +step 3000: train loss 5.4094, val loss 5.3950 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 3000: loss 5.5822, time 19828.04ms, mfu 0.05% +step 3250: train loss 5.3234, val loss 5.3491 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 3250: loss 5.3443, time 20619.88ms, mfu 0.05% +step 3500: train loss 5.3070, val loss 5.3014 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 3500: loss 4.9115, time 20624.57ms, mfu 0.05% +step 3750: train loss 5.2633, val loss 5.2709 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 3750: loss 5.5437, time 20595.73ms, mfu 0.05% +step 4000: train loss 5.1742, val loss 5.1997 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 4000: loss 5.0389, time 19883.04ms, mfu 0.05% +step 4250: train loss 5.1226, val loss 5.1164 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 4250: loss 5.1577, time 19986.57ms, mfu 0.05% +step 4500: train loss 5.0594, val loss 5.0623 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 4500: loss 4.5207, time 20107.44ms, mfu 0.05% +step 4750: train loss 5.0166, val loss 5.0325 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 4750: loss 4.7828, time 20034.30ms, mfu 0.05% +step 5000: train loss 4.9863, val loss 4.9718 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 5000: loss 5.3412, time 20093.23ms, mfu 0.05% +step 5250: train loss 4.9288, val loss 4.9276 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 5250: loss 4.8388, time 20480.62ms, mfu 0.05% +step 5500: train loss 4.8955, val loss 4.9007 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 5500: loss 4.7643, time 20399.30ms, mfu 0.05% +step 5750: train loss 4.8229, val loss 4.8283 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 5750: loss 5.5420, time 20232.41ms, mfu 0.05% +step 6000: train loss 4.7699, val loss 4.7832 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 6000: loss 4.6057, time 20325.66ms, mfu 0.05% +step 6250: train loss 4.7481, val loss 4.7258 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 6250: loss 4.9731, time 20260.25ms, mfu 0.05% +step 6500: train loss 4.7259, val loss 4.7287 +iter 6500: loss 4.6483, time 18235.48ms, mfu 0.05% +step 6750: train loss 4.6284, val loss 4.7080 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 6750: loss 4.5445, time 20407.07ms, mfu 0.05% +step 7000: train loss 4.6228, val loss 4.6690 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 7000: loss 5.0974, time 21879.37ms, mfu 0.05% +step 7250: train loss 4.6430, val loss 4.6452 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 7250: loss 4.6401, time 22923.42ms, mfu 0.05% +step 7500: train loss 4.5709, val loss 4.5838 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 7500: loss 5.0342, time 21817.90ms, mfu 0.05% +step 7750: train loss 4.5628, val loss 4.5744 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 7750: loss 4.7147, time 21742.22ms, mfu 0.05% +step 8000: train loss 4.5265, val loss 4.5413 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 8000: loss 4.3695, time 21558.59ms, mfu 0.05% +step 8250: train loss 4.5333, val loss 4.5265 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 8250: loss 4.5043, time 21210.24ms, mfu 0.05% +step 8500: train loss 4.4834, val loss 4.4813 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 8500: loss 4.5081, time 19954.96ms, mfu 0.05% +step 8750: train loss 4.4822, val loss 4.4959 +iter 8750: loss 4.2665, time 18358.20ms, mfu 0.05% +step 9000: train loss 4.4765, val loss 4.4839 +iter 9000: loss 4.5928, time 18360.04ms, mfu 0.05% +step 9250: train loss 4.4867, val loss 4.4436 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 9250: loss 4.6969, time 19996.19ms, mfu 0.05% +step 9500: train loss 4.4619, val loss 4.4417 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 9500: loss 4.5583, time 19816.18ms, mfu 0.05% +step 9750: train loss 4.4377, val loss 4.4582 +iter 9750: loss 4.2076, time 18399.91ms, mfu 0.05% +step 10000: train loss 4.4320, val loss 4.4290 +saving checkpoint to out-openwebtext_dynamic_a_conditioned_warmup2000_seed123_chaos +iter 10000: loss 4.0966, time 20452.90ms, mfu 0.05% + +🎯 RESULT | a_only | seed=123 | BEST=4.4290 | FINAL=4.4290 | ✓ seed42=4.4180 + + +🏆 RUNNING LEADERBOARD (this session, seeds 1337+123 only): + 1. a_only 4.4352 vs clean baseline: -0.0621 + 2. baseline 4.5030 vs clean baseline: +0.0057 + 3. ones_identity 4.5032 vs clean baseline: +0.0059 + + +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 +RUNNING: random_uniform | seed=1337 + chaos: uniform random [0,1] +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9794, val loss 10.9788 +iter 0: loss 10.9999, time 18024.45ms, mfu -100.00% +step 250: train loss 6.9386, val loss 6.9720 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 250: loss 6.7260, time 19286.06ms, mfu 0.05% +step 500: train loss 6.4827, val loss 6.5503 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 500: loss 6.2657, time 20103.94ms, mfu 0.05% +step 750: train loss 6.3039, val loss 6.3214 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 750: loss 6.0919, time 19871.90ms, mfu 0.05% +step 1000: train loss 6.1928, val loss 6.1646 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 1000: loss 6.1632, time 20022.37ms, mfu 0.05% +step 1250: train loss 6.0705, val loss 6.0645 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 1250: loss 6.1117, time 19426.84ms, mfu 0.05% +step 1500: train loss 6.0079, val loss 5.9347 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 1500: loss 5.7097, time 19531.32ms, mfu 0.05% +step 1750: train loss 5.9234, val loss 5.8879 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 1750: loss 6.1122, time 19747.78ms, mfu 0.05% +step 2000: train loss 5.8418, val loss 5.8146 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 2000: loss 5.8995, time 19833.05ms, mfu 0.05% +step 2250: train loss 5.7263, val loss 5.7321 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 2250: loss 5.9088, time 20574.27ms, mfu 0.05% +step 2500: train loss 5.6282, val loss 5.6068 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 2500: loss 6.0941, time 19996.82ms, mfu 0.05% +step 2750: train loss 5.5446, val loss 5.5403 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 2750: loss 5.5419, time 19352.02ms, mfu 0.05% +step 3000: train loss 5.4651, val loss 5.4796 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 3000: loss 5.5507, time 19352.77ms, mfu 0.05% +step 3250: train loss 5.3818, val loss 5.3881 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 3250: loss 5.3542, time 19483.24ms, mfu 0.05% +step 3500: train loss 5.3829, val loss 5.3434 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 3500: loss 5.2812, time 19440.30ms, mfu 0.05% +step 3750: train loss 5.2899, val loss 5.2803 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 3750: loss 5.5836, time 21406.94ms, mfu 0.05% +step 4000: train loss 5.2523, val loss 5.2570 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 4000: loss 5.3446, time 18917.35ms, mfu 0.05% +step 4250: train loss 5.1943, val loss 5.2069 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 4250: loss 4.7944, time 18922.90ms, mfu 0.05% +step 4500: train loss 5.1510, val loss 5.1460 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 4500: loss 4.9572, time 19288.37ms, mfu 0.05% +step 4750: train loss 5.1015, val loss 5.1060 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 4750: loss 5.2150, time 19128.18ms, mfu 0.05% +step 5000: train loss 5.0267, val loss 5.0621 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 5000: loss 5.2208, time 19048.24ms, mfu 0.05% +step 5250: train loss 5.0329, val loss 5.0131 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 5250: loss 4.6746, time 19158.65ms, mfu 0.05% +step 5500: train loss 5.0332, val loss 4.9799 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 5500: loss 5.2571, time 19247.52ms, mfu 0.05% +step 5750: train loss 4.9524, val loss 4.9047 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 5750: loss 5.2187, time 19493.80ms, mfu 0.05% +step 6000: train loss 4.8644, val loss 4.8620 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 6000: loss 4.7574, time 19074.19ms, mfu 0.05% +step 6250: train loss 4.8443, val loss 4.8741 +iter 6250: loss 4.6403, time 17469.38ms, mfu 0.05% +step 6500: train loss 4.8000, val loss 4.8319 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 6500: loss 4.9647, time 19031.18ms, mfu 0.05% +step 6750: train loss 4.7679, val loss 4.7747 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 6750: loss 4.9643, time 19461.20ms, mfu 0.05% +step 7000: train loss 4.7303, val loss 4.7265 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 7000: loss 4.6393, time 19407.52ms, mfu 0.05% +step 7250: train loss 4.6872, val loss 4.7092 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 7250: loss 4.7776, time 19487.54ms, mfu 0.05% +step 7500: train loss 4.6936, val loss 4.6451 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 7500: loss 4.5468, time 19534.68ms, mfu 0.05% +step 7750: train loss 4.6390, val loss 4.6311 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 7750: loss 4.8027, time 19558.32ms, mfu 0.05% +step 8000: train loss 4.5990, val loss 4.6239 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 8000: loss 4.6502, time 19454.45ms, mfu 0.05% +step 8250: train loss 4.5768, val loss 4.6040 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 8250: loss 4.3787, time 19813.85ms, mfu 0.05% +step 8500: train loss 4.5724, val loss 4.5801 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 8500: loss 4.3313, time 19799.71ms, mfu 0.05% +step 8750: train loss 4.5343, val loss 4.5368 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 8750: loss 4.5486, time 20001.05ms, mfu 0.05% +step 9000: train loss 4.5467, val loss 4.5492 +iter 9000: loss 4.6290, time 17789.64ms, mfu 0.05% +step 9250: train loss 4.5087, val loss 4.5398 +iter 9250: loss 4.5953, time 17832.04ms, mfu 0.05% +step 9500: train loss 4.5166, val loss 4.5261 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 9500: loss 4.5159, time 20102.17ms, mfu 0.05% +step 9750: train loss 4.5174, val loss 4.4948 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed1337_chaos +iter 9750: loss 4.4189, time 19978.80ms, mfu 0.05% +step 10000: train loss 4.5123, val loss 4.5373 +iter 10000: loss 4.5350, time 17818.52ms, mfu 0.05% + +🎯 RESULT | random_uniform | seed=1337 | BEST=4.4948 | FINAL=4.5373 | ✗(best@9750) seed42=4.5238 + + +🏆 RUNNING LEADERBOARD (this session, seeds 1337+123 only): + 1. a_only 4.4352 vs clean baseline: -0.0621 + 2. baseline 4.5030 vs clean baseline: +0.0057 + 3. ones_identity 4.5032 vs clean baseline: +0.0059 + 4. random_uniform 4.5373 vs clean baseline: +0.0400 + + +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 +RUNNING: random_uniform | seed=123 + chaos: uniform random [0,1] +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9595, val loss 10.9585 +iter 0: loss 10.9734, time 18134.99ms, mfu -100.00% +step 250: train loss 6.9639, val loss 6.9092 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 250: loss 6.6615, time 19264.57ms, mfu 0.05% +step 500: train loss 6.4936, val loss 6.5701 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 500: loss 6.6017, time 20038.94ms, mfu 0.05% +step 750: train loss 6.3170, val loss 6.3217 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 750: loss 5.6435, time 19754.80ms, mfu 0.05% +step 1000: train loss 6.2063, val loss 6.2072 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 1000: loss 6.5167, time 19721.90ms, mfu 0.05% +step 1250: train loss 6.0346, val loss 6.0627 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 1250: loss 5.7764, time 20171.44ms, mfu 0.05% +step 1500: train loss 6.0019, val loss 5.9709 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 1500: loss 5.7959, time 21505.50ms, mfu 0.05% +step 1750: train loss 5.8943, val loss 5.9366 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 1750: loss 6.5128, time 22340.26ms, mfu 0.05% +step 2000: train loss 5.8339, val loss 5.8718 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 2000: loss 5.7572, time 22502.91ms, mfu 0.05% +step 2250: train loss 5.6958, val loss 5.7539 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 2250: loss 6.1876, time 22531.62ms, mfu 0.05% +step 2500: train loss 5.6576, val loss 5.6648 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 2500: loss 5.8837, time 21981.37ms, mfu 0.05% +step 2750: train loss 5.5986, val loss 5.5735 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 2750: loss 6.0994, time 21822.53ms, mfu 0.05% +step 3000: train loss 5.5107, val loss 5.5157 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 3000: loss 6.2379, time 24111.14ms, mfu 0.05% +step 3250: train loss 5.4188, val loss 5.4184 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 3250: loss 5.3571, time 21585.25ms, mfu 0.05% +step 3500: train loss 5.3434, val loss 5.3936 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 3500: loss 5.3870, time 21494.14ms, mfu 0.05% +step 3750: train loss 5.3071, val loss 5.2910 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 3750: loss 5.0761, time 21396.37ms, mfu 0.05% +step 4000: train loss 5.2735, val loss 5.2783 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 4000: loss 5.1849, time 21288.76ms, mfu 0.05% +step 4250: train loss 5.2215, val loss 5.2333 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 4250: loss 5.2630, time 21406.52ms, mfu 0.05% +step 4500: train loss 5.1405, val loss 5.1420 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 4500: loss 5.2000, time 21337.92ms, mfu 0.05% +step 4750: train loss 5.1238, val loss 5.1232 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 4750: loss 5.0106, time 21572.36ms, mfu 0.05% +step 5000: train loss 5.0448, val loss 5.0416 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 5000: loss 5.2040, time 21200.36ms, mfu 0.05% +step 5250: train loss 5.0318, val loss 5.0200 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 5250: loss 5.1943, time 21297.69ms, mfu 0.05% +step 5500: train loss 4.9467, val loss 4.9857 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 5500: loss 4.8702, time 21239.61ms, mfu 0.05% +step 5750: train loss 4.9798, val loss 4.9591 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 5750: loss 5.3397, time 21358.90ms, mfu 0.05% +step 6000: train loss 4.9248, val loss 4.8905 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 6000: loss 4.9275, time 21324.85ms, mfu 0.05% +step 6250: train loss 4.8583, val loss 4.9109 +iter 6250: loss 5.1203, time 18200.60ms, mfu 0.05% +step 6500: train loss 4.8149, val loss 4.8212 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 6500: loss 4.6900, time 21256.55ms, mfu 0.05% +step 6750: train loss 4.7697, val loss 4.7910 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 6750: loss 4.5113, time 21246.48ms, mfu 0.05% +step 7000: train loss 4.7358, val loss 4.7434 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 7000: loss 4.6660, time 21218.52ms, mfu 0.05% +step 7250: train loss 4.7104, val loss 4.7307 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 7250: loss 4.3448, time 22408.08ms, mfu 0.05% +step 7500: train loss 4.7112, val loss 4.6939 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 7500: loss 4.6453, time 21764.36ms, mfu 0.05% +step 7750: train loss 4.6628, val loss 4.6577 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 7750: loss 4.8262, time 22037.29ms, mfu 0.05% +step 8000: train loss 4.6353, val loss 4.6272 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 8000: loss 4.2776, time 21438.63ms, mfu 0.05% +step 8250: train loss 4.5912, val loss 4.5899 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 8250: loss 4.4413, time 21837.97ms, mfu 0.05% +step 8500: train loss 4.5772, val loss 4.5955 +iter 8500: loss 4.3740, time 18437.87ms, mfu 0.05% +step 8750: train loss 4.5546, val loss 4.5665 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 8750: loss 4.3870, time 21283.41ms, mfu 0.05% +step 9000: train loss 4.5650, val loss 4.5484 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 9000: loss 4.3140, time 21508.82ms, mfu 0.05% +step 9250: train loss 4.5150, val loss 4.5397 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 9250: loss 5.0762, time 21307.88ms, mfu 0.05% +step 9500: train loss 4.5136, val loss 4.5070 +saving checkpoint to out-openwebtext_dynamic_random_gate_warmup2000_seed123_chaos +iter 9500: loss 4.9530, time 21557.07ms, mfu 0.05% +step 9750: train loss 4.5339, val loss 4.5536 +iter 9750: loss 4.4613, time 18425.70ms, mfu 0.05% +step 10000: train loss 4.5206, val loss 4.5266 +iter 10000: loss 4.6363, time 18537.42ms, mfu 0.05% + +🎯 RESULT | random_uniform | seed=123 | BEST=4.5070 | FINAL=4.5266 | ✗(best@9500) seed42=4.5238 + + +🏆 RUNNING LEADERBOARD (this session, seeds 1337+123 only): + 1. a_only 4.4352 vs clean baseline: -0.0621 + 2. baseline 4.5030 vs clean baseline: +0.0057 + 3. ones_identity 4.5032 vs clean baseline: +0.0059 + 4. random_uniform 4.5320 vs clean baseline: +0.0347 + + +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 +RUNNING: random_normal | seed=1337 + chaos: normal(0.5,0.2) clamped [0,1] +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9796, val loss 10.9789 +iter 0: loss 11.0014, time 19146.61ms, mfu -100.00% +step 250: train loss 6.9370, val loss 6.9702 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 250: loss 6.7255, time 21678.36ms, mfu 0.05% +step 500: train loss 6.4807, val loss 6.5483 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 500: loss 6.2636, time 21808.67ms, mfu 0.05% +step 750: train loss 6.3002, val loss 6.3181 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 750: loss 6.0831, time 22099.48ms, mfu 0.05% +step 1000: train loss 6.1826, val loss 6.1554 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 1000: loss 6.1523, time 21884.70ms, mfu 0.05% +step 1250: train loss 6.0731, val loss 6.0655 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 1250: loss 6.0993, time 22076.53ms, mfu 0.05% +step 1500: train loss 5.9895, val loss 5.9158 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 1500: loss 5.7017, time 21881.75ms, mfu 0.05% +step 1750: train loss 5.9189, val loss 5.8846 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 1750: loss 6.0816, time 21899.93ms, mfu 0.05% +step 2000: train loss 5.8199, val loss 5.7934 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 2000: loss 5.8540, time 22003.83ms, mfu 0.05% +step 2250: train loss 5.7063, val loss 5.7124 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 2250: loss 5.9063, time 21969.50ms, mfu 0.05% +step 2500: train loss 5.6088, val loss 5.5891 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 2500: loss 6.0668, time 22398.40ms, mfu 0.05% +step 2750: train loss 5.5217, val loss 5.5183 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 2750: loss 5.5129, time 24904.51ms, mfu 0.05% +step 3000: train loss 5.4569, val loss 5.4714 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 3000: loss 5.5308, time 22691.06ms, mfu 0.05% +step 3250: train loss 5.3556, val loss 5.3625 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 3250: loss 5.3382, time 22040.42ms, mfu 0.05% +step 3500: train loss 5.3691, val loss 5.3335 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 3500: loss 5.2777, time 21996.12ms, mfu 0.05% +step 3750: train loss 5.2791, val loss 5.2665 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 3750: loss 5.5940, time 21945.83ms, mfu 0.05% +step 4000: train loss 5.2363, val loss 5.2371 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 4000: loss 5.3180, time 22042.33ms, mfu 0.05% +step 4250: train loss 5.1764, val loss 5.1890 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 4250: loss 4.7659, time 21901.92ms, mfu 0.05% +step 4500: train loss 5.1377, val loss 5.1299 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 4500: loss 4.8864, time 22059.41ms, mfu 0.05% +step 4750: train loss 5.0767, val loss 5.0843 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 4750: loss 5.1802, time 22052.61ms, mfu 0.05% +step 5000: train loss 5.0055, val loss 5.0414 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 5000: loss 5.1957, time 22063.81ms, mfu 0.05% +step 5250: train loss 5.0108, val loss 4.9917 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 5250: loss 4.6443, time 22015.49ms, mfu 0.05% +step 5500: train loss 4.9857, val loss 4.9424 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 5500: loss 5.1911, time 21879.48ms, mfu 0.05% +step 5750: train loss 4.9321, val loss 4.8796 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 5750: loss 5.1988, time 21968.03ms, mfu 0.05% +step 6000: train loss 4.8339, val loss 4.8339 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 6000: loss 4.7239, time 21922.74ms, mfu 0.05% +step 6250: train loss 4.8144, val loss 4.8431 +iter 6250: loss 4.6265, time 18836.14ms, mfu 0.05% +step 6500: train loss 4.7756, val loss 4.8089 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 6500: loss 4.9291, time 22015.43ms, mfu 0.05% +step 6750: train loss 4.7354, val loss 4.7452 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 6750: loss 4.9612, time 21947.45ms, mfu 0.05% +step 7000: train loss 4.6990, val loss 4.6963 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 7000: loss 4.6028, time 21891.21ms, mfu 0.05% +step 7250: train loss 4.6561, val loss 4.6823 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 7250: loss 4.7587, time 21982.73ms, mfu 0.05% +step 7500: train loss 4.6628, val loss 4.6173 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 7500: loss 4.5406, time 22345.66ms, mfu 0.05% +step 7750: train loss 4.6112, val loss 4.6042 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 7750: loss 4.8044, time 21813.40ms, mfu 0.05% +step 8000: train loss 4.5714, val loss 4.5987 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 8000: loss 4.6147, time 21829.58ms, mfu 0.05% +step 8250: train loss 4.5503, val loss 4.5819 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 8250: loss 4.3669, time 21907.84ms, mfu 0.05% +step 8500: train loss 4.5458, val loss 4.5559 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 8500: loss 4.3015, time 21845.32ms, mfu 0.05% +step 8750: train loss 4.5076, val loss 4.5124 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 8750: loss 4.5235, time 21833.22ms, mfu 0.05% +step 9000: train loss 4.5218, val loss 4.5259 +iter 9000: loss 4.5778, time 18856.49ms, mfu 0.05% +step 9250: train loss 4.4857, val loss 4.5143 +iter 9250: loss 4.5782, time 18814.69ms, mfu 0.05% +step 9500: train loss 4.4909, val loss 4.4994 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 9500: loss 4.5277, time 21865.02ms, mfu 0.05% +step 9750: train loss 4.4947, val loss 4.4710 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed1337_chaos +iter 9750: loss 4.4056, time 22027.08ms, mfu 0.05% +step 10000: train loss 4.4882, val loss 4.5129 +iter 10000: loss 4.5165, time 18593.37ms, mfu 0.05% + +🎯 RESULT | random_normal | seed=1337 | BEST=4.4710 | FINAL=4.5129 | ✗(best@9750) seed42=4.4982 + + +🏆 RUNNING LEADERBOARD (this session, seeds 1337+123 only): + 1. a_only 4.4352 vs clean baseline: -0.0621 + 2. baseline 4.5030 vs clean baseline: +0.0057 + 3. ones_identity 4.5032 vs clean baseline: +0.0059 + 4. random_normal 4.5129 vs clean baseline: +0.0156 + 5. random_uniform 4.5320 vs clean baseline: +0.0347 + + +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 +RUNNING: random_normal | seed=123 + chaos: normal(0.5,0.2) clamped [0,1] +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9595, val loss 10.9585 +iter 0: loss 10.9776, time 18508.40ms, mfu -100.00% +step 250: train loss 6.9626, val loss 6.9083 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 250: loss 6.6604, time 20899.31ms, mfu 0.05% +step 500: train loss 6.4919, val loss 6.5686 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 500: loss 6.6023, time 21554.15ms, mfu 0.05% +step 750: train loss 6.3107, val loss 6.3150 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 750: loss 5.6403, time 21711.54ms, mfu 0.05% +step 1000: train loss 6.1959, val loss 6.1962 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 1000: loss 6.4845, time 21848.93ms, mfu 0.05% +step 1250: train loss 6.0253, val loss 6.0533 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 1250: loss 5.7573, time 21881.21ms, mfu 0.05% +step 1500: train loss 5.9962, val loss 5.9627 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 1500: loss 5.8049, time 22810.24ms, mfu 0.05% +step 1750: train loss 5.8781, val loss 5.9224 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 1750: loss 6.5316, time 23908.08ms, mfu 0.05% +step 2000: train loss 5.8418, val loss 5.8756 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 2000: loss 5.7599, time 26479.67ms, mfu 0.05% +step 2250: train loss 5.6641, val loss 5.7212 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 2250: loss 6.1652, time 22463.17ms, mfu 0.05% +step 2500: train loss 5.6317, val loss 5.6348 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 2500: loss 5.8928, time 21954.56ms, mfu 0.05% +step 2750: train loss 5.5792, val loss 5.5511 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 2750: loss 6.1036, time 22140.82ms, mfu 0.05% +step 3000: train loss 5.4935, val loss 5.4985 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 3000: loss 6.2226, time 21954.79ms, mfu 0.05% +step 3250: train loss 5.3946, val loss 5.3911 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 3250: loss 5.3385, time 21755.81ms, mfu 0.05% +step 3500: train loss 5.3196, val loss 5.3704 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 3500: loss 5.3581, time 21713.04ms, mfu 0.05% +step 3750: train loss 5.2933, val loss 5.2781 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 3750: loss 5.0512, time 21727.86ms, mfu 0.05% +step 4000: train loss 5.2453, val loss 5.2531 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 4000: loss 5.1359, time 24208.70ms, mfu 0.05% +step 4250: train loss 5.2074, val loss 5.2180 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 4250: loss 5.2668, time 20237.40ms, mfu 0.05% +step 4500: train loss 5.1196, val loss 5.1221 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 4500: loss 5.1701, time 22718.77ms, mfu 0.05% +step 4750: train loss 5.1084, val loss 5.1102 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 4750: loss 5.0078, time 22082.16ms, mfu 0.05% +step 5000: train loss 5.0274, val loss 5.0243 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 5000: loss 5.2004, time 22375.82ms, mfu 0.05% +step 5250: train loss 5.0140, val loss 5.0025 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 5250: loss 5.1464, time 22103.67ms, mfu 0.05% +step 5500: train loss 4.9300, val loss 4.9691 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 5500: loss 4.8351, time 22361.97ms, mfu 0.05% +step 5750: train loss 4.9557, val loss 4.9347 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 5750: loss 5.3068, time 22820.88ms, mfu 0.05% +step 6000: train loss 4.8957, val loss 4.8641 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 6000: loss 4.9102, time 22580.75ms, mfu 0.05% +step 6250: train loss 4.8248, val loss 4.8772 +iter 6250: loss 5.0895, time 19060.71ms, mfu 0.05% +step 6500: train loss 4.7876, val loss 4.7949 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 6500: loss 4.6556, time 22113.30ms, mfu 0.05% +step 6750: train loss 4.7402, val loss 4.7637 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 6750: loss 4.4853, time 22599.46ms, mfu 0.05% +step 7000: train loss 4.7041, val loss 4.7132 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 7000: loss 4.6445, time 22327.85ms, mfu 0.05% +step 7250: train loss 4.6785, val loss 4.7020 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 7250: loss 4.2955, time 22225.29ms, mfu 0.05% +step 7500: train loss 4.6843, val loss 4.6652 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 7500: loss 4.6474, time 21906.81ms, mfu 0.05% +step 7750: train loss 4.6331, val loss 4.6292 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 7750: loss 4.7948, time 22163.81ms, mfu 0.05% +step 8000: train loss 4.6074, val loss 4.5992 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 8000: loss 4.2221, time 22430.39ms, mfu 0.05% +step 8250: train loss 4.5638, val loss 4.5605 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 8250: loss 4.3995, time 22339.84ms, mfu 0.05% +step 8500: train loss 4.5501, val loss 4.5683 +iter 8500: loss 4.3338, time 18423.94ms, mfu 0.05% +step 8750: train loss 4.5248, val loss 4.5384 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 8750: loss 4.3900, time 22495.98ms, mfu 0.05% +step 9000: train loss 4.5413, val loss 4.5209 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 9000: loss 4.2985, time 22251.97ms, mfu 0.05% +step 9250: train loss 4.4894, val loss 4.5137 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 9250: loss 5.0789, time 22233.18ms, mfu 0.05% +step 9500: train loss 4.4858, val loss 4.4824 +saving checkpoint to out-openwebtext_dynamic_random_normal_warmup2000_seed123_chaos +iter 9500: loss 4.9364, time 22138.66ms, mfu 0.05% +step 9750: train loss 4.5097, val loss 4.5314 +iter 9750: loss 4.4285, time 18984.86ms, mfu 0.05% +step 10000: train loss 4.4932, val loss 4.5023 +iter 10000: loss 4.6344, time 18884.51ms, mfu 0.05% + +🎯 RESULT | random_normal | seed=123 | BEST=4.4824 | FINAL=4.5023 | ✗(best@9500) seed42=4.4982 + + +🏆 RUNNING LEADERBOARD (this session, seeds 1337+123 only): + 1. a_only 4.4352 vs clean baseline: -0.0621 + 2. baseline 4.5030 vs clean baseline: +0.0057 + 3. ones_identity 4.5032 vs clean baseline: +0.0059 + 4. random_normal 4.5076 vs clean baseline: +0.0103 + 5. random_uniform 4.5320 vs clean baseline: +0.0347 + + +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 +RUNNING: bernoulli_drop | seed=1337 + spicy: binary 50% dropout x2 gate +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9905, val loss 10.9916 +iter 0: loss 11.0039, time 18987.62ms, mfu -100.00% +step 250: train loss 6.9689, val loss 7.0026 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 250: loss 6.7537, time 21907.36ms, mfu 0.05% +step 500: train loss 6.5109, val loss 6.5791 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 500: loss 6.2973, time 22077.00ms, mfu 0.05% +step 750: train loss 6.3378, val loss 6.3548 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 750: loss 6.1218, time 22079.62ms, mfu 0.05% +step 1000: train loss 6.2452, val loss 6.2173 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 1000: loss 6.2178, time 21820.72ms, mfu 0.05% +step 1250: train loss 6.1392, val loss 6.1339 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 1250: loss 6.1760, time 21600.65ms, mfu 0.05% +step 1500: train loss 6.0633, val loss 5.9918 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 1500: loss 5.7556, time 22039.03ms, mfu 0.05% +step 1750: train loss 5.9968, val loss 5.9612 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 1750: loss 6.1676, time 23828.68ms, mfu 0.05% +step 2000: train loss 5.9129, val loss 5.8828 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 2000: loss 5.9386, time 22048.87ms, mfu 0.05% +step 2250: train loss 5.8133, val loss 5.8160 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 2250: loss 6.0428, time 21933.44ms, mfu 0.05% +step 2500: train loss 5.7075, val loss 5.6905 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 2500: loss 6.1537, time 22129.98ms, mfu 0.05% +step 2750: train loss 5.6332, val loss 5.6286 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 2750: loss 5.6012, time 22209.63ms, mfu 0.05% +step 3000: train loss 5.5530, val loss 5.5706 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 3000: loss 5.6428, time 22096.20ms, mfu 0.05% +step 3250: train loss 5.4682, val loss 5.4729 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 3250: loss 5.4623, time 21910.35ms, mfu 0.05% +step 3500: train loss 5.4758, val loss 5.4393 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 3500: loss 5.3645, time 21890.47ms, mfu 0.05% +step 3750: train loss 5.3953, val loss 5.3830 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 3750: loss 5.6696, time 21895.24ms, mfu 0.05% +step 4000: train loss 5.3451, val loss 5.3513 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 4000: loss 5.4091, time 21950.26ms, mfu 0.05% +step 4250: train loss 5.2888, val loss 5.3094 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 4250: loss 4.8612, time 21949.61ms, mfu 0.05% +step 4500: train loss 5.2557, val loss 5.2471 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 4500: loss 5.0703, time 21678.31ms, mfu 0.05% +step 4750: train loss 5.2071, val loss 5.2161 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 4750: loss 5.3198, time 21888.63ms, mfu 0.05% +step 5000: train loss 5.1423, val loss 5.1795 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 5000: loss 5.3354, time 21828.07ms, mfu 0.05% +step 5250: train loss 5.1474, val loss 5.1286 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 5250: loss 4.7703, time 21882.55ms, mfu 0.05% +step 5500: train loss 5.1386, val loss 5.0958 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 5500: loss 5.3802, time 21933.12ms, mfu 0.05% +step 5750: train loss 5.0828, val loss 5.0346 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 5750: loss 5.3301, time 22012.05ms, mfu 0.05% +step 6000: train loss 5.0064, val loss 5.0004 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 6000: loss 4.8764, time 21889.82ms, mfu 0.05% +step 6250: train loss 4.9895, val loss 5.0176 +iter 6250: loss 4.7641, time 18663.26ms, mfu 0.05% +step 6500: train loss 4.9456, val loss 4.9793 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 6500: loss 5.1160, time 21558.27ms, mfu 0.05% +step 6750: train loss 4.9200, val loss 4.9289 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 6750: loss 5.2077, time 21766.11ms, mfu 0.05% +step 7000: train loss 4.8881, val loss 4.8845 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 7000: loss 4.8647, time 21690.65ms, mfu 0.05% +step 7250: train loss 4.8495, val loss 4.8652 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 7250: loss 4.9457, time 21694.97ms, mfu 0.05% +step 7500: train loss 4.8526, val loss 4.7995 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 7500: loss 4.7553, time 20026.93ms, mfu 0.05% +step 7750: train loss 4.7999, val loss 4.7884 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 7750: loss 4.9736, time 22853.88ms, mfu 0.05% +step 8000: train loss 4.7569, val loss 4.7861 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 8000: loss 4.8020, time 22013.01ms, mfu 0.05% +step 8250: train loss 4.7413, val loss 4.7601 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 8250: loss 4.5060, time 21559.45ms, mfu 0.05% +step 8500: train loss 4.7337, val loss 4.7342 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 8500: loss 4.4590, time 21803.35ms, mfu 0.05% +step 8750: train loss 4.6915, val loss 4.6922 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 8750: loss 4.6771, time 21551.33ms, mfu 0.05% +step 9000: train loss 4.7076, val loss 4.7040 +iter 9000: loss 4.7897, time 18475.08ms, mfu 0.05% +step 9250: train loss 4.6630, val loss 4.6977 +iter 9250: loss 4.7576, time 18445.10ms, mfu 0.05% +step 9500: train loss 4.6731, val loss 4.6844 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 9500: loss 4.6806, time 21592.91ms, mfu 0.05% +step 9750: train loss 4.6726, val loss 4.6500 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed1337_chaos +iter 9750: loss 4.6364, time 21562.66ms, mfu 0.05% +step 10000: train loss 4.6688, val loss 4.6947 +iter 10000: loss 4.6617, time 18440.57ms, mfu 0.05% + +🎯 RESULT | bernoulli_drop | seed=1337 | BEST=4.6500 | FINAL=4.6947 | ✗(best@9750) seed42=4.6653 + + +🏆 RUNNING LEADERBOARD (this session, seeds 1337+123 only): + 1. a_only 4.4352 vs clean baseline: -0.0621 + 2. baseline 4.5030 vs clean baseline: +0.0057 + 3. ones_identity 4.5032 vs clean baseline: +0.0059 + 4. random_normal 4.5076 vs clean baseline: +0.0103 + 5. random_uniform 4.5320 vs clean baseline: +0.0347 + 6. bernoulli_drop 4.6947 vs clean baseline: +0.1974 + + +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 +RUNNING: bernoulli_drop | seed=123 + spicy: binary 50% dropout x2 gate +🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲🎲 + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9359, val loss 10.9360 +iter 0: loss 10.9503, time 18700.80ms, mfu -100.00% +step 250: train loss 6.9958, val loss 6.9411 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 250: loss 6.6817, time 21263.40ms, mfu 0.05% +step 500: train loss 6.5187, val loss 6.5927 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 500: loss 6.6156, time 21442.95ms, mfu 0.05% +step 750: train loss 6.3441, val loss 6.3488 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 750: loss 5.6862, time 20013.91ms, mfu 0.05% +step 1000: train loss 6.2501, val loss 6.2511 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 1000: loss 6.5603, time 20036.89ms, mfu 0.05% +step 1250: train loss 6.0830, val loss 6.1122 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 1250: loss 5.8382, time 22106.06ms, mfu 0.05% +step 1500: train loss 6.0495, val loss 6.0226 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 1500: loss 5.8755, time 22009.39ms, mfu 0.05% +step 1750: train loss 5.9493, val loss 5.9933 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 1750: loss 6.6251, time 21891.35ms, mfu 0.05% +step 2000: train loss 5.9138, val loss 5.9510 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 2000: loss 5.8472, time 21789.98ms, mfu 0.05% +step 2250: train loss 5.7654, val loss 5.8216 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 2250: loss 6.2511, time 22089.13ms, mfu 0.05% +step 2500: train loss 5.7230, val loss 5.7287 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 2500: loss 5.9266, time 21650.19ms, mfu 0.05% +step 2750: train loss 5.6714, val loss 5.6459 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 2750: loss 6.1787, time 22100.99ms, mfu 0.05% +step 3000: train loss 5.5878, val loss 5.5869 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 3000: loss 6.3067, time 21889.85ms, mfu 0.05% +step 3250: train loss 5.5076, val loss 5.5026 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 3250: loss 5.4350, time 22038.81ms, mfu 0.05% +step 3500: train loss 5.4340, val loss 5.4825 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 3500: loss 5.4860, time 22080.92ms, mfu 0.05% +step 3750: train loss 5.3971, val loss 5.3826 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 3750: loss 5.1656, time 22219.72ms, mfu 0.05% +step 4000: train loss 5.3452, val loss 5.3617 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 4000: loss 5.2360, time 22607.30ms, mfu 0.05% +step 4250: train loss 5.3132, val loss 5.3208 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 4250: loss 5.3827, time 20203.35ms, mfu 0.05% +step 4500: train loss 5.2322, val loss 5.2365 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 4500: loss 5.3071, time 19668.63ms, mfu 0.05% +step 4750: train loss 5.2194, val loss 5.2162 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 4750: loss 5.1243, time 19667.24ms, mfu 0.05% +step 5000: train loss 5.1458, val loss 5.1411 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 5000: loss 5.3218, time 19707.63ms, mfu 0.05% +step 5250: train loss 5.1350, val loss 5.1235 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 5250: loss 5.2872, time 19695.30ms, mfu 0.05% +step 5500: train loss 5.0598, val loss 5.0984 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 5500: loss 4.9622, time 19795.27ms, mfu 0.05% +step 5750: train loss 5.0947, val loss 5.0735 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 5750: loss 5.4259, time 22285.94ms, mfu 0.05% +step 6000: train loss 5.0366, val loss 5.0053 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 6000: loss 5.0154, time 22492.50ms, mfu 0.05% +step 6250: train loss 4.9713, val loss 5.0224 +iter 6250: loss 5.1922, time 19090.10ms, mfu 0.05% +step 6500: train loss 4.9413, val loss 4.9450 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 6500: loss 4.8239, time 22255.87ms, mfu 0.05% +step 6750: train loss 4.8907, val loss 4.9191 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 6750: loss 4.6390, time 22106.20ms, mfu 0.05% +step 7000: train loss 4.8619, val loss 4.8721 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 7000: loss 4.7989, time 22357.15ms, mfu 0.05% +step 7250: train loss 4.8407, val loss 4.8572 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 7250: loss 4.5832, time 22014.93ms, mfu 0.05% +step 7500: train loss 4.8443, val loss 4.8280 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 7500: loss 4.8026, time 21984.34ms, mfu 0.05% +step 7750: train loss 4.7948, val loss 4.7893 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 7750: loss 4.9401, time 22396.97ms, mfu 0.05% +step 8000: train loss 4.7660, val loss 4.7612 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 8000: loss 4.4294, time 22354.85ms, mfu 0.05% +step 8250: train loss 4.7301, val loss 4.7265 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 8250: loss 4.5903, time 23084.85ms, mfu 0.05% +step 8500: train loss 4.7146, val loss 4.7319 +iter 8500: loss 4.5623, time 18963.59ms, mfu 0.05% +step 8750: train loss 4.6886, val loss 4.7021 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 8750: loss 4.5270, time 22049.18ms, mfu 0.05% +step 9000: train loss 4.7034, val loss 4.6854 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 9000: loss 4.4511, time 22066.14ms, mfu 0.05% +step 9250: train loss 4.6517, val loss 4.6769 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 9250: loss 5.2270, time 21710.33ms, mfu 0.05% +step 9500: train loss 4.6485, val loss 4.6417 +saving checkpoint to out-openwebtext_dynamic_bernoulli_gate_warmup2000_seed123_chaos +iter 9500: loss 5.0503, time 22040.93ms, mfu 0.05% +step 9750: train loss 4.6711, val loss 4.6892 +iter 9750: loss 4.5881, time 18895.55ms, mfu 0.05% +step 10000: train loss 4.6582, val loss 4.6618 +iter 10000: loss 4.7575, time 18695.72ms, mfu 0.05% + +🎯 RESULT | bernoulli_drop | seed=123 | BEST=4.6417 | FINAL=4.6618 | ✗(best@9500) seed42=4.6653 + + +🏆 RUNNING LEADERBOARD (this session, seeds 1337+123 only): + 1. a_only 4.4352 vs clean baseline: -0.0621 + 2. baseline 4.5030 vs clean baseline: +0.0057 + 3. ones_identity 4.5032 vs clean baseline: +0.0059 + 4. random_normal 4.5076 vs clean baseline: +0.0103 + 5. random_uniform 4.5320 vs clean baseline: +0.0347 + 6. bernoulli_drop 4.6783 vs clean baseline: +0.1810 + + +==================================================================================================== +🎲 CHAOS CONFIRMATION COMPLETE 🎲 +==================================================================================================== +Results CSV: chaos_confirmation_runs_20260321_2215.csv +Summary: chaos_confirmation_summary_20260321_2215.txt + +Paste summary here. AdamW tips his hat. +PS C:\Users\brian\documents\nanogpt> + + + + + + + python +Python 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] on win32 +Type "help", "copyright", "credits" or "license" for more information. +>>> exit +Use exit() or Ctrl-Z plus Return to exit +>>> ^Z + +PS C:\Users\brian\documents\nanogpt> python priority1_runner.py +==================================================================================================== +PRIORITY 1: HEAD-TO-HEAD CLEAN CONFIRMATION +==================================================================================================== +Variants: 4 | Seeds: [42, 1337, 123] | Total runs: 12 +Estimated time: ~13h 0min + +THE QUESTION: Does midtier_q beat cheap_qa on clean 3060? + A10 says midtier_q wins by 0.011 — does 3060 agree? + + baseline — no gate — session anchor A10: 4.4675 + midtier_q — per-head MLP(64->128->64) on Q A10: 4.4039 + cheap_qa — per-head Linear(128->64) on cat(Q,y) A10: 4.4151 + cheap_q — per-head Linear(64->64) on Q A10: 4.4222 +==================================================================================================== + +==================================================================================================== +RUNNING: baseline | warmup=2000 | seed=42 + no gate — session anchor + A10 reference: 4.4675 ±0.0070 +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9665, val loss 10.9665 +iter 0: loss 10.9808, time 17936.97ms, mfu -100.00% +step 250: train loss 7.0173, val loss 7.0114 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 250: loss 7.1147, time 19447.12ms, mfu 0.05% +step 500: train loss 6.5239, val loss 6.5303 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 500: loss 6.1702, time 20140.03ms, mfu 0.05% +step 750: train loss 6.2959, val loss 6.3277 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 750: loss 6.6320, time 19852.73ms, mfu 0.05% +step 1000: train loss 6.1612, val loss 6.1577 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 1000: loss 5.8185, time 19572.73ms, mfu 0.05% +step 1250: train loss 6.0250, val loss 6.0407 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 1250: loss 6.5403, time 23577.35ms, mfu 0.05% +step 1500: train loss 5.9773, val loss 5.9779 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 1500: loss 5.4982, time 25368.94ms, mfu 0.05% +step 1750: train loss 5.8977, val loss 5.9094 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 1750: loss 6.1775, time 22976.59ms, mfu 0.05% +step 2000: train loss 5.8149, val loss 5.8286 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 2000: loss 5.6582, time 22617.04ms, mfu 0.05% +step 2250: train loss 5.6911, val loss 5.7037 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 2250: loss 5.7741, time 22847.63ms, mfu 0.05% +step 2500: train loss 5.6197, val loss 5.6146 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 2500: loss 4.9313, time 22160.36ms, mfu 0.05% +step 2750: train loss 5.5307, val loss 5.5307 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 2750: loss 5.5800, time 22613.55ms, mfu 0.05% +step 3000: train loss 5.4630, val loss 5.4499 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 3000: loss 5.5103, time 21375.62ms, mfu 0.05% +step 3250: train loss 5.4180, val loss 5.3747 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 3250: loss 5.5858, time 22221.42ms, mfu 0.05% +step 3500: train loss 5.3227, val loss 5.3731 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 3500: loss 5.4316, time 22951.58ms, mfu 0.05% +step 3750: train loss 5.2827, val loss 5.3085 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 3750: loss 5.3177, time 20341.33ms, mfu 0.05% +step 4000: train loss 5.2321, val loss 5.2416 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 4000: loss 5.9029, time 19666.00ms, mfu 0.05% +step 4250: train loss 5.1971, val loss 5.1802 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 4250: loss 4.9298, time 19410.48ms, mfu 0.05% +step 4500: train loss 5.1052, val loss 5.1312 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 4500: loss 5.2018, time 19484.35ms, mfu 0.05% +step 4750: train loss 5.0866, val loss 5.0956 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 4750: loss 5.1813, time 19597.32ms, mfu 0.05% +step 5000: train loss 5.0395, val loss 5.0157 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 5000: loss 5.4377, time 19929.81ms, mfu 0.05% +step 5250: train loss 4.9664, val loss 4.9670 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 5250: loss 5.0735, time 19989.89ms, mfu 0.05% +step 5500: train loss 4.9536, val loss 4.9406 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 5500: loss 4.7022, time 19321.16ms, mfu 0.05% +step 5750: train loss 4.8734, val loss 4.8850 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 5750: loss 5.1317, time 19442.81ms, mfu 0.05% +step 6000: train loss 4.8227, val loss 4.8305 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 6000: loss 4.7719, time 20103.71ms, mfu 0.05% +step 6250: train loss 4.7734, val loss 4.7897 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 6250: loss 4.4725, time 19847.62ms, mfu 0.05% +step 6500: train loss 4.7766, val loss 4.7544 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 6500: loss 4.5966, time 19945.71ms, mfu 0.05% +step 6750: train loss 4.7085, val loss 4.7011 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 6750: loss 4.6962, time 19535.39ms, mfu 0.05% +step 7000: train loss 4.6634, val loss 4.6878 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 7000: loss 4.6272, time 21096.12ms, mfu 0.05% +step 7250: train loss 4.6482, val loss 4.6424 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 7250: loss 4.8432, time 20016.37ms, mfu 0.05% +step 7500: train loss 4.6467, val loss 4.6281 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 7500: loss 4.7554, time 19798.53ms, mfu 0.05% +step 7750: train loss 4.6242, val loss 4.6022 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 7750: loss 4.7188, time 20005.73ms, mfu 0.05% +step 8000: train loss 4.5789, val loss 4.5828 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 8000: loss 4.5509, time 20008.94ms, mfu 0.05% +step 8250: train loss 4.5368, val loss 4.5596 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 8250: loss 4.3755, time 19625.69ms, mfu 0.05% +step 8500: train loss 4.5474, val loss 4.5759 +iter 8500: loss 4.3916, time 17768.93ms, mfu 0.05% +step 8750: train loss 4.5009, val loss 4.5248 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 8750: loss 4.6828, time 19401.75ms, mfu 0.05% +step 9000: train loss 4.5158, val loss 4.5364 +iter 9000: loss 4.4994, time 17816.80ms, mfu 0.05% +step 9250: train loss 4.4891, val loss 4.4813 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 9250: loss 4.6104, time 19453.51ms, mfu 0.05% +step 9500: train loss 4.4625, val loss 4.4801 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 9500: loss 4.2105, time 19538.01ms, mfu 0.05% +step 9750: train loss 4.4596, val loss 4.4728 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p1 +iter 9750: loss 4.7720, time 20661.12ms, mfu 0.05% +step 10000: train loss 4.4586, val loss 4.4861 +iter 10000: loss 4.4794, time 17721.26ms, mfu 0.05% + +--> RESULT | baseline | seed=42 | BEST=4.4728 | FINAL=4.4861 | ✗(best@9750) A10=4.4675 delta=+0.0053 + + +==================================================================================================== +RUNNING: baseline | warmup=2000 | seed=1337 + no gate — session anchor + A10 reference: 4.4675 ±0.0070 +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9989, val loss 10.9985 +iter 0: loss 11.0107, time 17879.72ms, mfu -100.00% +step 250: train loss 6.9435, val loss 6.9764 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 250: loss 6.7374, time 19261.77ms, mfu 0.05% +step 500: train loss 6.4863, val loss 6.5542 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 500: loss 6.2682, time 19948.98ms, mfu 0.05% +step 750: train loss 6.3189, val loss 6.3361 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 750: loss 6.1047, time 19692.65ms, mfu 0.05% +step 1000: train loss 6.1859, val loss 6.1602 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 1000: loss 6.1561, time 19803.78ms, mfu 0.05% +step 1250: train loss 6.0765, val loss 6.0677 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 1250: loss 6.1194, time 22064.03ms, mfu 0.05% +step 1500: train loss 6.0058, val loss 5.9312 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 1500: loss 5.7234, time 20210.43ms, mfu 0.05% +step 1750: train loss 5.9373, val loss 5.9038 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 1750: loss 6.1188, time 19807.29ms, mfu 0.05% +step 2000: train loss 5.8407, val loss 5.8085 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 2000: loss 5.8599, time 20007.50ms, mfu 0.05% +step 2250: train loss 5.7297, val loss 5.7357 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 2250: loss 5.9092, time 19760.87ms, mfu 0.05% +step 2500: train loss 5.6143, val loss 5.5947 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 2500: loss 6.0601, time 19550.35ms, mfu 0.05% +step 2750: train loss 5.5272, val loss 5.5257 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 2750: loss 5.5186, time 19839.29ms, mfu 0.05% +step 3000: train loss 5.4578, val loss 5.4718 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 3000: loss 5.5503, time 19888.34ms, mfu 0.05% +step 3250: train loss 5.3743, val loss 5.3791 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 3250: loss 5.3673, time 20034.51ms, mfu 0.05% +step 3500: train loss 5.3784, val loss 5.3435 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 3500: loss 5.2718, time 19899.80ms, mfu 0.05% +step 3750: train loss 5.2877, val loss 5.2740 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 3750: loss 5.5683, time 19563.04ms, mfu 0.05% +step 4000: train loss 5.2390, val loss 5.2457 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 4000: loss 5.3291, time 19651.71ms, mfu 0.05% +step 4250: train loss 5.1742, val loss 5.1893 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 4250: loss 4.7855, time 19363.93ms, mfu 0.05% +step 4500: train loss 5.1361, val loss 5.1275 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 4500: loss 4.8186, time 19381.76ms, mfu 0.05% +step 4750: train loss 5.0687, val loss 5.0835 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 4750: loss 5.1753, time 20357.70ms, mfu 0.05% +step 5000: train loss 4.9947, val loss 5.0296 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 5000: loss 5.1650, time 19988.21ms, mfu 0.05% +step 5250: train loss 4.9883, val loss 4.9775 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 5250: loss 4.6685, time 20017.32ms, mfu 0.05% +step 5500: train loss 4.9578, val loss 4.9278 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 5500: loss 5.1785, time 19504.13ms, mfu 0.05% +step 5750: train loss 4.8957, val loss 4.8608 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 5750: loss 5.1933, time 19869.80ms, mfu 0.05% +step 6000: train loss 4.8047, val loss 4.8123 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 6000: loss 4.7268, time 19660.31ms, mfu 0.05% +step 6250: train loss 4.7862, val loss 4.8176 +iter 6250: loss 4.5956, time 17707.62ms, mfu 0.05% +step 6500: train loss 4.7548, val loss 4.7895 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 6500: loss 4.9465, time 19951.15ms, mfu 0.05% +step 6750: train loss 4.7176, val loss 4.7221 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 6750: loss 4.8718, time 19553.51ms, mfu 0.05% +step 7000: train loss 4.6822, val loss 4.6790 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 7000: loss 4.6137, time 19554.21ms, mfu 0.05% +step 7250: train loss 4.6372, val loss 4.6692 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 7250: loss 4.7452, time 19120.87ms, mfu 0.05% +step 7500: train loss 4.6479, val loss 4.6052 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 7500: loss 4.5283, time 19100.55ms, mfu 0.05% +step 7750: train loss 4.5979, val loss 4.5906 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 7750: loss 4.7843, time 19104.08ms, mfu 0.05% +step 8000: train loss 4.5546, val loss 4.5825 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 8000: loss 4.6442, time 19111.93ms, mfu 0.05% +step 8250: train loss 4.5388, val loss 4.5691 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 8250: loss 4.3195, time 19097.80ms, mfu 0.05% +step 8500: train loss 4.5346, val loss 4.5457 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 8500: loss 4.2848, time 19106.52ms, mfu 0.05% +step 8750: train loss 4.5002, val loss 4.5018 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 8750: loss 4.5173, time 19149.20ms, mfu 0.05% +step 9000: train loss 4.5137, val loss 4.5187 +iter 9000: loss 4.5903, time 17615.91ms, mfu 0.05% +step 9250: train loss 4.4762, val loss 4.5038 +iter 9250: loss 4.5743, time 17622.31ms, mfu 0.06% +step 9500: train loss 4.4815, val loss 4.4917 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 9500: loss 4.5145, time 19119.06ms, mfu 0.06% +step 9750: train loss 4.4874, val loss 4.4609 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p1 +iter 9750: loss 4.3914, time 19111.62ms, mfu 0.06% +step 10000: train loss 4.4783, val loss 4.5048 +iter 10000: loss 4.4922, time 17681.93ms, mfu 0.06% + +--> RESULT | baseline | seed=1337 | BEST=4.4609 | FINAL=4.5048 | ✗(best@9750) A10=4.4675 delta=-0.0066 + + +baseline running mean (2/3 seeds): 4.4668 ±0.0060 A10=4.4675 + + +==================================================================================================== +RUNNING: baseline | warmup=2000 | seed=123 + no gate — session anchor + A10 reference: 4.4675 ±0.0070 +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9288, val loss 10.9279 +iter 0: loss 10.9503, time 17723.51ms, mfu -100.00% +step 250: train loss 6.9746, val loss 6.9200 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 250: loss 6.6646, time 18862.69ms, mfu 0.06% +step 500: train loss 6.4967, val loss 6.5720 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 500: loss 6.6018, time 19064.23ms, mfu 0.06% +step 750: train loss 6.3135, val loss 6.3184 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 750: loss 5.6487, time 18994.29ms, mfu 0.06% +step 1000: train loss 6.2009, val loss 6.2001 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 1000: loss 6.5104, time 19123.96ms, mfu 0.06% +step 1250: train loss 6.0361, val loss 6.0655 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 1250: loss 5.7763, time 19399.85ms, mfu 0.06% +step 1500: train loss 6.0055, val loss 5.9733 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 1500: loss 5.8235, time 19471.15ms, mfu 0.06% +step 1750: train loss 5.8902, val loss 5.9309 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 1750: loss 6.5565, time 19030.11ms, mfu 0.06% +step 2000: train loss 5.8236, val loss 5.8599 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 2000: loss 5.7465, time 19108.29ms, mfu 0.06% +step 2250: train loss 5.6661, val loss 5.7265 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 2250: loss 6.1634, time 19136.96ms, mfu 0.06% +step 2500: train loss 5.6403, val loss 5.6453 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 2500: loss 5.8509, time 19146.31ms, mfu 0.06% +step 2750: train loss 5.5766, val loss 5.5501 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 2750: loss 6.1043, time 19092.29ms, mfu 0.06% +step 3000: train loss 5.4996, val loss 5.5006 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 3000: loss 6.1957, time 19046.97ms, mfu 0.06% +step 3250: train loss 5.4078, val loss 5.4037 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 3250: loss 5.3574, time 19187.62ms, mfu 0.05% +step 3500: train loss 5.3403, val loss 5.3907 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 3500: loss 5.3838, time 19067.97ms, mfu 0.05% +step 3750: train loss 5.2993, val loss 5.2877 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 3750: loss 5.0773, time 19067.18ms, mfu 0.05% +step 4000: train loss 5.2513, val loss 5.2663 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 4000: loss 5.1700, time 19076.50ms, mfu 0.05% +step 4250: train loss 5.2143, val loss 5.2241 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 4250: loss 5.2438, time 19118.65ms, mfu 0.05% +step 4500: train loss 5.1373, val loss 5.1407 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 4500: loss 5.1941, time 19148.15ms, mfu 0.05% +step 4750: train loss 5.1149, val loss 5.1184 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 4750: loss 5.0242, time 19034.13ms, mfu 0.05% +step 5000: train loss 5.0286, val loss 5.0254 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 5000: loss 5.1837, time 20897.28ms, mfu 0.05% +step 5250: train loss 5.0076, val loss 5.0002 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 5250: loss 5.1726, time 21190.00ms, mfu 0.05% +step 5500: train loss 4.9212, val loss 4.9618 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 5500: loss 4.8456, time 20967.52ms, mfu 0.05% +step 5750: train loss 4.9363, val loss 4.9173 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 5750: loss 5.2583, time 20910.07ms, mfu 0.05% +step 6000: train loss 4.8814, val loss 4.8500 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 6000: loss 4.8700, time 21015.99ms, mfu 0.05% +step 6250: train loss 4.8068, val loss 4.8592 +iter 6250: loss 5.0623, time 17847.37ms, mfu 0.05% +step 6500: train loss 4.7783, val loss 4.7865 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 6500: loss 4.6398, time 21638.57ms, mfu 0.05% +step 6750: train loss 4.7291, val loss 4.7523 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 6750: loss 4.4599, time 20823.99ms, mfu 0.05% +step 7000: train loss 4.6979, val loss 4.7056 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 7000: loss 4.6244, time 20901.82ms, mfu 0.05% +step 7250: train loss 4.6700, val loss 4.6981 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 7250: loss 4.2811, time 20957.56ms, mfu 0.05% +step 7500: train loss 4.6752, val loss 4.6607 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 7500: loss 4.6498, time 20667.03ms, mfu 0.05% +step 7750: train loss 4.6281, val loss 4.6285 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 7750: loss 4.8230, time 20921.31ms, mfu 0.05% +step 8000: train loss 4.6017, val loss 4.5974 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 8000: loss 4.1767, time 21194.04ms, mfu 0.05% +step 8250: train loss 4.5587, val loss 4.5559 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 8250: loss 4.3666, time 20987.48ms, mfu 0.05% +step 8500: train loss 4.5434, val loss 4.5647 +iter 8500: loss 4.3306, time 17907.68ms, mfu 0.05% +step 8750: train loss 4.5244, val loss 4.5363 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 8750: loss 4.3824, time 21159.17ms, mfu 0.05% +step 9000: train loss 4.5362, val loss 4.5192 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 9000: loss 4.3010, time 20777.58ms, mfu 0.05% +step 9250: train loss 4.4831, val loss 4.5118 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 9250: loss 5.0242, time 20971.57ms, mfu 0.05% +step 9500: train loss 4.4820, val loss 4.4786 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p1 +iter 9500: loss 4.9255, time 21474.09ms, mfu 0.05% +step 9750: train loss 4.5058, val loss 4.5285 +iter 9750: loss 4.4104, time 17860.79ms, mfu 0.05% +step 10000: train loss 4.4909, val loss 4.5010 +iter 10000: loss 4.6405, time 17865.15ms, mfu 0.05% + +--> RESULT | baseline | seed=123 | BEST=4.4786 | FINAL=4.5010 | ✗(best@9500) A10=4.4675 delta=+0.0111 + + +baseline running mean (3/3 seeds): 4.4708 ±0.0074 A10=4.4675 + + +==================================================================================================== +RUNNING: midtier_q | warmup=2000 | seed=42 + per-head MLP(64->128->64) on Q + A10 reference: 4.4039 ±0.0063 +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.78M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 74, with 124,157,952 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9295, val loss 10.9324 +iter 0: loss 10.9417, time 19511.32ms, mfu -100.00% +step 250: train loss 6.9449, val loss 6.9474 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 250: loss 6.9659, time 21734.85ms, mfu 0.05% +step 500: train loss 6.4945, val loss 6.5247 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 500: loss 6.5175, time 22344.22ms, mfu 0.05% +step 750: train loss 6.3150, val loss 6.3438 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 750: loss 6.3963, time 22258.01ms, mfu 0.05% +step 1000: train loss 6.1492, val loss 6.2012 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 1000: loss 6.0634, time 22041.85ms, mfu 0.05% +step 1250: train loss 6.0202, val loss 6.0309 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 1250: loss 5.6641, time 22053.64ms, mfu 0.05% +step 1500: train loss 5.9615, val loss 5.9649 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 1500: loss 5.6634, time 22646.74ms, mfu 0.05% +step 1750: train loss 5.8484, val loss 5.8645 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 1750: loss 6.3944, time 23870.40ms, mfu 0.05% +step 2000: train loss 5.7536, val loss 5.7871 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 2000: loss 5.4133, time 24431.94ms, mfu 0.05% +step 2250: train loss 5.6350, val loss 5.6418 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 2250: loss 5.6145, time 25161.82ms, mfu 0.05% +step 2500: train loss 5.5175, val loss 5.5693 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 2500: loss 5.7930, time 25446.33ms, mfu 0.05% +step 2750: train loss 5.4450, val loss 5.4634 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 2750: loss 5.2766, time 26584.29ms, mfu 0.05% +step 3000: train loss 5.3807, val loss 5.3778 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 3000: loss 5.1504, time 24318.41ms, mfu 0.05% +step 3250: train loss 5.3013, val loss 5.3307 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 3250: loss 5.0820, time 24604.34ms, mfu 0.04% +step 3500: train loss 5.2594, val loss 5.2420 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 3500: loss 5.1207, time 24668.90ms, mfu 0.04% +step 3750: train loss 5.2033, val loss 5.1938 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 3750: loss 5.2455, time 25065.60ms, mfu 0.04% +step 4000: train loss 5.1628, val loss 5.1239 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 4000: loss 5.1383, time 22816.06ms, mfu 0.04% +step 4250: train loss 5.1161, val loss 5.1030 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 4250: loss 4.6854, time 22807.13ms, mfu 0.04% +step 4500: train loss 5.0388, val loss 5.0313 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 4500: loss 4.5794, time 22507.29ms, mfu 0.04% +step 4750: train loss 4.9865, val loss 4.9998 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 4750: loss 5.1571, time 22466.90ms, mfu 0.05% +step 5000: train loss 4.9578, val loss 4.9708 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 5000: loss 4.6838, time 22566.50ms, mfu 0.05% +step 5250: train loss 4.8726, val loss 4.8791 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 5250: loss 4.9596, time 22340.99ms, mfu 0.05% +step 5500: train loss 4.8651, val loss 4.8415 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 5500: loss 5.0835, time 22542.14ms, mfu 0.05% +step 5750: train loss 4.8081, val loss 4.7717 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 5750: loss 4.7483, time 22607.53ms, mfu 0.05% +step 6000: train loss 4.7578, val loss 4.7515 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 6000: loss 4.6853, time 22664.56ms, mfu 0.05% +step 6250: train loss 4.7256, val loss 4.7074 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 6250: loss 4.5354, time 22667.17ms, mfu 0.05% +step 6500: train loss 4.6723, val loss 4.6960 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 6500: loss 5.1498, time 22511.20ms, mfu 0.05% +step 6750: train loss 4.6246, val loss 4.6322 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 6750: loss 4.5240, time 22645.53ms, mfu 0.05% +step 7000: train loss 4.6009, val loss 4.6017 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 7000: loss 5.5362, time 22375.41ms, mfu 0.05% +step 7250: train loss 4.5397, val loss 4.5922 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 7250: loss 4.6954, time 22307.49ms, mfu 0.05% +step 7500: train loss 4.5437, val loss 4.5354 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 7500: loss 4.3740, time 22900.80ms, mfu 0.05% +step 7750: train loss 4.5316, val loss 4.5532 +iter 7750: loss 4.4333, time 19256.03ms, mfu 0.05% +step 8000: train loss 4.5168, val loss 4.5234 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 8000: loss 4.3989, time 22577.19ms, mfu 0.05% +step 8250: train loss 4.4976, val loss 4.4896 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 8250: loss 4.4728, time 22652.77ms, mfu 0.05% +step 8500: train loss 4.4698, val loss 4.4758 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 8500: loss 4.4311, time 22244.51ms, mfu 0.05% +step 8750: train loss 4.4647, val loss 4.4693 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 8750: loss 4.1623, time 22498.50ms, mfu 0.05% +step 9000: train loss 4.4356, val loss 4.4486 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 9000: loss 4.6271, time 22358.19ms, mfu 0.05% +step 9250: train loss 4.4345, val loss 4.4465 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 9250: loss 4.3649, time 22380.75ms, mfu 0.05% +step 9500: train loss 4.4109, val loss 4.4260 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 9500: loss 4.3464, time 22676.97ms, mfu 0.05% +step 9750: train loss 4.4046, val loss 4.4266 +iter 9750: loss 4.5937, time 19432.02ms, mfu 0.05% +step 10000: train loss 4.4098, val loss 4.4085 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed42_p1 +iter 10000: loss 5.0394, time 23145.92ms, mfu 0.05% + +--> RESULT | midtier_q | seed=42 | BEST=4.4085 | FINAL=4.4085 | ✓ A10=4.4039 delta=+0.0046 + + +==================================================================================================== +RUNNING: midtier_q | warmup=2000 | seed=1337 + per-head MLP(64->128->64) on Q + A10 reference: 4.4039 ±0.0063 +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.78M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 74, with 124,157,952 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9835, val loss 10.9830 +iter 0: loss 10.9713, time 19802.92ms, mfu -100.00% +step 250: train loss 6.9419, val loss 6.9438 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 250: loss 6.9994, time 23337.78ms, mfu 0.04% +step 500: train loss 6.4951, val loss 6.5384 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 500: loss 6.3094, time 25345.63ms, mfu 0.04% +step 750: train loss 6.2861, val loss 6.3151 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 750: loss 6.1856, time 24105.52ms, mfu 0.04% +step 1000: train loss 6.1683, val loss 6.1705 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 1000: loss 6.2052, time 23436.60ms, mfu 0.04% +step 1250: train loss 6.0816, val loss 6.0762 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 1250: loss 6.9991, time 22932.71ms, mfu 0.04% +step 1500: train loss 5.9603, val loss 5.9820 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 1500: loss 5.8668, time 24891.88ms, mfu 0.04% +step 1750: train loss 5.8977, val loss 5.8808 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 1750: loss 5.9396, time 22437.64ms, mfu 0.04% +step 2000: train loss 5.7716, val loss 5.7586 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 2000: loss 6.2386, time 22414.45ms, mfu 0.04% +step 2250: train loss 5.6735, val loss 5.6736 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 2250: loss 5.2360, time 22457.83ms, mfu 0.05% +step 2500: train loss 5.5793, val loss 5.5893 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 2500: loss 5.8414, time 22501.31ms, mfu 0.05% +step 2750: train loss 5.4819, val loss 5.4758 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 2750: loss 5.3229, time 22558.12ms, mfu 0.05% +step 3000: train loss 5.4299, val loss 5.4132 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 3000: loss 5.2605, time 22368.08ms, mfu 0.05% +step 3250: train loss 5.3481, val loss 5.3866 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 3250: loss 5.3853, time 22337.09ms, mfu 0.05% +step 3500: train loss 5.3032, val loss 5.2661 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 3500: loss 5.3671, time 22497.46ms, mfu 0.05% +step 3750: train loss 5.2083, val loss 5.2528 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 3750: loss 5.3815, time 22417.54ms, mfu 0.05% +step 4000: train loss 5.1864, val loss 5.1631 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 4000: loss 5.3865, time 22378.58ms, mfu 0.05% +step 4250: train loss 5.1059, val loss 5.1361 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 4250: loss 5.1928, time 22204.90ms, mfu 0.05% +step 4500: train loss 5.0727, val loss 5.0747 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 4500: loss 4.9299, time 22307.51ms, mfu 0.05% +step 4750: train loss 5.0123, val loss 5.0076 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 4750: loss 4.2716, time 22214.51ms, mfu 0.05% +step 5000: train loss 4.9651, val loss 4.9870 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 5000: loss 4.4896, time 22338.91ms, mfu 0.05% +step 5250: train loss 4.8627, val loss 4.9295 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 5250: loss 5.0377, time 22489.45ms, mfu 0.05% +step 5500: train loss 4.8701, val loss 4.8647 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 5500: loss 4.7797, time 22695.06ms, mfu 0.05% +step 5750: train loss 4.8237, val loss 4.8131 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 5750: loss 4.6805, time 22391.54ms, mfu 0.05% +step 6000: train loss 4.7539, val loss 4.7973 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 6000: loss 4.6319, time 22216.42ms, mfu 0.05% +step 6250: train loss 4.6874, val loss 4.7141 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 6250: loss 5.0631, time 22108.17ms, mfu 0.05% +step 6500: train loss 4.6890, val loss 4.6911 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 6500: loss 4.8336, time 22244.30ms, mfu 0.05% +step 6750: train loss 4.6257, val loss 4.6767 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 6750: loss 4.4706, time 22267.76ms, mfu 0.05% +step 7000: train loss 4.6298, val loss 4.6694 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 7000: loss 4.7988, time 22210.72ms, mfu 0.05% +step 7250: train loss 4.5958, val loss 4.6000 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 7250: loss 4.7948, time 22275.85ms, mfu 0.05% +step 7500: train loss 4.5488, val loss 4.5707 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 7500: loss 4.8214, time 22487.49ms, mfu 0.05% +step 7750: train loss 4.5598, val loss 4.5245 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 7750: loss 4.3518, time 22365.73ms, mfu 0.05% +step 8000: train loss 4.5102, val loss 4.5204 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 8000: loss 4.4666, time 22296.92ms, mfu 0.05% +step 8250: train loss 4.5068, val loss 4.5092 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 8250: loss 4.9091, time 22297.72ms, mfu 0.05% +step 8500: train loss 4.4665, val loss 4.5149 +iter 8500: loss 4.5066, time 19287.38ms, mfu 0.05% +step 8750: train loss 4.4669, val loss 4.4618 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 8750: loss 4.8043, time 22215.91ms, mfu 0.05% +step 9000: train loss 4.4523, val loss 4.4635 +iter 9000: loss 4.4699, time 19082.24ms, mfu 0.05% +step 9250: train loss 4.4463, val loss 4.4323 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 9250: loss 4.5899, time 22328.21ms, mfu 0.05% +step 9500: train loss 4.4430, val loss 4.4413 +iter 9500: loss 4.4018, time 19318.69ms, mfu 0.05% +step 9750: train loss 4.4119, val loss 4.4360 +iter 9750: loss 4.1331, time 19192.21ms, mfu 0.05% +step 10000: train loss 4.4181, val loss 4.4256 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed1337_p1 +iter 10000: loss 4.3075, time 22345.76ms, mfu 0.05% + +--> RESULT | midtier_q | seed=1337 | BEST=4.4256 | FINAL=4.4256 | ✓ A10=4.4039 delta=+0.0217 + + +midtier_q running mean (2/3 seeds): 4.4170 ±0.0086 A10=4.4039 + + +==================================================================================================== +RUNNING: midtier_q | warmup=2000 | seed=123 + per-head MLP(64->128->64) on Q + A10 reference: 4.4039 ±0.0063 +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.78M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 74, with 124,157,952 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 11.0155, val loss 11.0141 +iter 0: loss 10.9955, time 19747.51ms, mfu -100.00% +step 250: train loss 6.9260, val loss 6.9377 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 250: loss 6.9170, time 21896.68ms, mfu 0.05% +step 500: train loss 6.4699, val loss 6.5021 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 500: loss 6.1416, time 22245.03ms, mfu 0.05% +step 750: train loss 6.3410, val loss 6.3593 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 750: loss 6.3482, time 22420.52ms, mfu 0.05% +step 1000: train loss 6.2121, val loss 6.1606 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 1000: loss 5.8295, time 22413.24ms, mfu 0.05% +step 1250: train loss 6.0335, val loss 6.0687 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 1250: loss 6.0123, time 22439.74ms, mfu 0.05% +step 1500: train loss 5.9415, val loss 6.0020 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 1500: loss 5.6814, time 22262.53ms, mfu 0.05% +step 1750: train loss 5.8657, val loss 5.8639 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 1750: loss 5.6780, time 22191.43ms, mfu 0.05% +step 2000: train loss 5.7053, val loss 5.7509 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 2000: loss 5.9279, time 23253.42ms, mfu 0.05% +step 2250: train loss 5.6437, val loss 5.6599 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 2250: loss 5.6502, time 22224.09ms, mfu 0.05% +step 2500: train loss 5.5505, val loss 5.5311 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 2500: loss 5.0544, time 22360.80ms, mfu 0.05% +step 2750: train loss 5.4961, val loss 5.4881 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 2750: loss 5.7172, time 22639.77ms, mfu 0.05% +step 3000: train loss 5.3832, val loss 5.4156 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 3000: loss 5.2362, time 22185.42ms, mfu 0.05% +step 3250: train loss 5.3102, val loss 5.3173 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 3250: loss 5.3899, time 22394.60ms, mfu 0.05% +step 3500: train loss 5.2768, val loss 5.3071 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 3500: loss 5.4649, time 22193.99ms, mfu 0.05% +step 3750: train loss 5.1992, val loss 5.2256 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 3750: loss 5.2399, time 22170.13ms, mfu 0.05% +step 4000: train loss 5.1804, val loss 5.1429 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 4000: loss 5.0902, time 22329.50ms, mfu 0.05% +step 4250: train loss 5.1082, val loss 5.1301 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 4250: loss 5.2936, time 22270.94ms, mfu 0.05% +step 4500: train loss 5.0602, val loss 5.0829 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 4500: loss 4.8494, time 22354.96ms, mfu 0.05% +step 4750: train loss 5.0149, val loss 5.0239 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 4750: loss 4.6547, time 22282.84ms, mfu 0.05% +step 5000: train loss 4.9479, val loss 4.9706 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 5000: loss 5.0937, time 22228.66ms, mfu 0.05% +step 5250: train loss 4.9175, val loss 4.9489 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 5250: loss 4.9619, time 22253.01ms, mfu 0.05% +step 5500: train loss 4.8500, val loss 4.8413 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 5500: loss 4.6478, time 22247.80ms, mfu 0.05% +step 5750: train loss 4.8289, val loss 4.8377 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 5750: loss 4.6585, time 22281.64ms, mfu 0.05% +step 6000: train loss 4.7710, val loss 4.7615 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 6000: loss 4.9159, time 22307.48ms, mfu 0.05% +step 6250: train loss 4.7315, val loss 4.7293 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 6250: loss 4.8889, time 22199.90ms, mfu 0.05% +step 6500: train loss 4.7032, val loss 4.6902 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 6500: loss 4.4305, time 22014.06ms, mfu 0.05% +step 6750: train loss 4.6433, val loss 4.6496 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 6750: loss 4.6124, time 22143.40ms, mfu 0.05% +step 7000: train loss 4.6480, val loss 4.6464 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 7000: loss 4.5250, time 22231.33ms, mfu 0.05% +step 7250: train loss 4.6155, val loss 4.6216 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 7250: loss 4.5859, time 22148.72ms, mfu 0.05% +step 7500: train loss 4.5667, val loss 4.5768 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 7500: loss 4.3690, time 22380.15ms, mfu 0.05% +step 7750: train loss 4.5336, val loss 4.5576 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 7750: loss 4.8122, time 22039.48ms, mfu 0.05% +step 8000: train loss 4.5038, val loss 4.5237 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 8000: loss 4.4896, time 22348.29ms, mfu 0.05% +step 8250: train loss 4.4970, val loss 4.5154 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 8250: loss 4.3391, time 22315.72ms, mfu 0.05% +step 8500: train loss 4.4796, val loss 4.4977 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 8500: loss 4.2657, time 22230.80ms, mfu 0.05% +step 8750: train loss 4.4745, val loss 4.4717 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 8750: loss 4.6176, time 22170.82ms, mfu 0.05% +step 9000: train loss 4.4577, val loss 4.4556 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 9000: loss 4.4926, time 22311.77ms, mfu 0.05% +step 9250: train loss 4.4450, val loss 4.4680 +iter 9250: loss 4.7226, time 19135.27ms, mfu 0.05% +step 9500: train loss 4.4516, val loss 4.4487 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 9500: loss 4.3394, time 22346.73ms, mfu 0.05% +step 9750: train loss 4.3983, val loss 4.4317 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 9750: loss 4.2679, time 22360.55ms, mfu 0.05% +step 10000: train loss 4.4440, val loss 4.4065 +saving checkpoint to out-openwebtext_dynamic_qconditioned_mlp128_warmup2000_seed123_p1 +iter 10000: loss 4.7379, time 22365.18ms, mfu 0.05% + +--> RESULT | midtier_q | seed=123 | BEST=4.4065 | FINAL=4.4065 | ✓ A10=4.4039 delta=+0.0026 + + +midtier_q running mean (3/3 seeds): 4.4135 ±0.0086 A10=4.4039 + + +🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆 +CURRENT LEADERBOARD (3-seed complete): + 1. midtier_q 4.4135 ±0.0086 A10=4.4039 delta=+0.0096 + 2. baseline 4.4708 ±0.0074 A10=4.4675 delta=+0.0033 +🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆 + + +==================================================================================================== +RUNNING: cheap_qa | warmup=2000 | seed=42 + per-head Linear(128->64) on cat(Q,y) + A10 reference: 4.4151 ±0.0156 +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.69M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 124,059,648 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9432, val loss 10.9442 +iter 0: loss 10.9369, time 19297.56ms, mfu -100.00% +step 250: train loss 6.9464, val loss 6.9443 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 250: loss 6.8098, time 21719.77ms, mfu 0.05% +step 500: train loss 6.5256, val loss 6.5025 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 500: loss 6.3880, time 22128.25ms, mfu 0.05% +step 750: train loss 6.3276, val loss 6.3224 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 750: loss 6.5631, time 21714.37ms, mfu 0.05% +step 1000: train loss 6.1672, val loss 6.1650 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 1000: loss 5.9097, time 21853.91ms, mfu 0.05% +step 1250: train loss 6.0470, val loss 6.0747 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 1250: loss 5.9095, time 21966.25ms, mfu 0.05% +step 1500: train loss 5.9614, val loss 5.9866 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 1500: loss 5.7680, time 22120.49ms, mfu 0.05% +step 1750: train loss 5.8630, val loss 5.8707 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 1750: loss 6.8122, time 21815.57ms, mfu 0.05% +step 2000: train loss 5.7309, val loss 5.7379 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 2000: loss 5.6604, time 21872.59ms, mfu 0.05% +step 2250: train loss 5.6509, val loss 5.6715 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 2250: loss 5.5711, time 21797.67ms, mfu 0.05% +step 2500: train loss 5.5474, val loss 5.5299 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 2500: loss 5.5322, time 22001.64ms, mfu 0.05% +step 2750: train loss 5.4497, val loss 5.4564 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 2750: loss 5.6305, time 21960.94ms, mfu 0.05% +step 3000: train loss 5.4212, val loss 5.3855 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 3000: loss 5.4377, time 21831.24ms, mfu 0.05% +step 3250: train loss 5.3026, val loss 5.3133 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 3250: loss 5.4872, time 21865.88ms, mfu 0.05% +step 3500: train loss 5.2365, val loss 5.2626 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 3500: loss 5.3583, time 22154.08ms, mfu 0.05% +step 3750: train loss 5.2123, val loss 5.1945 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 3750: loss 5.3440, time 21834.11ms, mfu 0.05% +step 4000: train loss 5.1566, val loss 5.1569 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 4000: loss 4.9826, time 22186.81ms, mfu 0.05% +step 4250: train loss 5.0774, val loss 5.0752 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 4250: loss 4.8762, time 21868.94ms, mfu 0.05% +step 4500: train loss 5.0370, val loss 5.0603 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 4500: loss 5.2007, time 22079.64ms, mfu 0.05% +step 4750: train loss 4.9871, val loss 5.0027 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 4750: loss 5.2100, time 21887.77ms, mfu 0.05% +step 5000: train loss 4.9230, val loss 4.9469 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 5000: loss 4.9213, time 21819.25ms, mfu 0.05% +step 5250: train loss 4.8828, val loss 4.8995 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 5250: loss 5.1515, time 21844.51ms, mfu 0.05% +step 5500: train loss 4.8481, val loss 4.8575 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 5500: loss 4.6985, time 21842.56ms, mfu 0.05% +step 5750: train loss 4.7996, val loss 4.8277 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 5750: loss 4.6721, time 21895.74ms, mfu 0.05% +step 6000: train loss 4.7499, val loss 4.7636 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 6000: loss 4.6558, time 21832.82ms, mfu 0.05% +step 6250: train loss 4.6966, val loss 4.7399 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 6250: loss 4.8581, time 21979.85ms, mfu 0.05% +step 6500: train loss 4.6636, val loss 4.6537 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 6500: loss 4.5128, time 21789.70ms, mfu 0.05% +step 6750: train loss 4.6314, val loss 4.6249 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 6750: loss 4.5366, time 22196.47ms, mfu 0.05% +step 7000: train loss 4.6087, val loss 4.6008 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 7000: loss 4.3556, time 21851.03ms, mfu 0.05% +step 7250: train loss 4.5849, val loss 4.5703 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 7250: loss 4.7371, time 22041.14ms, mfu 0.05% +step 7500: train loss 4.5094, val loss 4.5627 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 7500: loss 4.6018, time 22309.84ms, mfu 0.05% +step 7750: train loss 4.5207, val loss 4.5301 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 7750: loss 4.3391, time 20840.70ms, mfu 0.05% +step 8000: train loss 4.4605, val loss 4.5055 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 8000: loss 4.6651, time 21673.76ms, mfu 0.05% +step 8250: train loss 4.4978, val loss 4.4907 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 8250: loss 4.0409, time 20749.50ms, mfu 0.05% +step 8500: train loss 4.4979, val loss 4.4632 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 8500: loss 4.6652, time 21266.56ms, mfu 0.05% +step 8750: train loss 4.4250, val loss 4.4740 +iter 8750: loss 4.2908, time 19353.79ms, mfu 0.05% +step 9000: train loss 4.4374, val loss 4.4436 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 9000: loss 4.0684, time 21057.68ms, mfu 0.05% +step 9250: train loss 4.4281, val loss 4.4618 +iter 9250: loss 4.2506, time 18941.05ms, mfu 0.05% +step 9500: train loss 4.4199, val loss 4.3986 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 9500: loss 4.4186, time 20959.11ms, mfu 0.05% +step 9750: train loss 4.4172, val loss 4.4262 +iter 9750: loss 3.9049, time 18941.47ms, mfu 0.05% +step 10000: train loss 4.3761, val loss 4.3830 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed42_p1 +iter 10000: loss 4.2532, time 21030.05ms, mfu 0.05% + +--> RESULT | cheap_qa | seed=42 | BEST=4.3830 | FINAL=4.3830 | ✓ A10=4.4151 delta=-0.0321 + + +==================================================================================================== +RUNNING: cheap_qa | warmup=2000 | seed=1337 + per-head Linear(128->64) on cat(Q,y) + A10 reference: 4.4151 ±0.0156 +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.69M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 124,059,648 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9681, val loss 10.9657 +iter 0: loss 10.9667, time 19146.08ms, mfu -100.00% +step 250: train loss 6.9439, val loss 6.8834 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 250: loss 7.1391, time 20625.63ms, mfu 0.05% +step 500: train loss 6.4997, val loss 6.5077 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 500: loss 7.0345, time 21144.69ms, mfu 0.05% +step 750: train loss 6.2830, val loss 6.2970 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 750: loss 6.3402, time 23046.18ms, mfu 0.05% +step 1000: train loss 6.1999, val loss 6.1636 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 1000: loss 6.0845, time 23689.10ms, mfu 0.05% +step 1250: train loss 6.0314, val loss 6.0282 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 1250: loss 6.2345, time 23950.01ms, mfu 0.05% +step 1500: train loss 5.9264, val loss 5.9280 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 1500: loss 7.0015, time 22685.23ms, mfu 0.05% +step 1750: train loss 5.8556, val loss 5.8529 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 1750: loss 5.4523, time 21005.84ms, mfu 0.05% +step 2000: train loss 5.7864, val loss 5.7658 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 2000: loss 5.2539, time 21218.85ms, mfu 0.05% +step 2250: train loss 5.6816, val loss 5.6933 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 2250: loss 5.1183, time 20622.24ms, mfu 0.05% +step 2500: train loss 5.5495, val loss 5.5599 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 2500: loss 4.9835, time 21142.87ms, mfu 0.05% +step 2750: train loss 5.4537, val loss 5.4696 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 2750: loss 5.1648, time 23432.08ms, mfu 0.05% +step 3000: train loss 5.3622, val loss 5.3962 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 3000: loss 5.3210, time 20495.33ms, mfu 0.05% +step 3250: train loss 5.2923, val loss 5.2561 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 3250: loss 5.2728, time 20796.37ms, mfu 0.05% +step 3500: train loss 5.2534, val loss 5.2446 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 3500: loss 5.7601, time 21525.06ms, mfu 0.05% +step 3750: train loss 5.1996, val loss 5.2251 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 3750: loss 5.0799, time 23462.73ms, mfu 0.05% +step 4000: train loss 5.1560, val loss 5.1154 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 4000: loss 5.2552, time 22787.31ms, mfu 0.05% +step 4250: train loss 5.0879, val loss 5.1189 +iter 4250: loss 5.2404, time 18928.16ms, mfu 0.05% +step 4500: train loss 5.0170, val loss 5.0613 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 4500: loss 5.2547, time 21189.19ms, mfu 0.05% +step 4750: train loss 4.9785, val loss 4.9454 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 4750: loss 5.0527, time 21743.29ms, mfu 0.05% +step 5000: train loss 4.9038, val loss 4.9428 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 5000: loss 5.1730, time 21069.91ms, mfu 0.05% +step 5250: train loss 4.8716, val loss 4.8956 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 5250: loss 4.6418, time 20573.39ms, mfu 0.05% +step 5500: train loss 4.8300, val loss 4.8227 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 5500: loss 4.7325, time 20632.36ms, mfu 0.05% +step 5750: train loss 4.7868, val loss 4.7819 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 5750: loss 4.8554, time 21194.66ms, mfu 0.05% +step 6000: train loss 4.7947, val loss 4.7759 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 6000: loss 4.8722, time 20863.31ms, mfu 0.05% +step 6250: train loss 4.6950, val loss 4.7078 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 6250: loss 4.3704, time 21145.86ms, mfu 0.05% +step 6500: train loss 4.7034, val loss 4.7165 +iter 6500: loss 4.7968, time 18922.83ms, mfu 0.05% +step 6750: train loss 4.6338, val loss 4.6285 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 6750: loss 4.8291, time 20695.73ms, mfu 0.05% +step 7000: train loss 4.5941, val loss 4.5998 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 7000: loss 4.9143, time 21048.55ms, mfu 0.05% +step 7250: train loss 4.6035, val loss 4.5698 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 7250: loss 4.2003, time 21107.81ms, mfu 0.05% +step 7500: train loss 4.5262, val loss 4.5402 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 7500: loss 4.5650, time 20867.16ms, mfu 0.05% +step 7750: train loss 4.5482, val loss 4.5275 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 7750: loss 4.3415, time 21102.60ms, mfu 0.05% +step 8000: train loss 4.4935, val loss 4.5382 +iter 8000: loss 4.3986, time 18849.12ms, mfu 0.05% +step 8250: train loss 4.4754, val loss 4.5020 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 8250: loss 4.0325, time 25070.77ms, mfu 0.05% +step 8500: train loss 4.4613, val loss 4.5097 +iter 8500: loss 4.5330, time 18876.40ms, mfu 0.05% +step 8750: train loss 4.4729, val loss 4.4688 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 8750: loss 4.8010, time 21047.50ms, mfu 0.05% +step 9000: train loss 4.4273, val loss 4.4249 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 9000: loss 4.0647, time 21094.27ms, mfu 0.05% +step 9250: train loss 4.4159, val loss 4.4174 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 9250: loss 4.3284, time 20820.54ms, mfu 0.05% +step 9500: train loss 4.4532, val loss 4.4307 +iter 9500: loss 4.6532, time 18773.32ms, mfu 0.05% +step 9750: train loss 4.4133, val loss 4.3907 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed1337_p1 +iter 9750: loss 4.4217, time 20790.21ms, mfu 0.05% +step 10000: train loss 4.4287, val loss 4.4321 +iter 10000: loss 4.6872, time 18846.06ms, mfu 0.05% + +--> RESULT | cheap_qa | seed=1337 | BEST=4.3907 | FINAL=4.4321 | ✗(best@9750) A10=4.4151 delta=-0.0244 + + +cheap_qa running mean (2/3 seeds): 4.3868 ±0.0038 A10=4.4151 + + +==================================================================================================== +RUNNING: cheap_qa | warmup=2000 | seed=123 + per-head Linear(128->64) on cat(Q,y) + A10 reference: 4.4151 ±0.0156 +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.69M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 124,059,648 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9516, val loss 10.9516 +iter 0: loss 10.9851, time 19034.18ms, mfu -100.00% +step 250: train loss 6.9361, val loss 6.9513 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 250: loss 7.1897, time 20332.83ms, mfu 0.05% +step 500: train loss 6.4976, val loss 6.5282 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 500: loss 6.3189, time 21241.65ms, mfu 0.05% +step 750: train loss 6.3338, val loss 6.3004 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 750: loss 6.6119, time 20966.35ms, mfu 0.05% +step 1000: train loss 6.1623, val loss 6.1566 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 1000: loss 5.9930, time 20619.28ms, mfu 0.05% +step 1250: train loss 6.0519, val loss 6.0571 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 1250: loss 6.4881, time 20539.37ms, mfu 0.05% +step 1500: train loss 5.9488, val loss 5.9454 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 1500: loss 5.6090, time 21201.47ms, mfu 0.05% +step 1750: train loss 5.8470, val loss 5.8504 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 1750: loss 5.9612, time 20519.43ms, mfu 0.05% +step 2000: train loss 5.7430, val loss 5.7674 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 2000: loss 5.5835, time 20393.96ms, mfu 0.05% +step 2250: train loss 5.6230, val loss 5.6190 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 2250: loss 5.3869, time 21257.60ms, mfu 0.05% +step 2500: train loss 5.4897, val loss 5.5237 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 2500: loss 5.6314, time 25362.32ms, mfu 0.05% +step 2750: train loss 5.4431, val loss 5.4455 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 2750: loss 5.4780, time 20953.70ms, mfu 0.05% +step 3000: train loss 5.3484, val loss 5.3852 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 3000: loss 5.3008, time 20809.22ms, mfu 0.05% +step 3250: train loss 5.3041, val loss 5.2697 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 3250: loss 4.7966, time 21008.67ms, mfu 0.05% +step 3500: train loss 5.2441, val loss 5.2347 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 3500: loss 5.0353, time 21065.96ms, mfu 0.05% +step 3750: train loss 5.1647, val loss 5.1820 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 3750: loss 5.0720, time 20764.41ms, mfu 0.05% +step 4000: train loss 5.1293, val loss 5.1533 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 4000: loss 5.0828, time 20657.87ms, mfu 0.05% +step 4250: train loss 5.0671, val loss 5.0556 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 4250: loss 4.9607, time 21061.44ms, mfu 0.05% +step 4500: train loss 5.0200, val loss 5.0096 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 4500: loss 4.8567, time 21060.18ms, mfu 0.05% +step 4750: train loss 4.9367, val loss 4.9242 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 4750: loss 5.4105, time 20639.97ms, mfu 0.05% +step 5000: train loss 4.8965, val loss 4.8675 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 5000: loss 4.5519, time 21210.43ms, mfu 0.05% +step 5250: train loss 4.8794, val loss 4.8789 +iter 5250: loss 4.3292, time 18893.37ms, mfu 0.05% +step 5500: train loss 4.7834, val loss 4.8240 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 5500: loss 4.5486, time 20798.74ms, mfu 0.05% +step 5750: train loss 4.7661, val loss 4.7585 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 5750: loss 4.8846, time 20543.58ms, mfu 0.05% +step 6000: train loss 4.7215, val loss 4.7393 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 6000: loss 4.7615, time 20492.12ms, mfu 0.05% +step 6250: train loss 4.6793, val loss 4.6568 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 6250: loss 4.7572, time 20968.72ms, mfu 0.05% +step 6500: train loss 4.6416, val loss 4.6695 +iter 6500: loss 5.1601, time 18949.84ms, mfu 0.05% +step 6750: train loss 4.5906, val loss 4.5985 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 6750: loss 4.3218, time 22889.35ms, mfu 0.05% +step 7000: train loss 4.6064, val loss 4.5902 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 7000: loss 4.7886, time 20757.53ms, mfu 0.05% +step 7250: train loss 4.5693, val loss 4.5682 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 7250: loss 4.5583, time 20784.73ms, mfu 0.05% +step 7500: train loss 4.5049, val loss 4.5281 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 7500: loss 4.6751, time 20463.40ms, mfu 0.05% +step 7750: train loss 4.4818, val loss 4.4947 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 7750: loss 5.0411, time 20683.34ms, mfu 0.05% +step 8000: train loss 4.4941, val loss 4.5080 +iter 8000: loss 4.5957, time 18858.35ms, mfu 0.05% +step 8250: train loss 4.4332, val loss 4.4838 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 8250: loss 4.7195, time 20648.83ms, mfu 0.05% +step 8500: train loss 4.4413, val loss 4.4609 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 8500: loss 3.9416, time 20919.87ms, mfu 0.05% +step 8750: train loss 4.4556, val loss 4.4668 +iter 8750: loss 4.7652, time 18882.81ms, mfu 0.05% +step 9000: train loss 4.4112, val loss 4.4378 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 9000: loss 3.8902, time 20772.00ms, mfu 0.05% +step 9250: train loss 4.3837, val loss 4.4142 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 9250: loss 4.1814, time 20775.73ms, mfu 0.05% +step 9500: train loss 4.3980, val loss 4.4141 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 9500: loss 4.6705, time 20895.54ms, mfu 0.05% +step 9750: train loss 4.3691, val loss 4.3953 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_warmup2000_seed123_p1 +iter 9750: loss 4.6076, time 20393.48ms, mfu 0.05% +step 10000: train loss 4.3781, val loss 4.4047 +iter 10000: loss 4.4630, time 18820.56ms, mfu 0.05% + +--> RESULT | cheap_qa | seed=123 | BEST=4.3953 | FINAL=4.4047 | ✗(best@9750) A10=4.4151 delta=-0.0198 + + +cheap_qa running mean (3/3 seeds): 4.3897 ±0.0051 A10=4.4151 + + +🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆 +CURRENT LEADERBOARD (3-seed complete): + 1. cheap_qa 4.3897 ±0.0051 A10=4.4151 delta=-0.0254 + 2. midtier_q 4.4135 ±0.0086 A10=4.4039 delta=+0.0096 + 3. baseline 4.4708 ±0.0074 A10=4.4675 delta=+0.0033 +🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆 + + +==================================================================================================== +RUNNING: cheap_q | warmup=2000 | seed=42 + per-head Linear(64->64) on Q + A10 reference: 4.4222 ±0.0064 +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.64M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 124,010,496 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9651, val loss 10.9621 +iter 0: loss 10.9454, time 18819.99ms, mfu -100.00% +step 250: train loss 6.9242, val loss 6.9413 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 250: loss 6.8599, time 19918.31ms, mfu 0.05% +step 500: train loss 6.4783, val loss 6.5236 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 500: loss 6.2171, time 20568.38ms, mfu 0.05% +step 750: train loss 6.3210, val loss 6.3302 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 750: loss 6.2990, time 20523.59ms, mfu 0.05% +step 1000: train loss 6.1733, val loss 6.1963 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 1000: loss 6.6634, time 20828.84ms, mfu 0.05% +step 1250: train loss 6.0437, val loss 6.0774 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 1250: loss 5.4958, time 20477.72ms, mfu 0.05% +step 1500: train loss 5.9191, val loss 5.9694 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 1500: loss 6.2541, time 20219.76ms, mfu 0.05% +step 1750: train loss 5.8811, val loss 5.8873 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 1750: loss 5.2481, time 20974.42ms, mfu 0.05% +step 2000: train loss 5.7559, val loss 5.7621 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 2000: loss 5.9225, time 25640.27ms, mfu 0.05% +step 2250: train loss 5.6248, val loss 5.6417 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 2250: loss 5.5323, time 23117.92ms, mfu 0.05% +step 2500: train loss 5.5222, val loss 5.5408 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 2500: loss 5.8093, time 19946.24ms, mfu 0.05% +step 2750: train loss 5.4897, val loss 5.4741 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 2750: loss 5.0954, time 19691.67ms, mfu 0.05% +step 3000: train loss 5.3733, val loss 5.4233 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 3000: loss 5.5317, time 19746.33ms, mfu 0.05% +step 3250: train loss 5.3607, val loss 5.3837 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 3250: loss 5.1507, time 19784.37ms, mfu 0.05% +step 3500: train loss 5.2875, val loss 5.2848 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 3500: loss 5.4891, time 19703.89ms, mfu 0.05% +step 3750: train loss 5.2390, val loss 5.2660 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 3750: loss 5.0371, time 19768.08ms, mfu 0.05% +step 4000: train loss 5.1611, val loss 5.1348 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 4000: loss 5.1677, time 19769.25ms, mfu 0.05% +step 4250: train loss 5.1091, val loss 5.0970 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 4250: loss 5.3440, time 19684.38ms, mfu 0.05% +step 4500: train loss 5.0305, val loss 5.0401 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 4500: loss 5.0647, time 21977.49ms, mfu 0.05% +step 4750: train loss 4.9933, val loss 5.0037 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 4750: loss 5.2667, time 19764.42ms, mfu 0.05% +step 5000: train loss 4.9618, val loss 4.9464 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 5000: loss 5.1728, time 19747.30ms, mfu 0.05% +step 5250: train loss 4.8803, val loss 4.9133 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 5250: loss 4.9880, time 21068.74ms, mfu 0.05% +step 5500: train loss 4.8426, val loss 4.8332 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 5500: loss 4.7103, time 20209.47ms, mfu 0.05% +step 5750: train loss 4.7813, val loss 4.8008 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 5750: loss 4.8101, time 20056.36ms, mfu 0.05% +step 6000: train loss 4.7856, val loss 4.7882 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 6000: loss 4.4512, time 20034.63ms, mfu 0.05% +step 6250: train loss 4.7317, val loss 4.7242 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 6250: loss 4.7125, time 19872.30ms, mfu 0.05% +step 6500: train loss 4.6869, val loss 4.7065 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 6500: loss 4.8136, time 20115.77ms, mfu 0.05% +step 6750: train loss 4.6371, val loss 4.6843 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 6750: loss 4.8796, time 20729.51ms, mfu 0.05% +step 7000: train loss 4.6257, val loss 4.6433 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 7000: loss 4.3330, time 20288.42ms, mfu 0.05% +step 7250: train loss 4.5592, val loss 4.6240 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 7250: loss 4.1654, time 22038.24ms, mfu 0.05% +step 7500: train loss 4.5505, val loss 4.5490 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 7500: loss 4.4771, time 20307.86ms, mfu 0.05% +step 7750: train loss 4.5283, val loss 4.5681 +iter 7750: loss 4.8200, time 18328.73ms, mfu 0.05% +step 8000: train loss 4.5211, val loss 4.5322 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 8000: loss 4.2713, time 20305.21ms, mfu 0.05% +step 8250: train loss 4.4799, val loss 4.5056 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 8250: loss 4.6838, time 20284.49ms, mfu 0.05% +step 8500: train loss 4.4856, val loss 4.4951 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 8500: loss 4.5225, time 20854.90ms, mfu 0.05% +step 8750: train loss 4.4691, val loss 4.5021 +iter 8750: loss 4.4914, time 18367.75ms, mfu 0.05% +step 9000: train loss 4.4479, val loss 4.4579 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 9000: loss 4.4427, time 20679.91ms, mfu 0.05% +step 9250: train loss 4.4336, val loss 4.4557 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 9250: loss 4.5391, time 20555.94ms, mfu 0.05% +step 9500: train loss 4.4610, val loss 4.4323 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 9500: loss 4.8782, time 20144.60ms, mfu 0.05% +step 9750: train loss 4.4063, val loss 4.4468 +iter 9750: loss 4.1984, time 18333.25ms, mfu 0.05% +step 10000: train loss 4.4132, val loss 4.4069 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed42_p1 +iter 10000: loss 4.5186, time 20738.69ms, mfu 0.05% + +--> RESULT | cheap_q | seed=42 | BEST=4.4069 | FINAL=4.4069 | ✓ A10=4.4222 delta=-0.0153 + + +==================================================================================================== +RUNNING: cheap_q | warmup=2000 | seed=1337 + per-head Linear(64->64) on Q + A10 reference: 4.4222 ±0.0064 +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.64M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 124,010,496 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9312, val loss 10.9307 +iter 0: loss 10.9359, time 18486.44ms, mfu -100.00% +step 250: train loss 6.9044, val loss 6.9314 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed1337_p1 +iter 250: loss 6.7952, time 19814.64ms, mfu 0.05% +step 500: train loss 6.4895, val loss 6.4932 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed1337_p1 +iter 500: loss 6.6431, time 20025.97ms, mfu 0.05% +step 750: train loss 6.2994, val loss 6.3271 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed1337_p1 +iter 750: loss 6.0902, time 20607.32ms, mfu 0.05% +step 1000: train loss 6.1588, val loss 6.1452 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed1337_p1 +iter 1000: loss 6.0982, time 20891.27ms, mfu 0.05% +step 1250: train loss 6.0687, val loss 6.0372 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed1337_p1 +iter 1250: loss 5.6996, time 20991.75ms, mfu 0.05% +step 1500: train loss 5.9407, val loss 5.9669 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed1337_p1 +iter 1500: loss 6.2750, time 21106.88ms, mfu 0.05% +step 1750: train loss 5.8306, val loss 5.8487 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed1337_p1 +iter 1750: loss 5.9657, time 24405.21ms, mfu 0.05% +step 2000: train loss 5.8073, val loss 5.7845 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed1337_p1 +iter 2000: loss 5.7899, time 20066.43ms, mfu 0.05% +step 2250: train loss 5.6770, val loss 5.6251 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed1337_p1 +iter 2250: loss 6.0664, time 20063.92ms, mfu 0.05% +step 2500: train loss 5.6150, val loss 5.5807 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed1337_p1 +iter 2500: loss 5.1627, time 19959.13ms, mfu 0.05% +step 2750: train loss 5.4941, val loss 5.5102 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed1337_p1 +iter 2750: loss 5.8309, time 20408.18ms, mfu 0.05% +step 3000: train loss 5.4012, val loss 5.4175 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed1337_p1 +iter 3000: loss 5.1134, time 20338.51ms, mfu 0.05% +step 3250: train loss 5.3738, val loss 5.3806 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed1337_p1 +iter 3250: loss 5.0626, time 20663.90ms, mfu 0.05% +step 3500: train loss 5.3103, val loss 5.2927 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed1337_p1 +iter 3500: loss 5.3967, time 20494.47ms, mfu 0.05% +step 3750: train loss 5.2493, val loss 5.2640 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed1337_p1 +iter 3750: loss 5.3459, time 20115.41ms, mfu 0.05% +step 4000: train loss 5.1795, val loss 5.2044 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed1337_p1 +iter 4000: loss 5.0993, time 20691.98ms, mfu 0.05% +step 4250: train loss 5.1734, val loss 5.1756 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed1337_p1 +iter 4250: loss 4.9314, time 20034.37ms, mfu 0.05% +step 4500: train loss 5.0916, val loss 5.1257 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed1337_p1 +iter 4500: loss 5.6351, time 20235.17ms, mfu 0.05% +step 4750: train loss 5.0190, val loss 5.0548 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed1337_p1 +iter 4750: loss 5.4202, time 20728.20ms, mfu 0.05% +step 5000: train loss 4.9995, val loss 5.0132 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed1337_p1 +iter 5000: loss 4.9093, time 20648.96ms, mfu 0.05% +step 5250: train loss 4.9402, val loss 4.9313 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed1337_p1 +iter 5250: loss 4.6396, time 20771.66ms, mfu 0.05% +step 5500: train loss 4.8856, val loss 4.8875 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed1337_p1 +iter 5500: loss 4.7646, time 19940.10ms, mfu 0.05% +step 5750: train loss 4.8596, val loss 4.8690 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed1337_p1 +iter 5750: loss 4.7602, time 19857.15ms, mfu 0.05% +step 6000: train loss 4.7803, val loss 4.7985 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed1337_p1 +iter 6000: loss 4.8936, time 20567.07ms, mfu 0.05% +step 6250: train loss 4.7666, val loss 4.7774 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed1337_p1 +iter 6250: loss 4.6149, time 20257.88ms, mfu 0.05% +step 6500: train loss 4.7105, val loss 4.7448 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed1337_p1 +iter 6500: loss 4.6647, time 20346.60ms, mfu 0.05% +step 6750: train loss 4.6701, val loss 4.6990 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed1337_p1 +iter 6750: loss 4.8172, time 20277.90ms, mfu 0.05% +step 7000: train loss 4.6563, val loss 4.6067 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed1337_p1 +iter 7000: loss 4.1714, time 20742.40ms, mfu 0.05% +step 7250: train loss 4.5830, val loss 4.6499 +iter 7250: loss 4.3769, time 18362.46ms, mfu 0.05% +step 7500: train loss 4.5831, val loss 4.5983 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed1337_p1 +iter 7500: loss 4.9589, time 20727.08ms, mfu 0.05% +step 7750: train loss 4.5726, val loss 4.5490 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed1337_p1 +iter 7750: loss 4.9636, time 20526.27ms, mfu 0.05% +step 8000: train loss 4.5692, val loss 4.5559 +iter 8000: loss 4.4566, time 18318.41ms, mfu 0.05% +step 8250: train loss 4.5283, val loss 4.5099 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed1337_p1 +iter 8250: loss 4.6709, time 20683.40ms, mfu 0.05% +step 8500: train loss 4.4908, val loss 4.4841 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed1337_p1 +iter 8500: loss 4.2292, time 20015.45ms, mfu 0.05% +step 8750: train loss 4.5149, val loss 4.5304 +iter 8750: loss 4.4192, time 18358.46ms, mfu 0.05% +step 9000: train loss 4.4742, val loss 4.5200 +iter 9000: loss 4.7921, time 18430.67ms, mfu 0.05% +step 9250: train loss 4.4685, val loss 4.4459 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed1337_p1 +iter 9250: loss 4.4452, time 20441.86ms, mfu 0.05% +step 9500: train loss 4.4654, val loss 4.4612 +iter 9500: loss 4.5318, time 18363.01ms, mfu 0.05% +step 9750: train loss 4.4347, val loss 4.4592 +iter 9750: loss 4.5091, time 18459.31ms, mfu 0.05% +step 10000: train loss 4.4249, val loss 4.4464 +iter 10000: loss 4.6689, time 18410.97ms, mfu 0.05% + +--> RESULT | cheap_q | seed=1337 | BEST=4.4459 | FINAL=4.4464 | ✗(best@9250) A10=4.4222 delta=+0.0237 + + +cheap_q running mean (2/3 seeds): 4.4264 ±0.0195 A10=4.4222 + + +==================================================================================================== +RUNNING: cheap_q | warmup=2000 | seed=123 + per-head Linear(64->64) on Q + A10 reference: 4.4222 ±0.0064 +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.64M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 124,010,496 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9084, val loss 10.9091 +iter 0: loss 10.9223, time 18550.86ms, mfu -100.00% +step 250: train loss 6.9508, val loss 6.9769 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 250: loss 7.0427, time 19682.68ms, mfu 0.05% +step 500: train loss 6.5226, val loss 6.5137 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 500: loss 6.2362, time 20309.34ms, mfu 0.05% +step 750: train loss 6.3292, val loss 6.3569 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 750: loss 5.8372, time 20671.31ms, mfu 0.05% +step 1000: train loss 6.1660, val loss 6.1745 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 1000: loss 6.1474, time 20770.51ms, mfu 0.05% +step 1250: train loss 6.0484, val loss 6.0469 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 1250: loss 6.1835, time 27578.86ms, mfu 0.05% +step 1500: train loss 5.9553, val loss 5.9552 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 1500: loss 6.2766, time 23123.73ms, mfu 0.05% +step 1750: train loss 5.8685, val loss 5.8741 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 1750: loss 5.6356, time 20082.08ms, mfu 0.05% +step 2000: train loss 5.7548, val loss 5.7537 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 2000: loss 5.8313, time 20321.84ms, mfu 0.05% +step 2250: train loss 5.6585, val loss 5.6716 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 2250: loss 5.8215, time 20105.83ms, mfu 0.05% +step 2500: train loss 5.6119, val loss 5.5960 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 2500: loss 5.4946, time 20933.13ms, mfu 0.05% +step 2750: train loss 5.4611, val loss 5.4955 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 2750: loss 5.1733, time 20375.62ms, mfu 0.05% +step 3000: train loss 5.3963, val loss 5.3877 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 3000: loss 5.5424, time 20442.47ms, mfu 0.05% +step 3250: train loss 5.3212, val loss 5.3485 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 3250: loss 5.3415, time 20441.69ms, mfu 0.05% +step 3500: train loss 5.3001, val loss 5.2935 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 3500: loss 4.9142, time 20616.35ms, mfu 0.05% +step 3750: train loss 5.2513, val loss 5.2539 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 3750: loss 5.5317, time 20401.84ms, mfu 0.05% +step 4000: train loss 5.1546, val loss 5.1794 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 4000: loss 5.0115, time 19994.05ms, mfu 0.05% +step 4250: train loss 5.1099, val loss 5.1046 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 4250: loss 5.1592, time 19962.31ms, mfu 0.05% +step 4500: train loss 5.0331, val loss 5.0398 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 4500: loss 4.4815, time 20225.02ms, mfu 0.05% +step 4750: train loss 4.9898, val loss 5.0106 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 4750: loss 4.7763, time 20514.28ms, mfu 0.05% +step 5000: train loss 4.9532, val loss 4.9413 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 5000: loss 5.2694, time 20370.04ms, mfu 0.05% +step 5250: train loss 4.8942, val loss 4.8961 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 5250: loss 4.8265, time 20545.20ms, mfu 0.05% +step 5500: train loss 4.8512, val loss 4.8617 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 5500: loss 4.7192, time 20504.59ms, mfu 0.05% +step 5750: train loss 4.7918, val loss 4.8005 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 5750: loss 5.4680, time 19881.87ms, mfu 0.05% +step 6000: train loss 4.7455, val loss 4.7615 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 6000: loss 4.5684, time 20024.51ms, mfu 0.05% +step 6250: train loss 4.7188, val loss 4.6968 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 6250: loss 4.9465, time 20381.34ms, mfu 0.05% +step 6500: train loss 4.7002, val loss 4.7047 +iter 6500: loss 4.6281, time 18307.92ms, mfu 0.05% +step 6750: train loss 4.5988, val loss 4.6764 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 6750: loss 4.4862, time 20823.14ms, mfu 0.05% +step 7000: train loss 4.5981, val loss 4.6417 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 7000: loss 5.0651, time 20021.90ms, mfu 0.05% +step 7250: train loss 4.6170, val loss 4.6228 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 7250: loss 4.6215, time 20053.23ms, mfu 0.05% +step 7500: train loss 4.5494, val loss 4.5637 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 7500: loss 4.9705, time 20745.50ms, mfu 0.05% +step 7750: train loss 4.5365, val loss 4.5498 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 7750: loss 4.7331, time 23312.79ms, mfu 0.05% +step 8000: train loss 4.5055, val loss 4.5196 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 8000: loss 4.3727, time 20783.08ms, mfu 0.05% +step 8250: train loss 4.5100, val loss 4.5039 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 8250: loss 4.4732, time 20131.60ms, mfu 0.05% +step 8500: train loss 4.4602, val loss 4.4596 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 8500: loss 4.4731, time 20180.54ms, mfu 0.05% +step 8750: train loss 4.4592, val loss 4.4723 +iter 8750: loss 4.2630, time 18360.41ms, mfu 0.05% +step 9000: train loss 4.4555, val loss 4.4620 +iter 9000: loss 4.5568, time 18334.44ms, mfu 0.05% +step 9250: train loss 4.4630, val loss 4.4230 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 9250: loss 4.6519, time 20440.10ms, mfu 0.05% +step 9500: train loss 4.4371, val loss 4.4182 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 9500: loss 4.5275, time 20221.40ms, mfu 0.05% +step 9750: train loss 4.4146, val loss 4.4369 +iter 9750: loss 4.1715, time 18328.09ms, mfu 0.05% +step 10000: train loss 4.4082, val loss 4.4066 +saving checkpoint to out-openwebtext_dynamic_warmup2000_seed123_p1 +iter 10000: loss 4.0757, time 20497.80ms, mfu 0.05% + +--> RESULT | cheap_q | seed=123 | BEST=4.4066 | FINAL=4.4066 | ✓ A10=4.4222 delta=-0.0156 + + +cheap_q running mean (3/3 seeds): 4.4198 ±0.0185 A10=4.4222 + + +🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆 +CURRENT LEADERBOARD (3-seed complete): + 1. cheap_qa 4.3897 ±0.0051 A10=4.4151 delta=-0.0254 + 2. midtier_q 4.4135 ±0.0086 A10=4.4039 delta=+0.0096 + 3. cheap_q 4.4198 ±0.0185 A10=4.4222 delta=-0.0024 + 4. baseline 4.4708 ±0.0074 A10=4.4675 delta=+0.0033 +🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆 + + +==================================================================================================== +PRIORITY 1 COMPLETE +==================================================================================================== +Results CSV: priority1_runs_20260322_1355.csv +Summary: priority1_summary_20260322_1355.txt + +Paste summary here. Then start Priority 2 overnight. +PS C:\Users\brian\documents\nanogpt> python priority2_runner.py +==================================================================================================== +PRIORITY 2: OVERNIGHT RUNNER +==================================================================================================== +Variants: 5 | Seeds: [42, 1337, 123] | Total: 15 runs +Estimated: ~16h 15min overnight + +VARIANTS: + baseline_p2 — fresh same-session baseline anchor + mlp128_qa — MLP(128->128->64) on cat(Q,y) — capacity + dual signal ref=4.4282 (DIRTY 3060 — this run replaces) + static_prehead — learned per-head scalar gate — 9 params, no signal ref=4.4438 (A10 fairness sweep) + full_x — full X gate Linear(768->768) — paper implementation ref=4.5374 (DIRTY 3060 — this run replaces) + x_headshared — head-shared X gate Linear(64->64) ref=4.5863 (DIRTY 3060 — this run replaces) + +THE BIG QUESTION: + mlp128_qa (capacity + dual signal) vs cheap_qa (4.3897) + Does adding A to midtier_q give the same ~0.024 boost as adding A to cheap_q? + +CLEAN P1 CHAMPIONS FOR REFERENCE: + cheap_qa: 4.3897 ±0.0051 + midtier_q: 4.4135 ±0.0086 + cheap_q: 4.4198 ±0.0185 + baseline: 4.4708 ±0.0074 +==================================================================================================== + +==================================================================================================== +RUNNING: baseline_p2 | warmup=2000 | seed=42 + fresh same-session baseline anchor +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9665, val loss 10.9665 +iter 0: loss 10.9808, time 18984.12ms, mfu -100.00% +step 250: train loss 7.0174, val loss 7.0114 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 250: loss 7.1148, time 21815.13ms, mfu 0.05% +step 500: train loss 6.5238, val loss 6.5302 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 500: loss 6.1699, time 21624.54ms, mfu 0.05% +step 750: train loss 6.2966, val loss 6.3284 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 750: loss 6.6330, time 23052.15ms, mfu 0.05% +step 1000: train loss 6.1638, val loss 6.1596 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 1000: loss 5.8107, time 21379.27ms, mfu 0.05% +step 1250: train loss 6.0387, val loss 6.0545 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 1250: loss 6.5446, time 21410.22ms, mfu 0.05% +step 1500: train loss 5.9823, val loss 5.9781 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 1500: loss 5.4953, time 21392.81ms, mfu 0.05% +step 1750: train loss 5.8900, val loss 5.8995 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 1750: loss 6.1942, time 21388.06ms, mfu 0.05% +step 2000: train loss 5.8283, val loss 5.8410 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 2000: loss 5.6612, time 21465.05ms, mfu 0.05% +step 2250: train loss 5.6983, val loss 5.7084 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 2250: loss 5.7812, time 21226.71ms, mfu 0.05% +step 2500: train loss 5.6217, val loss 5.6164 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 2500: loss 4.9156, time 21176.40ms, mfu 0.05% +step 2750: train loss 5.5337, val loss 5.5316 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 2750: loss 5.5710, time 21916.26ms, mfu 0.05% +step 3000: train loss 5.4636, val loss 5.4486 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 3000: loss 5.5046, time 21142.24ms, mfu 0.05% +step 3250: train loss 5.4150, val loss 5.3715 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 3250: loss 5.5838, time 21357.92ms, mfu 0.05% +step 3500: train loss 5.3210, val loss 5.3706 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 3500: loss 5.4543, time 21265.30ms, mfu 0.05% +step 3750: train loss 5.2786, val loss 5.3038 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 3750: loss 5.3309, time 21443.54ms, mfu 0.05% +step 4000: train loss 5.2421, val loss 5.2488 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 4000: loss 5.8776, time 21222.68ms, mfu 0.05% +step 4250: train loss 5.1958, val loss 5.1778 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 4250: loss 4.9246, time 21363.88ms, mfu 0.05% +step 4500: train loss 5.1059, val loss 5.1320 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 4500: loss 5.2177, time 22115.93ms, mfu 0.05% +step 4750: train loss 5.0801, val loss 5.0863 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 4750: loss 5.1819, time 24421.60ms, mfu 0.05% +step 5000: train loss 5.0381, val loss 5.0118 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 5000: loss 5.4403, time 25875.17ms, mfu 0.05% +step 5250: train loss 4.9697, val loss 4.9714 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 5250: loss 5.0938, time 21075.13ms, mfu 0.05% +step 5500: train loss 4.9516, val loss 4.9397 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 5500: loss 4.6682, time 21244.39ms, mfu 0.05% +step 5750: train loss 4.8750, val loss 4.8858 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 5750: loss 5.1301, time 21261.57ms, mfu 0.05% +step 6000: train loss 4.8156, val loss 4.8249 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 6000: loss 4.7601, time 21195.00ms, mfu 0.05% +step 6250: train loss 4.7787, val loss 4.7942 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 6250: loss 4.4634, time 21361.62ms, mfu 0.05% +step 6500: train loss 4.7773, val loss 4.7543 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 6500: loss 4.5996, time 21383.99ms, mfu 0.05% +step 6750: train loss 4.7072, val loss 4.7001 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 6750: loss 4.7107, time 21204.36ms, mfu 0.05% +step 7000: train loss 4.6624, val loss 4.6915 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 7000: loss 4.6424, time 21093.87ms, mfu 0.05% +step 7250: train loss 4.6503, val loss 4.6473 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 7250: loss 4.8589, time 21248.74ms, mfu 0.05% +step 7500: train loss 4.6456, val loss 4.6240 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 7500: loss 4.7475, time 19859.28ms, mfu 0.05% +step 7750: train loss 4.6182, val loss 4.5971 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 7750: loss 4.7206, time 20257.36ms, mfu 0.05% +step 8000: train loss 4.5767, val loss 4.5791 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 8000: loss 4.5333, time 20347.23ms, mfu 0.05% +step 8250: train loss 4.5320, val loss 4.5563 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 8250: loss 4.3741, time 20199.79ms, mfu 0.05% +step 8500: train loss 4.5445, val loss 4.5755 +iter 8500: loss 4.4012, time 18004.14ms, mfu 0.05% +step 8750: train loss 4.4967, val loss 4.5206 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 8750: loss 4.6538, time 20449.74ms, mfu 0.05% +step 9000: train loss 4.5107, val loss 4.5320 +iter 9000: loss 4.5031, time 18198.13ms, mfu 0.05% +step 9250: train loss 4.4876, val loss 4.4806 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 9250: loss 4.6112, time 21109.39ms, mfu 0.05% +step 9500: train loss 4.4608, val loss 4.4778 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 9500: loss 4.2004, time 23601.42ms, mfu 0.05% +step 9750: train loss 4.4579, val loss 4.4705 +saving checkpoint to out-openwebtext_none_warmup2000_seed42_p2 +iter 9750: loss 4.7822, time 22786.80ms, mfu 0.05% +step 10000: train loss 4.4556, val loss 4.4839 +iter 10000: loss 4.4689, time 17669.89ms, mfu 0.05% + +--> RESULT | baseline_p2 | seed=42 | BEST=4.4705 | FINAL=4.4839 | ✗(best@9750) + + +==================================================================================================== +RUNNING: baseline_p2 | warmup=2000 | seed=1337 + fresh same-session baseline anchor +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9989, val loss 10.9985 +iter 0: loss 11.0107, time 17874.14ms, mfu -100.00% +step 250: train loss 6.9435, val loss 6.9764 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 250: loss 6.7374, time 21312.31ms, mfu 0.05% +step 500: train loss 6.4863, val loss 6.5542 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 500: loss 6.2683, time 22078.54ms, mfu 0.05% +step 750: train loss 6.3185, val loss 6.3357 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 750: loss 6.1040, time 21242.98ms, mfu 0.05% +step 1000: train loss 6.1793, val loss 6.1528 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 1000: loss 6.1545, time 21586.28ms, mfu 0.05% +step 1250: train loss 6.0787, val loss 6.0696 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 1250: loss 6.1073, time 22015.87ms, mfu 0.05% +step 1500: train loss 5.9995, val loss 5.9264 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 1500: loss 5.7085, time 21213.75ms, mfu 0.05% +step 1750: train loss 5.9295, val loss 5.8931 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 1750: loss 6.1277, time 21385.26ms, mfu 0.05% +step 2000: train loss 5.8275, val loss 5.7962 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 2000: loss 5.8655, time 21326.62ms, mfu 0.05% +step 2250: train loss 5.7163, val loss 5.7206 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 2250: loss 5.8957, time 21112.05ms, mfu 0.05% +step 2500: train loss 5.6064, val loss 5.5865 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 2500: loss 6.0507, time 21059.41ms, mfu 0.05% +step 2750: train loss 5.5277, val loss 5.5219 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 2750: loss 5.5253, time 22660.43ms, mfu 0.05% +step 3000: train loss 5.4528, val loss 5.4712 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 3000: loss 5.5389, time 25679.87ms, mfu 0.05% +step 3250: train loss 5.3770, val loss 5.3823 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 3250: loss 5.3345, time 22826.03ms, mfu 0.05% +step 3500: train loss 5.3796, val loss 5.3418 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 3500: loss 5.2833, time 22427.43ms, mfu 0.05% +step 3750: train loss 5.2866, val loss 5.2756 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 3750: loss 5.5733, time 23655.47ms, mfu 0.05% +step 4000: train loss 5.2375, val loss 5.2463 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 4000: loss 5.3305, time 23680.48ms, mfu 0.05% +step 4250: train loss 5.1788, val loss 5.1924 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 4250: loss 4.7763, time 21219.38ms, mfu 0.05% +step 4500: train loss 5.1451, val loss 5.1363 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 4500: loss 4.8612, time 20998.45ms, mfu 0.05% +step 4750: train loss 5.0690, val loss 5.0839 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 4750: loss 5.1896, time 21049.20ms, mfu 0.05% +step 5000: train loss 4.9996, val loss 5.0376 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 5000: loss 5.1788, time 20935.95ms, mfu 0.05% +step 5250: train loss 4.9921, val loss 4.9797 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 5250: loss 4.6505, time 21000.40ms, mfu 0.05% +step 5500: train loss 4.9558, val loss 4.9267 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 5500: loss 5.1249, time 21053.20ms, mfu 0.05% +step 5750: train loss 4.8931, val loss 4.8591 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 5750: loss 5.1725, time 20861.77ms, mfu 0.05% +step 6000: train loss 4.8072, val loss 4.8122 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 6000: loss 4.7276, time 21049.44ms, mfu 0.05% +step 6250: train loss 4.7909, val loss 4.8228 +iter 6250: loss 4.5790, time 18042.25ms, mfu 0.05% +step 6500: train loss 4.7509, val loss 4.7859 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 6500: loss 4.9320, time 20922.12ms, mfu 0.05% +step 6750: train loss 4.7165, val loss 4.7215 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 6750: loss 4.8704, time 20935.24ms, mfu 0.05% +step 7000: train loss 4.6842, val loss 4.6799 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 7000: loss 4.6298, time 20969.36ms, mfu 0.05% +step 7250: train loss 4.6365, val loss 4.6658 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 7250: loss 4.7336, time 21105.55ms, mfu 0.05% +step 7500: train loss 4.6470, val loss 4.6033 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 7500: loss 4.5333, time 21168.02ms, mfu 0.05% +step 7750: train loss 4.5936, val loss 4.5882 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 7750: loss 4.7583, time 20859.67ms, mfu 0.05% +step 8000: train loss 4.5562, val loss 4.5822 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 8000: loss 4.6411, time 20752.62ms, mfu 0.05% +step 8250: train loss 4.5374, val loss 4.5676 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 8250: loss 4.3204, time 20952.38ms, mfu 0.05% +step 8500: train loss 4.5342, val loss 4.5434 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 8500: loss 4.2681, time 20999.32ms, mfu 0.05% +step 8750: train loss 4.4984, val loss 4.5000 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 8750: loss 4.5187, time 20881.06ms, mfu 0.05% +step 9000: train loss 4.5135, val loss 4.5160 +iter 9000: loss 4.6067, time 17936.49ms, mfu 0.05% +step 9250: train loss 4.4748, val loss 4.5034 +iter 9250: loss 4.5745, time 17952.85ms, mfu 0.05% +step 9500: train loss 4.4809, val loss 4.4905 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 9500: loss 4.5043, time 21116.55ms, mfu 0.05% +step 9750: train loss 4.4856, val loss 4.4591 +saving checkpoint to out-openwebtext_none_warmup2000_seed1337_p2 +iter 9750: loss 4.3721, time 20984.58ms, mfu 0.05% +step 10000: train loss 4.4769, val loss 4.5028 +iter 10000: loss 4.4878, time 17991.08ms, mfu 0.05% + +--> RESULT | baseline_p2 | seed=1337 | BEST=4.4591 | FINAL=4.5028 | ✗(best@9750) + + +baseline_p2 running mean (2/3 seeds): 4.4648 ±0.0057 + + +==================================================================================================== +RUNNING: baseline_p2 | warmup=2000 | seed=123 + fresh same-session baseline anchor +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9288, val loss 10.9279 +iter 0: loss 10.9503, time 18053.17ms, mfu -100.00% +step 250: train loss 6.9746, val loss 6.9200 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 250: loss 6.6646, time 21051.02ms, mfu 0.05% +step 500: train loss 6.4968, val loss 6.5721 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 500: loss 6.6019, time 22737.53ms, mfu 0.05% +step 750: train loss 6.3136, val loss 6.3184 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 750: loss 5.6487, time 20918.69ms, mfu 0.05% +step 1000: train loss 6.1980, val loss 6.1975 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 1000: loss 6.5136, time 20987.36ms, mfu 0.05% +step 1250: train loss 6.0399, val loss 6.0666 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 1250: loss 5.7804, time 20930.30ms, mfu 0.05% +step 1500: train loss 6.0166, val loss 5.9866 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 1500: loss 5.8220, time 20884.10ms, mfu 0.05% +step 1750: train loss 5.8828, val loss 5.9254 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 1750: loss 6.4961, time 22027.62ms, mfu 0.05% +step 2000: train loss 5.8249, val loss 5.8627 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 2000: loss 5.7543, time 21088.28ms, mfu 0.05% +step 2250: train loss 5.6760, val loss 5.7323 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 2250: loss 6.1522, time 20889.60ms, mfu 0.05% +step 2500: train loss 5.6340, val loss 5.6393 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 2500: loss 5.8514, time 21034.89ms, mfu 0.05% +step 2750: train loss 5.5706, val loss 5.5465 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 2750: loss 6.0767, time 20938.93ms, mfu 0.05% +step 3000: train loss 5.5027, val loss 5.4993 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 3000: loss 6.2350, time 20838.14ms, mfu 0.05% +step 3250: train loss 5.4019, val loss 5.3994 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 3250: loss 5.3501, time 20868.71ms, mfu 0.05% +step 3500: train loss 5.3323, val loss 5.3811 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 3500: loss 5.3586, time 20738.15ms, mfu 0.05% +step 3750: train loss 5.3024, val loss 5.2882 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 3750: loss 5.0722, time 20910.36ms, mfu 0.05% +step 4000: train loss 5.2476, val loss 5.2604 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 4000: loss 5.1567, time 20876.86ms, mfu 0.05% +step 4250: train loss 5.2110, val loss 5.2201 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 4250: loss 5.2649, time 20879.71ms, mfu 0.05% +step 4500: train loss 5.1231, val loss 5.1267 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 4500: loss 5.1641, time 20736.17ms, mfu 0.05% +step 4750: train loss 5.1060, val loss 5.1037 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 4750: loss 4.9922, time 20883.39ms, mfu 0.05% +step 5000: train loss 5.0233, val loss 5.0234 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 5000: loss 5.1357, time 20949.99ms, mfu 0.05% +step 5250: train loss 5.0035, val loss 4.9932 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 5250: loss 5.1778, time 21015.02ms, mfu 0.05% +step 5500: train loss 4.9171, val loss 4.9615 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 5500: loss 4.8457, time 21078.72ms, mfu 0.05% +step 5750: train loss 4.9316, val loss 4.9143 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 5750: loss 5.2354, time 20980.33ms, mfu 0.05% +step 6000: train loss 4.8821, val loss 4.8486 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 6000: loss 4.9029, time 20940.99ms, mfu 0.05% +step 6250: train loss 4.8042, val loss 4.8557 +iter 6250: loss 5.0535, time 17847.92ms, mfu 0.05% +step 6500: train loss 4.7716, val loss 4.7801 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 6500: loss 4.6146, time 20918.06ms, mfu 0.05% +step 6750: train loss 4.7259, val loss 4.7485 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 6750: loss 4.3562, time 20904.37ms, mfu 0.05% +step 7000: train loss 4.6924, val loss 4.7020 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 7000: loss 4.6371, time 20768.60ms, mfu 0.05% +step 7250: train loss 4.6657, val loss 4.6946 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 7250: loss 4.2777, time 20889.40ms, mfu 0.05% +step 7500: train loss 4.6711, val loss 4.6551 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 7500: loss 4.6348, time 20905.82ms, mfu 0.05% +step 7750: train loss 4.6235, val loss 4.6213 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 7750: loss 4.8165, time 20922.59ms, mfu 0.05% +step 8000: train loss 4.6000, val loss 4.5959 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 8000: loss 4.1563, time 20925.53ms, mfu 0.05% +step 8250: train loss 4.5587, val loss 4.5538 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 8250: loss 4.3586, time 20830.15ms, mfu 0.05% +step 8500: train loss 4.5406, val loss 4.5597 +iter 8500: loss 4.2955, time 17767.72ms, mfu 0.05% +step 8750: train loss 4.5191, val loss 4.5333 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 8750: loss 4.3907, time 20822.57ms, mfu 0.05% +step 9000: train loss 4.5368, val loss 4.5171 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 9000: loss 4.3068, time 20953.44ms, mfu 0.05% +step 9250: train loss 4.4804, val loss 4.5105 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 9250: loss 5.0383, time 20932.04ms, mfu 0.05% +step 9500: train loss 4.4806, val loss 4.4768 +saving checkpoint to out-openwebtext_none_warmup2000_seed123_p2 +iter 9500: loss 4.9487, time 20742.90ms, mfu 0.05% +step 9750: train loss 4.5047, val loss 4.5270 +iter 9750: loss 4.3966, time 17773.16ms, mfu 0.05% +step 10000: train loss 4.4875, val loss 4.4972 +iter 10000: loss 4.6343, time 17752.55ms, mfu 0.05% + +--> RESULT | baseline_p2 | seed=123 | BEST=4.4768 | FINAL=4.4972 | ✗(best@9500) + + +baseline_p2 running mean (3/3 seeds): 4.4688 ±0.0073 + + +==================================================================================================== +RUNNING: mlp128_qa | warmup=2000 | seed=42 + MLP(128->128->64) on cat(Q,y) — capacity + dual signal + Reference: 4.4282 (DIRTY 3060 — this run replaces) +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.88M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 74, with 124,256,256 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9577, val loss 10.9571 +iter 0: loss 10.9258, time 19834.62ms, mfu -100.00% +step 250: train loss 6.9272, val loss 6.9344 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 250: loss 7.0100, time 22194.92ms, mfu 0.05% +step 500: train loss 6.5181, val loss 6.5248 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 500: loss 6.7435, time 22644.66ms, mfu 0.05% +step 750: train loss 6.2818, val loss 6.3411 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 750: loss 6.3631, time 22215.47ms, mfu 0.05% +step 1000: train loss 6.1773, val loss 6.1947 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 1000: loss 6.0599, time 22199.92ms, mfu 0.05% +step 1250: train loss 6.0597, val loss 6.0456 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 1250: loss 6.6189, time 23090.53ms, mfu 0.05% +step 1500: train loss 5.9288, val loss 5.9601 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 1500: loss 6.3717, time 25215.65ms, mfu 0.05% +step 1750: train loss 5.8136, val loss 5.8319 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 1750: loss 5.7486, time 24312.17ms, mfu 0.05% +step 2000: train loss 5.7568, val loss 5.7579 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 2000: loss 5.0763, time 24115.09ms, mfu 0.05% +step 2250: train loss 5.6317, val loss 5.6157 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 2250: loss 5.5536, time 23445.79ms, mfu 0.05% +step 2500: train loss 5.5167, val loss 5.5443 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 2500: loss 5.8477, time 22624.28ms, mfu 0.05% +step 2750: train loss 5.4731, val loss 5.4658 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 2750: loss 5.2244, time 22361.84ms, mfu 0.05% +step 3000: train loss 5.3652, val loss 5.3672 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 3000: loss 6.4064, time 22384.06ms, mfu 0.05% +step 3250: train loss 5.3179, val loss 5.2939 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 3250: loss 5.1203, time 22301.06ms, mfu 0.05% +step 3500: train loss 5.2738, val loss 5.2480 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 3500: loss 5.2722, time 22414.66ms, mfu 0.05% +step 3750: train loss 5.2106, val loss 5.1840 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 3750: loss 4.9261, time 22821.98ms, mfu 0.05% +step 4000: train loss 5.1247, val loss 5.1533 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 4000: loss 4.6931, time 22574.67ms, mfu 0.05% +step 4250: train loss 5.0632, val loss 5.0733 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 4250: loss 4.8850, time 22775.94ms, mfu 0.05% +step 4500: train loss 4.9684, val loss 5.0010 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 4500: loss 5.3253, time 22834.57ms, mfu 0.05% +step 4750: train loss 4.9926, val loss 4.9586 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 4750: loss 5.3941, time 22758.28ms, mfu 0.05% +step 5000: train loss 4.8842, val loss 4.9298 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 5000: loss 5.0901, time 22451.50ms, mfu 0.05% +step 5250: train loss 4.8537, val loss 4.8581 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 5250: loss 4.4709, time 22462.44ms, mfu 0.05% +step 5500: train loss 4.8357, val loss 4.8211 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 5500: loss 5.0069, time 22446.86ms, mfu 0.05% +step 5750: train loss 4.7731, val loss 4.7985 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 5750: loss 4.6325, time 22577.40ms, mfu 0.05% +step 6000: train loss 4.7287, val loss 4.7235 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 6000: loss 4.7060, time 22709.84ms, mfu 0.05% +step 6250: train loss 4.6586, val loss 4.6838 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 6250: loss 4.9185, time 22580.63ms, mfu 0.05% +step 6500: train loss 4.6341, val loss 4.6636 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 6500: loss 4.7587, time 22498.07ms, mfu 0.05% +step 6750: train loss 4.6499, val loss 4.6332 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 6750: loss 4.5785, time 22567.17ms, mfu 0.05% +step 7000: train loss 4.6045, val loss 4.5923 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 7000: loss 4.6537, time 22580.17ms, mfu 0.05% +step 7250: train loss 4.6071, val loss 4.5257 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 7250: loss 4.6992, time 22854.64ms, mfu 0.05% +step 7500: train loss 4.5537, val loss 4.5405 +iter 7500: loss 4.5095, time 19510.65ms, mfu 0.05% +step 7750: train loss 4.5311, val loss 4.5560 +iter 7750: loss 4.7552, time 19540.34ms, mfu 0.05% +step 8000: train loss 4.5305, val loss 4.4874 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 8000: loss 4.5933, time 22838.31ms, mfu 0.05% +step 8250: train loss 4.4952, val loss 4.5050 +iter 8250: loss 4.1980, time 19568.10ms, mfu 0.05% +step 8500: train loss 4.4461, val loss 4.4661 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 8500: loss 4.3319, time 22626.06ms, mfu 0.05% +step 8750: train loss 4.4485, val loss 4.4392 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 8750: loss 4.2779, time 22662.38ms, mfu 0.05% +step 9000: train loss 4.4607, val loss 4.4520 +iter 9000: loss 4.3643, time 19556.37ms, mfu 0.05% +step 9250: train loss 4.4217, val loss 4.4283 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 9250: loss 4.8711, time 22738.84ms, mfu 0.05% +step 9500: train loss 4.3920, val loss 4.4459 +iter 9500: loss 4.5217, time 19621.59ms, mfu 0.05% +step 9750: train loss 4.4360, val loss 4.4280 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 9750: loss 4.5845, time 22668.09ms, mfu 0.05% +step 10000: train loss 4.4080, val loss 4.4240 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed42_p2 +iter 10000: loss 4.5429, time 22566.00ms, mfu 0.05% + +--> RESULT | mlp128_qa | seed=42 | BEST=4.4240 | FINAL=4.4240 | ✓ ref=4.4282 delta=-0.0042 + + +==================================================================================================== +RUNNING: mlp128_qa | warmup=2000 | seed=1337 + MLP(128->128->64) on cat(Q,y) — capacity + dual signal + Reference: 4.4282 (DIRTY 3060 — this run replaces) +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.88M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 74, with 124,256,256 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9369, val loss 10.9371 +iter 0: loss 10.9432, time 20703.13ms, mfu -100.00% +step 250: train loss 6.9303, val loss 6.9657 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 250: loss 6.7694, time 22279.44ms, mfu 0.05% +step 500: train loss 6.4938, val loss 6.5120 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 500: loss 6.3856, time 24085.90ms, mfu 0.05% +step 750: train loss 6.3122, val loss 6.3509 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 750: loss 7.1770, time 22620.02ms, mfu 0.05% +step 1000: train loss 6.1447, val loss 6.1996 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 1000: loss 6.2048, time 23222.37ms, mfu 0.05% +step 1250: train loss 6.0848, val loss 6.0479 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 1250: loss 6.3025, time 22851.28ms, mfu 0.05% +step 1500: train loss 5.9454, val loss 5.9959 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 1500: loss 6.2268, time 24060.81ms, mfu 0.05% +step 1750: train loss 5.8461, val loss 5.8518 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 1750: loss 5.7575, time 23608.72ms, mfu 0.05% +step 2000: train loss 5.7672, val loss 5.7894 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 2000: loss 5.6400, time 25264.66ms, mfu 0.05% +step 2250: train loss 5.6875, val loss 5.6846 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 2250: loss 5.7992, time 23791.82ms, mfu 0.05% +step 2500: train loss 5.5940, val loss 5.5605 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 2500: loss 5.5787, time 24362.00ms, mfu 0.05% +step 2750: train loss 5.4913, val loss 5.4918 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 2750: loss 5.2005, time 23731.56ms, mfu 0.05% +step 3000: train loss 5.4172, val loss 5.3938 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 3000: loss 5.0432, time 23582.46ms, mfu 0.05% +step 3250: train loss 5.3204, val loss 5.3159 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 3250: loss 5.3179, time 23797.16ms, mfu 0.04% +step 3500: train loss 5.2433, val loss 5.2745 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 3500: loss 5.6164, time 22408.54ms, mfu 0.05% +step 3750: train loss 5.1914, val loss 5.1875 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 3750: loss 5.7475, time 22494.62ms, mfu 0.05% +step 4000: train loss 5.1618, val loss 5.1341 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 4000: loss 5.1499, time 22550.06ms, mfu 0.05% +step 4250: train loss 5.0936, val loss 5.1143 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 4250: loss 5.0751, time 22595.20ms, mfu 0.05% +step 4500: train loss 5.0183, val loss 5.0528 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 4500: loss 4.5620, time 22642.90ms, mfu 0.05% +step 4750: train loss 5.0098, val loss 4.9889 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 4750: loss 4.1053, time 24050.99ms, mfu 0.05% +step 5000: train loss 4.9184, val loss 4.9312 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 5000: loss 5.0860, time 26237.31ms, mfu 0.04% +step 5250: train loss 4.8793, val loss 4.8949 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 5250: loss 5.0475, time 22657.12ms, mfu 0.05% +step 5500: train loss 4.8286, val loss 4.8600 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 5500: loss 4.7964, time 22611.27ms, mfu 0.05% +step 5750: train loss 4.7862, val loss 4.7536 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 5750: loss 4.5299, time 22474.08ms, mfu 0.05% +step 6000: train loss 4.7432, val loss 4.7567 +iter 6000: loss 4.7627, time 19454.95ms, mfu 0.05% +step 6250: train loss 4.6808, val loss 4.7219 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 6250: loss 4.9241, time 22559.49ms, mfu 0.05% +step 6500: train loss 4.6418, val loss 4.6684 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 6500: loss 4.7323, time 22533.96ms, mfu 0.05% +step 6750: train loss 4.6206, val loss 4.6105 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 6750: loss 4.8763, time 22651.29ms, mfu 0.05% +step 7000: train loss 4.5870, val loss 4.6029 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 7000: loss 4.2824, time 22398.61ms, mfu 0.05% +step 7250: train loss 4.5916, val loss 4.5619 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 7250: loss 4.8337, time 22633.91ms, mfu 0.05% +step 7500: train loss 4.5660, val loss 4.5558 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 7500: loss 4.2215, time 22852.94ms, mfu 0.05% +step 7750: train loss 4.5368, val loss 4.5346 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 7750: loss 4.3914, time 22506.51ms, mfu 0.05% +step 8000: train loss 4.4848, val loss 4.4949 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 8000: loss 4.6615, time 22426.65ms, mfu 0.05% +step 8250: train loss 4.4822, val loss 4.4691 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 8250: loss 4.3015, time 22483.12ms, mfu 0.05% +step 8500: train loss 4.4631, val loss 4.4723 +iter 8500: loss 4.4381, time 19509.70ms, mfu 0.05% +step 8750: train loss 4.4280, val loss 4.4222 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 8750: loss 4.1850, time 22643.21ms, mfu 0.05% +step 9000: train loss 4.4133, val loss 4.4468 +iter 9000: loss 4.6447, time 19373.91ms, mfu 0.05% +step 9250: train loss 4.4321, val loss 4.4194 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 9250: loss 4.3053, time 22448.89ms, mfu 0.05% +step 9500: train loss 4.4175, val loss 4.4070 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 9500: loss 4.3098, time 22500.11ms, mfu 0.05% +step 9750: train loss 4.4042, val loss 4.3976 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed1337_p2 +iter 9750: loss 4.1642, time 22520.97ms, mfu 0.05% +step 10000: train loss 4.4008, val loss 4.4163 +iter 10000: loss 4.6629, time 19425.84ms, mfu 0.05% + +--> RESULT | mlp128_qa | seed=1337 | BEST=4.3976 | FINAL=4.4163 | ✗(best@9750) ref=4.4282 delta=-0.0306 + + +mlp128_qa running mean (2/3 seeds): 4.4108 ±0.0132 ref=4.4282 + + +==================================================================================================== +RUNNING: mlp128_qa | warmup=2000 | seed=123 + MLP(128->128->64) on cat(Q,y) — capacity + dual signal + Reference: 4.4282 (DIRTY 3060 — this run replaces) +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.88M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 74, with 124,256,256 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9417, val loss 10.9407 +iter 0: loss 10.8864, time 19852.99ms, mfu -100.00% +step 250: train loss 6.8952, val loss 6.9470 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 250: loss 6.6891, time 22131.86ms, mfu 0.05% +step 500: train loss 6.5403, val loss 6.5212 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 500: loss 6.4820, time 22511.43ms, mfu 0.05% +step 750: train loss 6.3212, val loss 6.3206 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 750: loss 6.3295, time 22417.35ms, mfu 0.05% +step 1000: train loss 6.1656, val loss 6.1856 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 1000: loss 6.1740, time 22524.06ms, mfu 0.05% +step 1250: train loss 6.0740, val loss 6.0714 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 1250: loss 6.2705, time 22391.23ms, mfu 0.05% +step 1500: train loss 5.9377, val loss 5.8993 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 1500: loss 6.0117, time 22363.87ms, mfu 0.05% +step 1750: train loss 5.8584, val loss 5.8628 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 1750: loss 5.6294, time 22752.00ms, mfu 0.05% +step 2000: train loss 5.6726, val loss 5.7197 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 2000: loss 5.5789, time 22508.90ms, mfu 0.05% +step 2250: train loss 5.6275, val loss 5.6220 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 2250: loss 5.1822, time 22661.54ms, mfu 0.05% +step 2500: train loss 5.5537, val loss 5.5198 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 2500: loss 5.2311, time 22401.03ms, mfu 0.05% +step 2750: train loss 5.4421, val loss 5.4518 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 2750: loss 5.5271, time 22538.62ms, mfu 0.05% +step 3000: train loss 5.3593, val loss 5.3567 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 3000: loss 5.2735, time 22658.00ms, mfu 0.05% +step 3250: train loss 5.3218, val loss 5.3268 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 3250: loss 6.0172, time 22804.00ms, mfu 0.05% +step 3500: train loss 5.2415, val loss 5.2420 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 3500: loss 5.7964, time 23154.48ms, mfu 0.05% +step 3750: train loss 5.1942, val loss 5.1880 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 3750: loss 5.3685, time 23704.95ms, mfu 0.05% +step 4000: train loss 5.1494, val loss 5.1474 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 4000: loss 5.1964, time 22963.38ms, mfu 0.05% +step 4250: train loss 5.0483, val loss 5.0732 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 4250: loss 5.1110, time 22597.85ms, mfu 0.05% +step 4500: train loss 5.0259, val loss 5.0302 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 4500: loss 5.1676, time 22358.60ms, mfu 0.05% +step 4750: train loss 4.9406, val loss 4.9703 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 4750: loss 5.2971, time 22625.94ms, mfu 0.05% +step 5000: train loss 4.9025, val loss 4.9303 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 5000: loss 5.6154, time 22533.03ms, mfu 0.05% +step 5250: train loss 4.8502, val loss 4.8741 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 5250: loss 5.0390, time 22373.92ms, mfu 0.05% +step 5500: train loss 4.8397, val loss 4.8393 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 5500: loss 4.7868, time 22504.88ms, mfu 0.05% +step 5750: train loss 4.7785, val loss 4.8092 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 5750: loss 4.6485, time 22338.22ms, mfu 0.05% +step 6000: train loss 4.7152, val loss 4.7492 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 6000: loss 4.6176, time 22444.22ms, mfu 0.05% +step 6250: train loss 4.6942, val loss 4.7040 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 6250: loss 4.9124, time 22353.65ms, mfu 0.05% +step 6500: train loss 4.6944, val loss 4.6651 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 6500: loss 4.2574, time 22381.30ms, mfu 0.05% +step 6750: train loss 4.6252, val loss 4.6192 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 6750: loss 4.9418, time 22464.74ms, mfu 0.05% +step 7000: train loss 4.5969, val loss 4.5917 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 7000: loss 4.3840, time 22509.07ms, mfu 0.05% +step 7250: train loss 4.5678, val loss 4.5804 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 7250: loss 4.3111, time 22461.07ms, mfu 0.05% +step 7500: train loss 4.5727, val loss 4.5684 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 7500: loss 4.6639, time 22499.78ms, mfu 0.05% +step 7750: train loss 4.4993, val loss 4.5335 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 7750: loss 4.3439, time 22468.85ms, mfu 0.05% +step 8000: train loss 4.4937, val loss 4.5054 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 8000: loss 4.1516, time 22576.78ms, mfu 0.05% +step 8250: train loss 4.4914, val loss 4.5072 +iter 8250: loss 3.6515, time 19381.65ms, mfu 0.05% +step 8500: train loss 4.4494, val loss 4.4650 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 8500: loss 4.7586, time 22413.63ms, mfu 0.05% +step 8750: train loss 4.4052, val loss 4.4617 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 8750: loss 4.5224, time 22468.09ms, mfu 0.05% +step 9000: train loss 4.4354, val loss 4.4210 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 9000: loss 4.4506, time 22303.77ms, mfu 0.05% +step 9250: train loss 4.4081, val loss 4.4256 +iter 9250: loss 4.4480, time 19488.28ms, mfu 0.05% +step 9500: train loss 4.4178, val loss 4.4383 +iter 9500: loss 4.3337, time 19398.25ms, mfu 0.05% +step 9750: train loss 4.3968, val loss 4.3879 +saving checkpoint to out-openwebtext_dynamic_qa_conditioned_mlp128_warmup2000_seed123_p2 +iter 9750: loss 4.4995, time 22304.60ms, mfu 0.05% +step 10000: train loss 4.4160, val loss 4.4036 +iter 10000: loss 4.3844, time 19635.94ms, mfu 0.05% + +--> RESULT | mlp128_qa | seed=123 | BEST=4.3879 | FINAL=4.4036 | ✗(best@9750) ref=4.4282 delta=-0.0403 + + +mlp128_qa running mean (3/3 seeds): 4.4032 ±0.0153 ref=4.4282 + + +🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆 +UPDATED LEADERBOARD: + 1. cheap_qa 4.3897 ±0.0051 vs baseline: -0.0811 (P1) + 2. mlp128_qa 4.4032 ±0.0153 vs baseline: -0.0676 ← P2 + 3. midtier_q 4.4135 ±0.0086 vs baseline: -0.0573 (P1) + 4. cheap_q 4.4198 ±0.0185 vs baseline: -0.0510 (P1) + 5. baseline_p2 4.4688 ±0.0073 vs baseline: -0.0020 ← P2 + 6. baseline 4.4708 ±0.0074 vs baseline: +0.0000 (P1) +🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆 + + +==================================================================================================== +RUNNING: static_prehead | warmup=2000 | seed=42 + learned per-head scalar gate — 9 params, no signal + Reference: 4.4438 (A10 fairness sweep) +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.60M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 123,970,560 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9682, val loss 10.9677 +iter 0: loss 10.9624, time 18373.43ms, mfu -100.00% +step 250: train loss 6.9873, val loss 6.9835 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 250: loss 7.0967, time 21131.24ms, mfu 0.05% +step 500: train loss 6.5065, val loss 6.5115 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 500: loss 6.1549, time 21489.96ms, mfu 0.05% +step 750: train loss 6.2903, val loss 6.3220 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 750: loss 6.6212, time 21440.85ms, mfu 0.05% +step 1000: train loss 6.1579, val loss 6.1547 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 1000: loss 5.7954, time 21511.70ms, mfu 0.05% +step 1250: train loss 6.0166, val loss 6.0323 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 1250: loss 6.5322, time 21414.41ms, mfu 0.05% +step 1500: train loss 5.9537, val loss 5.9521 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 1500: loss 5.4712, time 21390.21ms, mfu 0.05% +step 1750: train loss 5.8669, val loss 5.8772 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 1750: loss 6.1713, time 21315.96ms, mfu 0.05% +step 2000: train loss 5.8150, val loss 5.8257 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 2000: loss 5.6222, time 21607.35ms, mfu 0.05% +step 2250: train loss 5.6681, val loss 5.6821 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 2250: loss 5.7404, time 23728.87ms, mfu 0.05% +step 2500: train loss 5.5900, val loss 5.5804 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 2500: loss 4.9055, time 22003.76ms, mfu 0.05% +step 2750: train loss 5.5026, val loss 5.5032 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 2750: loss 5.5399, time 21611.78ms, mfu 0.05% +step 3000: train loss 5.4308, val loss 5.4158 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 3000: loss 5.4768, time 21759.47ms, mfu 0.05% +step 3250: train loss 5.3914, val loss 5.3486 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 3250: loss 5.5411, time 21698.15ms, mfu 0.05% +step 3500: train loss 5.2891, val loss 5.3374 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 3500: loss 5.4025, time 22195.03ms, mfu 0.05% +step 3750: train loss 5.2628, val loss 5.2874 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 3750: loss 5.3058, time 21962.56ms, mfu 0.05% +step 4000: train loss 5.2087, val loss 5.2148 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 4000: loss 5.8774, time 21833.32ms, mfu 0.05% +step 4250: train loss 5.1704, val loss 5.1537 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 4250: loss 4.8993, time 21605.11ms, mfu 0.05% +step 4500: train loss 5.0839, val loss 5.1102 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 4500: loss 5.1931, time 21523.46ms, mfu 0.05% +step 4750: train loss 5.0671, val loss 5.0745 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 4750: loss 5.1491, time 21422.17ms, mfu 0.05% +step 5000: train loss 5.0255, val loss 4.9998 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 5000: loss 5.4521, time 21447.99ms, mfu 0.05% +step 5250: train loss 4.9611, val loss 4.9604 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 5250: loss 5.0849, time 21413.09ms, mfu 0.05% +step 5500: train loss 4.9523, val loss 4.9395 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 5500: loss 4.7047, time 21728.61ms, mfu 0.05% +step 5750: train loss 4.8761, val loss 4.8802 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 5750: loss 5.1306, time 23033.41ms, mfu 0.05% +step 6000: train loss 4.8147, val loss 4.8216 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 6000: loss 4.7583, time 21901.01ms, mfu 0.05% +step 6250: train loss 4.7731, val loss 4.7866 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 6250: loss 4.4558, time 21716.61ms, mfu 0.05% +step 6500: train loss 4.7657, val loss 4.7461 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 6500: loss 4.6071, time 22285.48ms, mfu 0.05% +step 6750: train loss 4.6977, val loss 4.6878 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 6750: loss 4.6569, time 21697.73ms, mfu 0.05% +step 7000: train loss 4.6521, val loss 4.6796 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 7000: loss 4.6189, time 22434.43ms, mfu 0.05% +step 7250: train loss 4.6355, val loss 4.6313 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 7250: loss 4.8222, time 21620.67ms, mfu 0.05% +step 7500: train loss 4.6326, val loss 4.6143 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 7500: loss 4.7335, time 22011.82ms, mfu 0.05% +step 7750: train loss 4.6071, val loss 4.5859 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 7750: loss 4.7042, time 21630.33ms, mfu 0.05% +step 8000: train loss 4.5655, val loss 4.5683 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 8000: loss 4.5342, time 21695.03ms, mfu 0.05% +step 8250: train loss 4.5213, val loss 4.5421 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 8250: loss 4.3668, time 22215.24ms, mfu 0.05% +step 8500: train loss 4.5310, val loss 4.5605 +iter 8500: loss 4.3939, time 18432.74ms, mfu 0.05% +step 8750: train loss 4.4840, val loss 4.5064 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 8750: loss 4.6545, time 23877.81ms, mfu 0.05% +step 9000: train loss 4.4979, val loss 4.5173 +iter 9000: loss 4.4840, time 18525.89ms, mfu 0.05% +step 9250: train loss 4.4736, val loss 4.4662 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 9250: loss 4.5863, time 24151.53ms, mfu 0.05% +step 9500: train loss 4.4477, val loss 4.4625 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 9500: loss 4.1875, time 21823.46ms, mfu 0.05% +step 9750: train loss 4.4416, val loss 4.4568 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed42_p2 +iter 9750: loss 4.7621, time 21778.17ms, mfu 0.05% +step 10000: train loss 4.4437, val loss 4.4710 +iter 10000: loss 4.4588, time 18464.72ms, mfu 0.05% + +--> RESULT | static_prehead | seed=42 | BEST=4.4568 | FINAL=4.4710 | ✗(best@9750) ref=4.4438 delta=+0.0130 + + +==================================================================================================== +RUNNING: static_prehead | warmup=2000 | seed=1337 + learned per-head scalar gate — 9 params, no signal + Reference: 4.4438 (A10 fairness sweep) +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.60M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 123,970,560 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9797, val loss 10.9790 +iter 0: loss 11.0013, time 18665.11ms, mfu -100.00% +step 250: train loss 6.9363, val loss 6.9696 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed1337_p2 +iter 250: loss 6.7254, time 21356.34ms, mfu 0.05% +step 500: train loss 6.4777, val loss 6.5452 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed1337_p2 +iter 500: loss 6.2611, time 23683.72ms, mfu 0.05% +step 750: train loss 6.3006, val loss 6.3183 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed1337_p2 +iter 750: loss 6.0861, time 22040.95ms, mfu 0.05% +step 1000: train loss 6.1791, val loss 6.1530 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed1337_p2 +iter 1000: loss 6.1485, time 22535.56ms, mfu 0.05% +step 1250: train loss 6.0544, val loss 6.0464 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed1337_p2 +iter 1250: loss 6.0948, time 22044.46ms, mfu 0.05% +step 1500: train loss 5.9878, val loss 5.9137 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed1337_p2 +Traceback (most recent call last): + File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 850, in save + _save( + File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 1114, in _save + zip_file.write_record(name, storage, num_bytes) +RuntimeError: [enforce fail at inline_container.cc:783] . PytorchStreamWriter failed writing file data/89: file write failed + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "C:\Users\brian\documents\nanogpt\train.py", line 257, in + torch.save(checkpoint, os.path.join(out_dir, 'ckpt.pt')) + File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 849, in save + with _open_zipfile_writer(f) as opened_zipfile: + File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 690, in __exit__ + self.file_like.write_end_of_file() +RuntimeError: [enforce fail at inline_container.cc:603] . unexpected pos 1316105152 vs 1316105104 + +--> RESULT | static_prehead | seed=1337 | BEST=5.9137 | FINAL=5.9137 | ✗(best@1500) ref=4.4438 delta=+1.4699 + + +static_prehead running mean (2/3 seeds): 5.1852 ±0.7285 ref=4.4438 + + +==================================================================================================== +RUNNING: static_prehead | warmup=2000 | seed=123 + learned per-head scalar gate — 9 params, no signal + Reference: 4.4438 (A10 fairness sweep) +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.60M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 123,970,560 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9594, val loss 10.9585 +iter 0: loss 10.9765, time 18771.56ms, mfu -100.00% +step 250: train loss 6.9612, val loss 6.9069 +saving checkpoint to out-openwebtext_static_gate_prehead_warmup2000_seed123_p2 +Traceback (most recent call last): + File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 850, in save + _save( + File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 1114, in _save + zip_file.write_record(name, storage, num_bytes) +RuntimeError: [enforce fail at inline_container.cc:783] . PytorchStreamWriter failed writing file data/0: file write failed + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "C:\Users\brian\documents\nanogpt\train.py", line 257, in + torch.save(checkpoint, os.path.join(out_dir, 'ckpt.pt')) + File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 849, in save + with _open_zipfile_writer(f) as opened_zipfile: + File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 690, in __exit__ + self.file_like.write_end_of_file() +RuntimeError: [enforce fail at inline_container.cc:603] . unexpected pos 45696 vs 45590 + +--> RESULT | static_prehead | seed=123 | BEST=6.9069 | FINAL=6.9069 | ✗(best@250) ref=4.4438 delta=+2.4631 + + +static_prehead running mean (3/3 seeds): 5.7591 ±1.0062 ref=4.4438 + + +🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆 +UPDATED LEADERBOARD: + 1. cheap_qa 4.3897 ±0.0051 vs baseline: -0.0811 (P1) + 2. mlp128_qa 4.4032 ±0.0153 vs baseline: -0.0676 ← P2 + 3. midtier_q 4.4135 ±0.0086 vs baseline: -0.0573 (P1) + 4. cheap_q 4.4198 ±0.0185 vs baseline: -0.0510 (P1) + 5. baseline_p2 4.4688 ±0.0073 vs baseline: -0.0020 ← P2 + 6. baseline 4.4708 ±0.0074 vs baseline: +0.0000 (P1) + 7. static_prehead 5.7591 ±1.0062 vs baseline: +1.2883 ← P2 +🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆 + + +==================================================================================================== +RUNNING: full_x | warmup=2000 | seed=42 + full X gate Linear(768->768) — paper implementation + Reference: 4.5374 (DIRTY 3060 — this run replaces) +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 130.67M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 131,039,232 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9285, val loss 10.9307 +iter 0: loss 10.9425, time 19549.63ms, mfu -100.00% +step 250: train loss 6.9144, val loss 6.9348 +saving checkpoint to out-openwebtext_dynamic_xconditioned_g1_warmup2000_seed42_p2 +Traceback (most recent call last): + File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 850, in save + _save( + File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 1114, in _save + zip_file.write_record(name, storage, num_bytes) +RuntimeError: [enforce fail at inline_container.cc:783] . PytorchStreamWriter failed writing file data/0: file write failed + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "C:\Users\brian\documents\nanogpt\train.py", line 257, in + torch.save(checkpoint, os.path.join(out_dir, 'ckpt.pt')) + File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 849, in save + with _open_zipfile_writer(f) as opened_zipfile: + File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 690, in __exit__ + self.file_like.write_end_of_file() +RuntimeError: [enforce fail at inline_container.cc:603] . unexpected pos 46464 vs 46358 + +--> RESULT | full_x | seed=42 | BEST=6.9348 | FINAL=6.9348 | ✗(best@250) ref=4.5374 delta=+2.3974 + + +==================================================================================================== +RUNNING: full_x | warmup=2000 | seed=1337 + full X gate Linear(768->768) — paper implementation + Reference: 4.5374 (DIRTY 3060 — this run replaces) +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 130.67M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 131,039,232 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9622, val loss 10.9587 +iter 0: loss 10.9558, time 19475.13ms, mfu -100.00% +step 250: train loss 6.9475, val loss 6.9370 +saving checkpoint to out-openwebtext_dynamic_xconditioned_g1_warmup2000_seed1337_p2 +Traceback (most recent call last): + File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 850, in save + _save( + File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 1114, in _save + zip_file.write_record(name, storage, num_bytes) +RuntimeError: [enforce fail at inline_container.cc:783] . PytorchStreamWriter failed writing file data/0: file write failed + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "C:\Users\brian\documents\nanogpt\train.py", line 257, in + torch.save(checkpoint, os.path.join(out_dir, 'ckpt.pt')) + File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 849, in save + with _open_zipfile_writer(f) as opened_zipfile: + File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 690, in __exit__ + self.file_like.write_end_of_file() +RuntimeError: [enforce fail at inline_container.cc:603] . unexpected pos 46464 vs 46358 + +--> RESULT | full_x | seed=1337 | BEST=6.9370 | FINAL=6.9370 | ✗(best@250) ref=4.5374 delta=+2.3996 + + +full_x running mean (2/3 seeds): 6.9359 ±0.0011 ref=4.5374 + + +==================================================================================================== +RUNNING: full_x | warmup=2000 | seed=123 + full X gate Linear(768->768) — paper implementation + Reference: 4.5374 (DIRTY 3060 — this run replaces) +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 130.67M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 131,039,232 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9733, val loss 10.9748 +iter 0: loss 10.9713, time 19703.30ms, mfu -100.00% +step 250: train loss 6.9196, val loss 6.9534 +saving checkpoint to out-openwebtext_dynamic_xconditioned_g1_warmup2000_seed123_p2 +Traceback (most recent call last): + File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 850, in save + _save( + File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 1114, in _save + zip_file.write_record(name, storage, num_bytes) +RuntimeError: [enforce fail at inline_container.cc:783] . PytorchStreamWriter failed writing file data/0: file write failed + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "C:\Users\brian\documents\nanogpt\train.py", line 257, in + torch.save(checkpoint, os.path.join(out_dir, 'ckpt.pt')) + File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 849, in save + with _open_zipfile_writer(f) as opened_zipfile: + File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 690, in __exit__ + self.file_like.write_end_of_file() +RuntimeError: [enforce fail at inline_container.cc:603] . unexpected pos 46464 vs 46358 + +--> RESULT | full_x | seed=123 | BEST=6.9534 | FINAL=6.9534 | ✗(best@250) ref=4.5374 delta=+2.4160 + + +full_x running mean (3/3 seeds): 6.9417 ±0.0083 ref=4.5374 + + +🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆 +UPDATED LEADERBOARD: + 1. cheap_qa 4.3897 ±0.0051 vs baseline: -0.0811 (P1) + 2. mlp128_qa 4.4032 ±0.0153 vs baseline: -0.0676 ← P2 + 3. midtier_q 4.4135 ±0.0086 vs baseline: -0.0573 (P1) + 4. cheap_q 4.4198 ±0.0185 vs baseline: -0.0510 (P1) + 5. baseline_p2 4.4688 ±0.0073 vs baseline: -0.0020 ← P2 + 6. baseline 4.4708 ±0.0074 vs baseline: +0.0000 (P1) + 7. static_prehead 5.7591 ±1.0062 vs baseline: +1.2883 ← P2 + 8. full_x 6.9417 ±0.0083 vs baseline: +2.4709 ← P2 +🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆 + + +==================================================================================================== +RUNNING: x_headshared | warmup=2000 | seed=42 + head-shared X gate Linear(64->64) + Reference: 4.5863 (DIRTY 3060 — this run replaces) +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.64M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 124,010,496 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9641, val loss 10.9611 +iter 0: loss 10.9442, time 18889.58ms, mfu -100.00% +step 250: train loss 6.9237, val loss 6.9409 +saving checkpoint to out-openwebtext_dynamic_x_g1_headshared_elementwise_warmup2000_seed42_p2 +Traceback (most recent call last): + File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 850, in save + _save( + File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 1114, in _save + zip_file.write_record(name, storage, num_bytes) +RuntimeError: [enforce fail at inline_container.cc:783] . PytorchStreamWriter failed writing file data/0: file write failed + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "C:\Users\brian\documents\nanogpt\train.py", line 257, in + torch.save(checkpoint, os.path.join(out_dir, 'ckpt.pt')) + File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 849, in save + with _open_zipfile_writer(f) as opened_zipfile: + File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 690, in __exit__ + self.file_like.write_end_of_file() +RuntimeError: [enforce fail at inline_container.cc:603] . unexpected pos 46720 vs 46614 + +--> RESULT | x_headshared | seed=42 | BEST=6.9409 | FINAL=6.9409 | ✗(best@250) ref=4.5863 delta=+2.3546 + + +==================================================================================================== +RUNNING: x_headshared | warmup=2000 | seed=1337 + head-shared X gate Linear(64->64) + Reference: 4.5863 (DIRTY 3060 — this run replaces) +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.64M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 124,010,496 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9310, val loss 10.9306 +iter 0: loss 10.9358, time 19184.13ms, mfu -100.00% +step 250: train loss 6.9042, val loss 6.9312 +saving checkpoint to out-openwebtext_dynamic_x_g1_headshared_elementwise_warmup2000_seed1337_p2 +Traceback (most recent call last): + File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 850, in save + _save( + File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 1114, in _save + zip_file.write_record(name, storage, num_bytes) +RuntimeError: [enforce fail at inline_container.cc:783] . PytorchStreamWriter failed writing file data/0: file write failed + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "C:\Users\brian\documents\nanogpt\train.py", line 257, in + torch.save(checkpoint, os.path.join(out_dir, 'ckpt.pt')) + File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 849, in save + with _open_zipfile_writer(f) as opened_zipfile: + File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 690, in __exit__ + self.file_like.write_end_of_file() +RuntimeError: [enforce fail at inline_container.cc:603] . unexpected pos 46720 vs 46614 + +--> RESULT | x_headshared | seed=1337 | BEST=6.9312 | FINAL=6.9312 | ✗(best@250) ref=4.5863 delta=+2.3449 + + +x_headshared running mean (2/3 seeds): 6.9360 ±0.0049 ref=4.5863 + + +==================================================================================================== +RUNNING: x_headshared | warmup=2000 | seed=123 + head-shared X gate Linear(64->64) + Reference: 4.5863 (DIRTY 3060 — this run replaces) +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.64M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 124,010,496 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9091, val loss 10.9097 +iter 0: loss 10.9225, time 19200.06ms, mfu -100.00% +step 250: train loss 6.9509, val loss 6.9769 +saving checkpoint to out-openwebtext_dynamic_x_g1_headshared_elementwise_warmup2000_seed123_p2 +Traceback (most recent call last): + File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 850, in save + _save( + File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 1114, in _save + zip_file.write_record(name, storage, num_bytes) +RuntimeError: [enforce fail at inline_container.cc:783] . PytorchStreamWriter failed writing file data/0: file write failed + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "C:\Users\brian\documents\nanogpt\train.py", line 257, in + torch.save(checkpoint, os.path.join(out_dir, 'ckpt.pt')) + File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 849, in save + with _open_zipfile_writer(f) as opened_zipfile: + File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 690, in __exit__ + self.file_like.write_end_of_file() +RuntimeError: [enforce fail at inline_container.cc:603] . unexpected pos 46720 vs 46614 + +--> RESULT | x_headshared | seed=123 | BEST=6.9769 | FINAL=6.9769 | ✗(best@250) ref=4.5863 delta=+2.3906 + + +x_headshared running mean (3/3 seeds): 6.9497 ±0.0197 ref=4.5863 + + +🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆 +UPDATED LEADERBOARD: + 1. cheap_qa 4.3897 ±0.0051 vs baseline: -0.0811 (P1) + 2. mlp128_qa 4.4032 ±0.0153 vs baseline: -0.0676 ← P2 + 3. midtier_q 4.4135 ±0.0086 vs baseline: -0.0573 (P1) + 4. cheap_q 4.4198 ±0.0185 vs baseline: -0.0510 (P1) + 5. baseline_p2 4.4688 ±0.0073 vs baseline: -0.0020 ← P2 + 6. baseline 4.4708 ±0.0074 vs baseline: +0.0000 (P1) + 7. static_prehead 5.7591 ±1.0062 vs baseline: +1.2883 ← P2 + 8. full_x 6.9417 ±0.0083 vs baseline: +2.4709 ← P2 + 9. x_headshared 6.9497 ±0.0197 vs baseline: +2.4789 ← P2 +🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆🏆 + + +==================================================================================================== +PRIORITY 2 COMPLETE +==================================================================================================== +Results CSV: priority2_runs_20260323_0623.csv +Summary: priority2_summary_20260323_0623.txt + +Paste summary here for final analysis. +Then: write the #bigasssummary and open a fresh window. +PS C:\Users\brian\documents\nanogpt> Get-ChildItem -Directory -Filter "out-openwebtext_*" | Remove-Item -Recurse -Force +PS C:\Users\brian\documents\nanogpt> Get-PSDrive C + +Name Used (GB) Free (GB) Provider Root CurrentLocation +---- --------- --------- -------- ---- --------------- +C 738.37 195.62 FileSystem C:\ Users\brian\documents\nanogpt + + +PS C:\Users\brian\documents\nanogpt> python p2_rerun_runner.py +==================================================================================================== +PRIORITY 2 RERUN — DISK CASUALTY RECOVERY +==================================================================================================== +Total runs: 9 | Est: ~9h 45min +CHECKPOINT SAVING: DISABLED ✓ (disk safe) + +Variants: + static_prehead — learned per-head scalar — 9 params, no signal + ref: 4.4438 (A10 fairness sweep) + full_x — full X gate Linear(768->768) on x_norm + ref: 4.5374 (DIRTY 3060) + x_headshared — head-shared X gate Linear(64->64) on x + ref: 4.5863 (DIRTY 3060) + +Current leaderboard for context: + cheap_qa 4.3897 ±0.0051 + mlp128_qa 4.4032 ±0.0153 + midtier_q 4.4135 ±0.0086 + cheap_q 4.4198 ±0.0185 + a_only 4.4298 ±0.0095 + baseline 4.4708 ±0.0074 +==================================================================================================== + +==================================================================================================== +RUNNING: static_prehead | warmup=2000 | seed=42 + learned per-head scalar — 9 params, no signal + NO CHECKPOINT SAVING — disk safe ✓ + Reference: 4.4438 ±0.0119 (A10 fairness sweep) +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.60M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 123,970,560 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9682, val loss 10.9677 +iter 0: loss 10.9624, time 18127.70ms, mfu -100.00% +step 250: train loss 6.9873, val loss 6.9835 +saving checkpoint to out-openwebtext_rerun_shared +iter 250: loss 7.0970, time 19627.78ms, mfu 0.05% +step 500: train loss 6.5065, val loss 6.5115 +saving checkpoint to out-openwebtext_rerun_shared +iter 500: loss 6.1548, time 19340.59ms, mfu 0.05% +step 750: train loss 6.2889, val loss 6.3204 +saving checkpoint to out-openwebtext_rerun_shared +iter 750: loss 6.6195, time 19183.41ms, mfu 0.05% +step 1000: train loss 6.1638, val loss 6.1607 +saving checkpoint to out-openwebtext_rerun_shared +iter 1000: loss 5.8180, time 19250.81ms, mfu 0.05% +step 1250: train loss 6.0059, val loss 6.0236 +saving checkpoint to out-openwebtext_rerun_shared +iter 1250: loss 6.5209, time 19189.72ms, mfu 0.05% +step 1500: train loss 5.9646, val loss 5.9579 +saving checkpoint to out-openwebtext_rerun_shared +iter 1500: loss 5.4895, time 19146.74ms, mfu 0.05% +step 1750: train loss 5.8735, val loss 5.8854 +saving checkpoint to out-openwebtext_rerun_shared +iter 1750: loss 6.1767, time 19312.43ms, mfu 0.05% +step 2000: train loss 5.8002, val loss 5.8123 +saving checkpoint to out-openwebtext_rerun_shared +iter 2000: loss 5.6055, time 19301.09ms, mfu 0.05% +step 2250: train loss 5.6611, val loss 5.6741 +saving checkpoint to out-openwebtext_rerun_shared +iter 2250: loss 5.7555, time 19495.18ms, mfu 0.05% +step 2500: train loss 5.5981, val loss 5.5940 +saving checkpoint to out-openwebtext_rerun_shared +iter 2500: loss 4.9232, time 20318.00ms, mfu 0.05% +step 2750: train loss 5.5083, val loss 5.5042 +saving checkpoint to out-openwebtext_rerun_shared +iter 2750: loss 5.5406, time 20180.09ms, mfu 0.05% +step 3000: train loss 5.4337, val loss 5.4194 +saving checkpoint to out-openwebtext_rerun_shared +iter 3000: loss 5.4812, time 19611.98ms, mfu 0.05% +step 3250: train loss 5.3883, val loss 5.3449 +saving checkpoint to out-openwebtext_rerun_shared +iter 3250: loss 5.5516, time 19349.88ms, mfu 0.05% +step 3500: train loss 5.2938, val loss 5.3456 +iter 3500: loss 5.4241, time 18145.04ms, mfu 0.05% +step 3750: train loss 5.2583, val loss 5.2814 +saving checkpoint to out-openwebtext_rerun_shared +iter 3750: loss 5.3021, time 19785.70ms, mfu 0.05% +step 4000: train loss 5.2098, val loss 5.2147 +saving checkpoint to out-openwebtext_rerun_shared +iter 4000: loss 5.8809, time 20262.53ms, mfu 0.05% +step 4250: train loss 5.1693, val loss 5.1547 +saving checkpoint to out-openwebtext_rerun_shared +iter 4250: loss 4.8967, time 19834.49ms, mfu 0.05% +step 4500: train loss 5.0857, val loss 5.1120 +saving checkpoint to out-openwebtext_rerun_shared +iter 4500: loss 5.1966, time 20197.86ms, mfu 0.05% +step 4750: train loss 5.0681, val loss 5.0741 +saving checkpoint to out-openwebtext_rerun_shared +iter 4750: loss 5.1653, time 19294.15ms, mfu 0.05% +step 5000: train loss 5.0148, val loss 4.9889 +saving checkpoint to out-openwebtext_rerun_shared +iter 5000: loss 5.4418, time 19250.52ms, mfu 0.05% +step 5250: train loss 4.9552, val loss 4.9577 +saving checkpoint to out-openwebtext_rerun_shared +iter 5250: loss 5.0776, time 19798.29ms, mfu 0.05% +step 5500: train loss 4.9456, val loss 4.9336 +saving checkpoint to out-openwebtext_rerun_shared +iter 5500: loss 4.6919, time 19748.73ms, mfu 0.05% +step 5750: train loss 4.8688, val loss 4.8734 +saving checkpoint to out-openwebtext_rerun_shared +iter 5750: loss 5.0975, time 19967.70ms, mfu 0.05% +step 6000: train loss 4.8107, val loss 4.8159 +saving checkpoint to out-openwebtext_rerun_shared +iter 6000: loss 4.7419, time 19405.03ms, mfu 0.05% +step 6250: train loss 4.7656, val loss 4.7774 +saving checkpoint to out-openwebtext_rerun_shared +iter 6250: loss 4.4581, time 19245.91ms, mfu 0.05% +step 6500: train loss 4.7652, val loss 4.7450 +saving checkpoint to out-openwebtext_rerun_shared +iter 6500: loss 4.6060, time 19315.13ms, mfu 0.05% +step 6750: train loss 4.6993, val loss 4.6855 +saving checkpoint to out-openwebtext_rerun_shared +iter 6750: loss 4.6537, time 19233.51ms, mfu 0.05% +step 7000: train loss 4.6526, val loss 4.6764 +saving checkpoint to out-openwebtext_rerun_shared +iter 7000: loss 4.6395, time 19865.11ms, mfu 0.05% +step 7250: train loss 4.6321, val loss 4.6303 +saving checkpoint to out-openwebtext_rerun_shared +iter 7250: loss 4.8298, time 19205.05ms, mfu 0.05% +step 7500: train loss 4.6301, val loss 4.6111 +saving checkpoint to out-openwebtext_rerun_shared +iter 7500: loss 4.7316, time 19187.12ms, mfu 0.05% +step 7750: train loss 4.6051, val loss 4.5827 +saving checkpoint to out-openwebtext_rerun_shared +iter 7750: loss 4.6957, time 19184.52ms, mfu 0.05% +step 8000: train loss 4.5623, val loss 4.5637 +saving checkpoint to out-openwebtext_rerun_shared +iter 8000: loss 4.5431, time 19143.82ms, mfu 0.05% +step 8250: train loss 4.5209, val loss 4.5406 +saving checkpoint to out-openwebtext_rerun_shared +iter 8250: loss 4.3536, time 19245.11ms, mfu 0.05% +step 8500: train loss 4.5286, val loss 4.5583 +iter 8500: loss 4.3886, time 17719.72ms, mfu 0.05% +step 8750: train loss 4.4823, val loss 4.5045 +saving checkpoint to out-openwebtext_rerun_shared +iter 8750: loss 4.6599, time 19254.91ms, mfu 0.05% +step 9000: train loss 4.4965, val loss 4.5172 +iter 9000: loss 4.4744, time 17618.39ms, mfu 0.06% +step 9250: train loss 4.4745, val loss 4.4649 +saving checkpoint to out-openwebtext_rerun_shared +iter 9250: loss 4.5835, time 19800.23ms, mfu 0.05% +step 9500: train loss 4.4473, val loss 4.4651 +iter 9500: loss 4.1806, time 17631.67ms, mfu 0.06% +step 9750: train loss 4.4425, val loss 4.4550 +saving checkpoint to out-openwebtext_rerun_shared +iter 9750: loss 4.7760, time 19352.06ms, mfu 0.06% +step 10000: train loss 4.4411, val loss 4.4699 +iter 10000: loss 4.4480, time 17651.47ms, mfu 0.06% + +--> RESULT | static_prehead | seed=42 | BEST=4.4550 | FINAL=4.4699 | ✗(best@9750) ref=4.4438 delta=+0.0112 + + +==================================================================================================== +RUNNING: static_prehead | warmup=2000 | seed=1337 + learned per-head scalar — 9 params, no signal + NO CHECKPOINT SAVING — disk safe ✓ + Reference: 4.4438 ±0.0119 (A10 fairness sweep) +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.60M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 123,970,560 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9797, val loss 10.9790 +iter 0: loss 11.0013, time 17869.34ms, mfu -100.00% +step 250: train loss 6.9363, val loss 6.9696 +saving checkpoint to out-openwebtext_rerun_shared +iter 250: loss 6.7253, time 19307.26ms, mfu 0.05% +step 500: train loss 6.4777, val loss 6.5451 +saving checkpoint to out-openwebtext_rerun_shared +iter 500: loss 6.2608, time 19318.54ms, mfu 0.05% +step 750: train loss 6.3010, val loss 6.3186 +saving checkpoint to out-openwebtext_rerun_shared +iter 750: loss 6.0866, time 19174.70ms, mfu 0.05% +step 1000: train loss 6.1738, val loss 6.1470 +saving checkpoint to out-openwebtext_rerun_shared +iter 1000: loss 6.1500, time 19344.49ms, mfu 0.05% +step 1250: train loss 6.0577, val loss 6.0501 +saving checkpoint to out-openwebtext_rerun_shared +iter 1250: loss 6.0931, time 19234.99ms, mfu 0.05% +step 1500: train loss 5.9933, val loss 5.9171 +saving checkpoint to out-openwebtext_rerun_shared +iter 1500: loss 5.7086, time 19372.42ms, mfu 0.05% +step 1750: train loss 5.9092, val loss 5.8722 +saving checkpoint to out-openwebtext_rerun_shared +iter 1750: loss 6.0696, time 19896.84ms, mfu 0.05% +step 2000: train loss 5.8108, val loss 5.7812 +saving checkpoint to out-openwebtext_rerun_shared +iter 2000: loss 5.8443, time 20153.43ms, mfu 0.05% +step 2250: train loss 5.6885, val loss 5.6941 +saving checkpoint to out-openwebtext_rerun_shared +iter 2250: loss 5.8955, time 19784.42ms, mfu 0.05% +step 2500: train loss 5.5932, val loss 5.5722 +saving checkpoint to out-openwebtext_rerun_shared +iter 2500: loss 6.0556, time 19835.77ms, mfu 0.05% +step 2750: train loss 5.5103, val loss 5.5028 +saving checkpoint to out-openwebtext_rerun_shared +iter 2750: loss 5.5081, time 19849.48ms, mfu 0.05% +step 3000: train loss 5.4236, val loss 5.4415 +saving checkpoint to out-openwebtext_rerun_shared +iter 3000: loss 5.4906, time 20137.08ms, mfu 0.05% +step 3250: train loss 5.3281, val loss 5.3341 +saving checkpoint to out-openwebtext_rerun_shared +iter 3250: loss 5.2934, time 19932.10ms, mfu 0.05% +step 3500: train loss 5.3371, val loss 5.2998 +saving checkpoint to out-openwebtext_rerun_shared +iter 3500: loss 5.2238, time 19931.09ms, mfu 0.05% +step 3750: train loss 5.2446, val loss 5.2326 +saving checkpoint to out-openwebtext_rerun_shared +iter 3750: loss 5.5530, time 19788.00ms, mfu 0.05% +step 4000: train loss 5.1971, val loss 5.1996 +saving checkpoint to out-openwebtext_rerun_shared +iter 4000: loss 5.2560, time 19275.87ms, mfu 0.05% +step 4250: train loss 5.1441, val loss 5.1573 +saving checkpoint to out-openwebtext_rerun_shared +iter 4250: loss 4.7499, time 19790.81ms, mfu 0.05% +step 4500: train loss 5.0993, val loss 5.0914 +saving checkpoint to out-openwebtext_rerun_shared +iter 4500: loss 4.8418, time 20029.28ms, mfu 0.05% +step 4750: train loss 5.0386, val loss 5.0487 +saving checkpoint to out-openwebtext_rerun_shared +iter 4750: loss 5.1462, time 19721.30ms, mfu 0.05% +step 5000: train loss 4.9718, val loss 5.0090 +saving checkpoint to out-openwebtext_rerun_shared +iter 5000: loss 5.1591, time 20147.85ms, mfu 0.05% +step 5250: train loss 4.9672, val loss 4.9525 +saving checkpoint to out-openwebtext_rerun_shared +iter 5250: loss 4.6187, time 20372.44ms, mfu 0.05% +step 5500: train loss 4.9482, val loss 4.9098 +saving checkpoint to out-openwebtext_rerun_shared +iter 5500: loss 5.1646, time 19786.75ms, mfu 0.05% +step 5750: train loss 4.8786, val loss 4.8386 +saving checkpoint to out-openwebtext_rerun_shared +iter 5750: loss 5.1269, time 20056.09ms, mfu 0.05% +step 6000: train loss 4.7852, val loss 4.7896 +saving checkpoint to out-openwebtext_rerun_shared +iter 6000: loss 4.6789, time 20154.04ms, mfu 0.05% +step 6250: train loss 4.7645, val loss 4.7962 +iter 6250: loss 4.5706, time 18240.35ms, mfu 0.05% +step 6500: train loss 4.7267, val loss 4.7615 +saving checkpoint to out-openwebtext_rerun_shared +iter 6500: loss 4.9074, time 20207.04ms, mfu 0.05% +step 6750: train loss 4.6923, val loss 4.6991 +saving checkpoint to out-openwebtext_rerun_shared +iter 6750: loss 4.8792, time 20150.57ms, mfu 0.05% +step 7000: train loss 4.6574, val loss 4.6533 +saving checkpoint to out-openwebtext_rerun_shared +iter 7000: loss 4.5625, time 20298.82ms, mfu 0.05% +step 7250: train loss 4.6120, val loss 4.6422 +saving checkpoint to out-openwebtext_rerun_shared +iter 7250: loss 4.7262, time 20416.51ms, mfu 0.05% +step 7500: train loss 4.6216, val loss 4.5774 +saving checkpoint to out-openwebtext_rerun_shared +iter 7500: loss 4.4983, time 20477.05ms, mfu 0.05% +step 7750: train loss 4.5701, val loss 4.5632 +saving checkpoint to out-openwebtext_rerun_shared +iter 7750: loss 4.7535, time 20334.46ms, mfu 0.05% +step 8000: train loss 4.5329, val loss 4.5565 +saving checkpoint to out-openwebtext_rerun_shared +iter 8000: loss 4.5902, time 19831.93ms, mfu 0.05% +step 8250: train loss 4.5099, val loss 4.5400 +saving checkpoint to out-openwebtext_rerun_shared +iter 8250: loss 4.2967, time 20145.05ms, mfu 0.05% +step 8500: train loss 4.5072, val loss 4.5162 +saving checkpoint to out-openwebtext_rerun_shared +iter 8500: loss 4.2451, time 19866.00ms, mfu 0.05% +step 8750: train loss 4.4704, val loss 4.4724 +saving checkpoint to out-openwebtext_rerun_shared +iter 8750: loss 4.4906, time 20100.74ms, mfu 0.05% +step 9000: train loss 4.4839, val loss 4.4884 +iter 9000: loss 4.5710, time 18196.53ms, mfu 0.05% +step 9250: train loss 4.4455, val loss 4.4766 +iter 9250: loss 4.5147, time 18197.45ms, mfu 0.05% +step 9500: train loss 4.4517, val loss 4.4631 +saving checkpoint to out-openwebtext_rerun_shared +iter 9500: loss 4.4707, time 20164.18ms, mfu 0.05% +step 9750: train loss 4.4562, val loss 4.4340 +saving checkpoint to out-openwebtext_rerun_shared +iter 9750: loss 4.3577, time 20186.83ms, mfu 0.05% +step 10000: train loss 4.4490, val loss 4.4742 +iter 10000: loss 4.4786, time 18117.43ms, mfu 0.05% + +--> RESULT | static_prehead | seed=1337 | BEST=4.4340 | FINAL=4.4742 | ✗(best@9750) ref=4.4438 delta=-0.0098 + + +static_prehead running mean (2/3 seeds): 4.4445 ±0.0105 ref=4.4438 + + +==================================================================================================== +RUNNING: static_prehead | warmup=2000 | seed=123 + learned per-head scalar — 9 params, no signal + NO CHECKPOINT SAVING — disk safe ✓ + Reference: 4.4438 ±0.0119 (A10 fairness sweep) +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.60M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 123,970,560 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9594, val loss 10.9585 +iter 0: loss 10.9765, time 17940.17ms, mfu -100.00% +step 250: train loss 6.9611, val loss 6.9069 +saving checkpoint to out-openwebtext_rerun_shared +iter 250: loss 6.6577, time 19337.17ms, mfu 0.05% +step 500: train loss 6.4898, val loss 6.5664 +saving checkpoint to out-openwebtext_rerun_shared +iter 500: loss 6.5983, time 20267.61ms, mfu 0.05% +step 750: train loss 6.3091, val loss 6.3137 +saving checkpoint to out-openwebtext_rerun_shared +iter 750: loss 5.6393, time 20162.08ms, mfu 0.05% +step 1000: train loss 6.1905, val loss 6.1895 +saving checkpoint to out-openwebtext_rerun_shared +iter 1000: loss 6.4894, time 20204.55ms, mfu 0.05% +step 1250: train loss 6.0205, val loss 6.0481 +saving checkpoint to out-openwebtext_rerun_shared +iter 1250: loss 5.7661, time 19865.70ms, mfu 0.05% +step 1500: train loss 5.9799, val loss 5.9497 +saving checkpoint to out-openwebtext_rerun_shared +iter 1500: loss 5.7764, time 20167.88ms, mfu 0.05% +step 1750: train loss 5.8743, val loss 5.9126 +saving checkpoint to out-openwebtext_rerun_shared +iter 1750: loss 6.5066, time 20448.90ms, mfu 0.05% +step 2000: train loss 5.8144, val loss 5.8523 +saving checkpoint to out-openwebtext_rerun_shared +iter 2000: loss 5.7585, time 20182.09ms, mfu 0.05% +step 2250: train loss 5.6531, val loss 5.7110 +saving checkpoint to out-openwebtext_rerun_shared +iter 2250: loss 6.1416, time 20264.91ms, mfu 0.05% +step 2500: train loss 5.6265, val loss 5.6316 +saving checkpoint to out-openwebtext_rerun_shared +iter 2500: loss 5.8583, time 20175.78ms, mfu 0.05% +step 2750: train loss 5.5600, val loss 5.5333 +saving checkpoint to out-openwebtext_rerun_shared +iter 2750: loss 6.0901, time 19911.52ms, mfu 0.05% +step 3000: train loss 5.4712, val loss 5.4740 +saving checkpoint to out-openwebtext_rerun_shared +iter 3000: loss 6.2075, time 19864.62ms, mfu 0.05% +step 3250: train loss 5.3702, val loss 5.3662 +saving checkpoint to out-openwebtext_rerun_shared +iter 3250: loss 5.3279, time 19855.07ms, mfu 0.05% +step 3500: train loss 5.3037, val loss 5.3502 +saving checkpoint to out-openwebtext_rerun_shared +iter 3500: loss 5.3613, time 19841.98ms, mfu 0.05% +step 3750: train loss 5.2672, val loss 5.2519 +saving checkpoint to out-openwebtext_rerun_shared +iter 3750: loss 5.0321, time 19748.97ms, mfu 0.05% +step 4000: train loss 5.2178, val loss 5.2289 +saving checkpoint to out-openwebtext_rerun_shared +iter 4000: loss 5.1394, time 19428.41ms, mfu 0.05% +step 4250: train loss 5.1757, val loss 5.1863 +saving checkpoint to out-openwebtext_rerun_shared +iter 4250: loss 5.2375, time 20081.08ms, mfu 0.05% +step 4500: train loss 5.1027, val loss 5.1043 +saving checkpoint to out-openwebtext_rerun_shared +iter 4500: loss 5.1631, time 20247.52ms, mfu 0.05% +step 4750: train loss 5.0881, val loss 5.0843 +saving checkpoint to out-openwebtext_rerun_shared +iter 4750: loss 4.9750, time 20320.59ms, mfu 0.05% +step 5000: train loss 4.9971, val loss 4.9959 +saving checkpoint to out-openwebtext_rerun_shared +iter 5000: loss 5.1270, time 19703.84ms, mfu 0.05% +step 5250: train loss 4.9835, val loss 4.9708 +saving checkpoint to out-openwebtext_rerun_shared +iter 5250: loss 5.1620, time 19844.66ms, mfu 0.05% +step 5500: train loss 4.9022, val loss 4.9401 +saving checkpoint to out-openwebtext_rerun_shared +iter 5500: loss 4.7989, time 20296.94ms, mfu 0.05% +step 5750: train loss 4.9243, val loss 4.9046 +saving checkpoint to out-openwebtext_rerun_shared +iter 5750: loss 5.2501, time 20250.15ms, mfu 0.05% +step 6000: train loss 4.8699, val loss 4.8389 +saving checkpoint to out-openwebtext_rerun_shared +iter 6000: loss 4.8798, time 19764.29ms, mfu 0.05% +step 6250: train loss 4.7956, val loss 4.8488 +iter 6250: loss 5.0271, time 18187.94ms, mfu 0.05% +step 6500: train loss 4.7592, val loss 4.7660 +saving checkpoint to out-openwebtext_rerun_shared +iter 6500: loss 4.6352, time 20237.10ms, mfu 0.05% +step 6750: train loss 4.7142, val loss 4.7359 +saving checkpoint to out-openwebtext_rerun_shared +iter 6750: loss 4.5783, time 20434.93ms, mfu 0.05% +step 7000: train loss 4.6790, val loss 4.6877 +saving checkpoint to out-openwebtext_rerun_shared +iter 7000: loss 4.6195, time 20262.29ms, mfu 0.05% +step 7250: train loss 4.6506, val loss 4.6781 +saving checkpoint to out-openwebtext_rerun_shared +iter 7250: loss 4.2664, time 19936.76ms, mfu 0.05% +step 7500: train loss 4.6580, val loss 4.6395 +saving checkpoint to out-openwebtext_rerun_shared +iter 7500: loss 4.5987, time 19904.72ms, mfu 0.05% +step 7750: train loss 4.6078, val loss 4.6037 +saving checkpoint to out-openwebtext_rerun_shared +iter 7750: loss 4.8012, time 20139.25ms, mfu 0.05% +step 8000: train loss 4.5832, val loss 4.5790 +saving checkpoint to out-openwebtext_rerun_shared +iter 8000: loss 4.1934, time 20526.58ms, mfu 0.05% +step 8250: train loss 4.5383, val loss 4.5358 +saving checkpoint to out-openwebtext_rerun_shared +iter 8250: loss 4.3608, time 19760.66ms, mfu 0.05% +step 8500: train loss 4.5245, val loss 4.5444 +iter 8500: loss 4.3462, time 18174.37ms, mfu 0.05% +step 8750: train loss 4.5036, val loss 4.5170 +saving checkpoint to out-openwebtext_rerun_shared +iter 8750: loss 4.3588, time 19684.72ms, mfu 0.05% +step 9000: train loss 4.5158, val loss 4.4975 +saving checkpoint to out-openwebtext_rerun_shared +iter 9000: loss 4.2739, time 20074.10ms, mfu 0.05% +step 9250: train loss 4.4655, val loss 4.4908 +saving checkpoint to out-openwebtext_rerun_shared +iter 9250: loss 5.0094, time 19820.18ms, mfu 0.05% +step 9500: train loss 4.4609, val loss 4.4559 +saving checkpoint to out-openwebtext_rerun_shared +iter 9500: loss 4.9194, time 20064.32ms, mfu 0.05% +step 9750: train loss 4.4836, val loss 4.5074 +iter 9750: loss 4.4104, time 18149.13ms, mfu 0.05% +step 10000: train loss 4.4701, val loss 4.4777 +iter 10000: loss 4.6073, time 18234.34ms, mfu 0.05% + +--> RESULT | static_prehead | seed=123 | BEST=4.4559 | FINAL=4.4777 | ✗(best@9500) ref=4.4438 delta=+0.0121 + + +static_prehead running mean (3/3 seeds): 4.4483 ±0.0101 ref=4.4438 + + +==================================================================================================== +RUNNING: full_x | warmup=2000 | seed=42 + full X gate Linear(768->768) on x_norm + NO CHECKPOINT SAVING — disk safe ✓ + Reference: 4.5374 ±0.0089 (DIRTY 3060) +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 130.67M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 131,039,232 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9285, val loss 10.9307 +iter 0: loss 10.9425, time 18613.26ms, mfu -100.00% +step 250: train loss 6.9144, val loss 6.9347 +saving checkpoint to out-openwebtext_rerun_shared +iter 250: loss 6.5241, time 20016.14ms, mfu 0.06% +step 500: train loss 6.5146, val loss 6.5229 +saving checkpoint to out-openwebtext_rerun_shared +iter 500: loss 6.4255, time 21000.03ms, mfu 0.05% +step 750: train loss 6.3131, val loss 6.3068 +saving checkpoint to out-openwebtext_rerun_shared +iter 750: loss 6.6228, time 20501.28ms, mfu 0.05% +step 1000: train loss 6.1492, val loss 6.1616 +saving checkpoint to out-openwebtext_rerun_shared +iter 1000: loss 6.2063, time 20952.72ms, mfu 0.05% +step 1250: train loss 6.0261, val loss 6.0082 +saving checkpoint to out-openwebtext_rerun_shared +iter 1250: loss 5.9983, time 20582.42ms, mfu 0.05% +step 1500: train loss 5.9283, val loss 5.9923 +saving checkpoint to out-openwebtext_rerun_shared +iter 1500: loss 5.6249, time 20954.07ms, mfu 0.05% +step 1750: train loss 5.8572, val loss 5.9159 +saving checkpoint to out-openwebtext_rerun_shared +iter 1750: loss 5.8913, time 21191.69ms, mfu 0.05% +step 2000: train loss 5.7952, val loss 5.7668 +saving checkpoint to out-openwebtext_rerun_shared +iter 2000: loss 5.8754, time 20827.53ms, mfu 0.05% +step 2250: train loss 5.6396, val loss 5.6429 +saving checkpoint to out-openwebtext_rerun_shared +iter 2250: loss 5.6144, time 20627.01ms, mfu 0.05% +step 2500: train loss 5.5551, val loss 5.5489 +saving checkpoint to out-openwebtext_rerun_shared +iter 2500: loss 5.7884, time 20646.33ms, mfu 0.05% +step 2750: train loss 5.4817, val loss 5.4644 +saving checkpoint to out-openwebtext_rerun_shared +iter 2750: loss 5.5370, time 20846.94ms, mfu 0.05% +step 3000: train loss 5.3793, val loss 5.3767 +saving checkpoint to out-openwebtext_rerun_shared +iter 3000: loss 5.9443, time 20585.55ms, mfu 0.05% +step 3250: train loss 5.3299, val loss 5.3340 +saving checkpoint to out-openwebtext_rerun_shared +iter 3250: loss 5.2913, time 20780.19ms, mfu 0.05% +step 3500: train loss 5.2434, val loss 5.2421 +saving checkpoint to out-openwebtext_rerun_shared +iter 3500: loss 5.6948, time 21408.23ms, mfu 0.05% +step 3750: train loss 5.2180, val loss 5.2112 +saving checkpoint to out-openwebtext_rerun_shared +iter 3750: loss 5.3472, time 20982.33ms, mfu 0.05% +step 4000: train loss 5.1540, val loss 5.1706 +saving checkpoint to out-openwebtext_rerun_shared +iter 4000: loss 5.1421, time 21413.50ms, mfu 0.05% +step 4250: train loss 5.0985, val loss 5.1041 +saving checkpoint to out-openwebtext_rerun_shared +iter 4250: loss 4.9729, time 21169.48ms, mfu 0.05% +step 4500: train loss 5.0545, val loss 5.0480 +saving checkpoint to out-openwebtext_rerun_shared +iter 4500: loss 4.8987, time 21249.05ms, mfu 0.05% +step 4750: train loss 4.9629, val loss 4.9967 +saving checkpoint to out-openwebtext_rerun_shared +iter 4750: loss 4.9016, time 20752.09ms, mfu 0.05% +step 5000: train loss 4.9408, val loss 4.9471 +saving checkpoint to out-openwebtext_rerun_shared +iter 5000: loss 4.9525, time 20647.30ms, mfu 0.05% +step 5250: train loss 4.8890, val loss 4.8812 +saving checkpoint to out-openwebtext_rerun_shared +iter 5250: loss 4.8073, time 21255.21ms, mfu 0.05% +step 5500: train loss 4.8295, val loss 4.8371 +saving checkpoint to out-openwebtext_rerun_shared +iter 5500: loss 5.0168, time 20618.01ms, mfu 0.05% +step 5750: train loss 4.7918, val loss 4.7844 +saving checkpoint to out-openwebtext_rerun_shared +iter 5750: loss 4.8882, time 20503.20ms, mfu 0.05% +step 6000: train loss 4.7652, val loss 4.7484 +saving checkpoint to out-openwebtext_rerun_shared +iter 6000: loss 4.5550, time 20683.87ms, mfu 0.05% +step 6250: train loss 4.7062, val loss 4.7031 +saving checkpoint to out-openwebtext_rerun_shared +iter 6250: loss 4.5142, time 20862.16ms, mfu 0.05% +step 6500: train loss 4.6721, val loss 4.7066 +iter 6500: loss 4.4941, time 19018.76ms, mfu 0.05% +step 6750: train loss 4.6424, val loss 4.6431 +saving checkpoint to out-openwebtext_rerun_shared +iter 6750: loss 4.6010, time 20650.63ms, mfu 0.05% +step 7000: train loss 4.6049, val loss 4.6303 +saving checkpoint to out-openwebtext_rerun_shared +iter 7000: loss 4.8959, time 20619.01ms, mfu 0.05% +step 7250: train loss 4.5929, val loss 4.5929 +saving checkpoint to out-openwebtext_rerun_shared +iter 7250: loss 5.0342, time 21849.97ms, mfu 0.05% +step 7500: train loss 4.5827, val loss 4.5758 +saving checkpoint to out-openwebtext_rerun_shared +iter 7500: loss 4.5337, time 21527.56ms, mfu 0.05% +step 7750: train loss 4.5223, val loss 4.5464 +saving checkpoint to out-openwebtext_rerun_shared +iter 7750: loss 4.8966, time 20801.43ms, mfu 0.05% +step 8000: train loss 4.5235, val loss 4.4956 +saving checkpoint to out-openwebtext_rerun_shared +iter 8000: loss 4.1206, time 21340.77ms, mfu 0.05% +step 8250: train loss 4.5100, val loss 4.4775 +saving checkpoint to out-openwebtext_rerun_shared +iter 8250: loss 4.6659, time 20915.50ms, mfu 0.05% +step 8500: train loss 4.4690, val loss 4.4648 +saving checkpoint to out-openwebtext_rerun_shared +iter 8500: loss 4.5679, time 21302.63ms, mfu 0.05% +step 8750: train loss 4.4647, val loss 4.4492 +saving checkpoint to out-openwebtext_rerun_shared +iter 8750: loss 4.3315, time 20571.26ms, mfu 0.05% +step 9000: train loss 4.4418, val loss 4.4417 +saving checkpoint to out-openwebtext_rerun_shared +iter 9000: loss 4.5420, time 21047.08ms, mfu 0.05% +step 9250: train loss 4.4395, val loss 4.4142 +saving checkpoint to out-openwebtext_rerun_shared +iter 9250: loss 4.2225, time 22070.41ms, mfu 0.05% +step 9500: train loss 4.4096, val loss 4.4299 +iter 9500: loss 4.1981, time 20052.92ms, mfu 0.05% +step 9750: train loss 4.4143, val loss 4.4290 +iter 9750: loss 4.2634, time 19170.79ms, mfu 0.05% +step 10000: train loss 4.4021, val loss 4.3897 +saving checkpoint to out-openwebtext_rerun_shared +iter 10000: loss 4.6448, time 21442.38ms, mfu 0.05% + +--> RESULT | full_x | seed=42 | BEST=4.3897 | FINAL=4.3897 | ✓ ref=4.5374 delta=-0.1477 + + +==================================================================================================== +RUNNING: full_x | warmup=2000 | seed=1337 + full X gate Linear(768->768) on x_norm + NO CHECKPOINT SAVING — disk safe ✓ + Reference: 4.5374 ±0.0089 (DIRTY 3060) +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 130.67M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 131,039,232 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9622, val loss 10.9587 +iter 0: loss 10.9558, time 19309.82ms, mfu -100.00% +step 250: train loss 6.9475, val loss 6.9370 +saving checkpoint to out-openwebtext_rerun_shared +iter 250: loss 6.7684, time 20799.91ms, mfu 0.05% +step 500: train loss 6.5307, val loss 6.5190 +saving checkpoint to out-openwebtext_rerun_shared +iter 500: loss 6.1714, time 21800.07ms, mfu 0.05% +step 750: train loss 6.2888, val loss 6.2941 +saving checkpoint to out-openwebtext_rerun_shared +iter 750: loss 6.5801, time 22081.82ms, mfu 0.05% +step 1000: train loss 6.1255, val loss 6.1427 +saving checkpoint to out-openwebtext_rerun_shared +iter 1000: loss 6.1494, time 23116.42ms, mfu 0.05% +step 1250: train loss 6.0369, val loss 6.0447 +saving checkpoint to out-openwebtext_rerun_shared +iter 1250: loss 5.8991, time 22164.74ms, mfu 0.05% +step 1500: train loss 5.9685, val loss 5.9420 +saving checkpoint to out-openwebtext_rerun_shared +iter 1500: loss 6.0893, time 22073.11ms, mfu 0.05% +step 1750: train loss 5.8133, val loss 5.8078 +saving checkpoint to out-openwebtext_rerun_shared +iter 1750: loss 5.4630, time 22096.70ms, mfu 0.05% +step 2000: train loss 5.7433, val loss 5.7429 +saving checkpoint to out-openwebtext_rerun_shared +iter 2000: loss 5.6494, time 22009.14ms, mfu 0.05% +step 2250: train loss 5.6492, val loss 5.6433 +saving checkpoint to out-openwebtext_rerun_shared +iter 2250: loss 5.9185, time 22138.39ms, mfu 0.05% +step 2500: train loss 5.5626, val loss 5.5347 +saving checkpoint to out-openwebtext_rerun_shared +iter 2500: loss 6.0863, time 22099.61ms, mfu 0.05% +step 2750: train loss 5.4159, val loss 5.4615 +saving checkpoint to out-openwebtext_rerun_shared +iter 2750: loss 5.7968, time 22166.26ms, mfu 0.05% +step 3000: train loss 5.3953, val loss 5.4148 +saving checkpoint to out-openwebtext_rerun_shared +iter 3000: loss 4.8958, time 22133.76ms, mfu 0.05% +step 3250: train loss 5.3116, val loss 5.3244 +saving checkpoint to out-openwebtext_rerun_shared +iter 3250: loss 5.2315, time 22169.38ms, mfu 0.05% +step 3500: train loss 5.2704, val loss 5.2726 +saving checkpoint to out-openwebtext_rerun_shared +iter 3500: loss 5.0322, time 22041.67ms, mfu 0.05% +step 3750: train loss 5.1632, val loss 5.1729 +saving checkpoint to out-openwebtext_rerun_shared +iter 3750: loss 5.1488, time 22045.19ms, mfu 0.05% +step 4000: train loss 5.1663, val loss 5.1553 +saving checkpoint to out-openwebtext_rerun_shared +iter 4000: loss 5.3464, time 21972.18ms, mfu 0.05% +step 4250: train loss 5.1070, val loss 5.0698 +saving checkpoint to out-openwebtext_rerun_shared +iter 4250: loss 5.5115, time 21980.61ms, mfu 0.05% +step 4500: train loss 5.0632, val loss 5.0741 +iter 4500: loss 4.8094, time 18906.77ms, mfu 0.05% +step 4750: train loss 4.9833, val loss 5.0168 +saving checkpoint to out-openwebtext_rerun_shared +iter 4750: loss 4.9448, time 22229.91ms, mfu 0.05% +step 5000: train loss 4.9854, val loss 4.9203 +saving checkpoint to out-openwebtext_rerun_shared +iter 5000: loss 5.0599, time 22048.05ms, mfu 0.05% +step 5250: train loss 4.9081, val loss 4.9042 +saving checkpoint to out-openwebtext_rerun_shared +iter 5250: loss 5.0660, time 22051.86ms, mfu 0.05% +step 5500: train loss 4.8525, val loss 4.8534 +saving checkpoint to out-openwebtext_rerun_shared +iter 5500: loss 4.7744, time 22053.63ms, mfu 0.05% +step 5750: train loss 4.7794, val loss 4.8107 +saving checkpoint to out-openwebtext_rerun_shared +iter 5750: loss 4.8149, time 22046.99ms, mfu 0.05% +step 6000: train loss 4.7210, val loss 4.7304 +saving checkpoint to out-openwebtext_rerun_shared +iter 6000: loss 4.2898, time 21992.04ms, mfu 0.05% +step 6250: train loss 4.6971, val loss 4.7060 +saving checkpoint to out-openwebtext_rerun_shared +iter 6250: loss 4.3267, time 21995.14ms, mfu 0.05% +step 6500: train loss 4.6595, val loss 4.6745 +saving checkpoint to out-openwebtext_rerun_shared +iter 6500: loss 4.7315, time 22150.84ms, mfu 0.05% +step 6750: train loss 4.6432, val loss 4.6403 +saving checkpoint to out-openwebtext_rerun_shared +iter 6750: loss 5.1942, time 22180.68ms, mfu 0.05% +step 7000: train loss 4.6246, val loss 4.6116 +saving checkpoint to out-openwebtext_rerun_shared +iter 7000: loss 4.6395, time 22137.66ms, mfu 0.05% +step 7250: train loss 4.5563, val loss 4.5964 +saving checkpoint to out-openwebtext_rerun_shared +iter 7250: loss 4.6274, time 22097.22ms, mfu 0.05% +step 7500: train loss 4.5485, val loss 4.5865 +saving checkpoint to out-openwebtext_rerun_shared +iter 7500: loss 4.6290, time 22046.79ms, mfu 0.05% +step 7750: train loss 4.5392, val loss 4.5414 +saving checkpoint to out-openwebtext_rerun_shared +iter 7750: loss 4.7038, time 22026.53ms, mfu 0.05% +step 8000: train loss 4.5076, val loss 4.5206 +saving checkpoint to out-openwebtext_rerun_shared +iter 8000: loss 4.7464, time 22381.47ms, mfu 0.05% +step 8250: train loss 4.4756, val loss 4.4942 +saving checkpoint to out-openwebtext_rerun_shared +iter 8250: loss 4.7371, time 22057.15ms, mfu 0.05% +step 8500: train loss 4.4653, val loss 4.4707 +saving checkpoint to out-openwebtext_rerun_shared +iter 8500: loss 4.4583, time 22073.25ms, mfu 0.05% +step 8750: train loss 4.4675, val loss 4.4640 +saving checkpoint to out-openwebtext_rerun_shared +iter 8750: loss 4.5219, time 21906.75ms, mfu 0.05% +step 9000: train loss 4.4072, val loss 4.4665 +iter 9000: loss 4.4988, time 19042.02ms, mfu 0.05% +step 9250: train loss 4.4372, val loss 4.4327 +saving checkpoint to out-openwebtext_rerun_shared +iter 9250: loss 4.3705, time 22165.08ms, mfu 0.05% +step 9500: train loss 4.4077, val loss 4.4170 +saving checkpoint to out-openwebtext_rerun_shared +iter 9500: loss 4.8012, time 23740.18ms, mfu 0.05% +step 9750: train loss 4.4187, val loss 4.4192 +iter 9750: loss 4.4308, time 18830.98ms, mfu 0.05% +step 10000: train loss 4.3686, val loss 4.3842 +saving checkpoint to out-openwebtext_rerun_shared +iter 10000: loss 4.9276, time 20411.83ms, mfu 0.05% + +--> RESULT | full_x | seed=1337 | BEST=4.3842 | FINAL=4.3842 | ✓ ref=4.5374 delta=-0.1532 + + +full_x running mean (2/3 seeds): 4.3870 ±0.0028 ref=4.5374 + + +==================================================================================================== +RUNNING: full_x | warmup=2000 | seed=123 + full X gate Linear(768->768) on x_norm + NO CHECKPOINT SAVING — disk safe ✓ + Reference: 4.5374 ±0.0089 (DIRTY 3060) +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 130.67M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 131,039,232 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True + +--> FAILED | full_x | seed=123 | exit code 3221226505 + + +full_x running mean (2/3 seeds): 4.3870 ±0.0028 ref=4.5374 + + +==================================================================================================== +RUNNING: x_headshared | warmup=2000 | seed=42 + head-shared X gate Linear(64->64) on x + NO CHECKPOINT SAVING — disk safe ✓ + Reference: 4.5863 ±0.0116 (DIRTY 3060) +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.64M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 124,010,496 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9641, val loss 10.9611 +iter 0: loss 10.9442, time 18495.27ms, mfu -100.00% +step 250: train loss 6.9237, val loss 6.9409 +saving checkpoint to out-openwebtext_rerun_shared +iter 250: loss 6.8589, time 19934.44ms, mfu 0.05% +step 500: train loss 6.4777, val loss 6.5230 +saving checkpoint to out-openwebtext_rerun_shared +iter 500: loss 6.2176, time 19883.74ms, mfu 0.05% +step 750: train loss 6.3202, val loss 6.3306 +saving checkpoint to out-openwebtext_rerun_shared +iter 750: loss 6.2960, time 19902.99ms, mfu 0.05% +step 1000: train loss 6.1727, val loss 6.1959 +saving checkpoint to out-openwebtext_rerun_shared +iter 1000: loss 6.6711, time 19912.21ms, mfu 0.05% +step 1250: train loss 6.0502, val loss 6.0823 +saving checkpoint to out-openwebtext_rerun_shared +iter 1250: loss 5.4967, time 20353.14ms, mfu 0.05% +step 1500: train loss 5.9288, val loss 5.9755 +saving checkpoint to out-openwebtext_rerun_shared +iter 1500: loss 6.2423, time 20462.83ms, mfu 0.05% +step 1750: train loss 5.9001, val loss 5.9072 +saving checkpoint to out-openwebtext_rerun_shared +iter 1750: loss 5.2559, time 20107.79ms, mfu 0.05% +step 2000: train loss 5.7827, val loss 5.7875 +saving checkpoint to out-openwebtext_rerun_shared +iter 2000: loss 5.9367, time 19881.26ms, mfu 0.05% +step 2250: train loss 5.6644, val loss 5.6749 +saving checkpoint to out-openwebtext_rerun_shared +iter 2250: loss 5.5695, time 19842.86ms, mfu 0.05% +step 2500: train loss 5.5532, val loss 5.5670 +saving checkpoint to out-openwebtext_rerun_shared +iter 2500: loss 5.8322, time 19869.30ms, mfu 0.05% +step 2750: train loss 5.5186, val loss 5.5020 +saving checkpoint to out-openwebtext_rerun_shared +iter 2750: loss 5.0819, time 19895.14ms, mfu 0.05% +step 3000: train loss 5.4030, val loss 5.4490 +saving checkpoint to out-openwebtext_rerun_shared +iter 3000: loss 5.5632, time 19904.32ms, mfu 0.05% +step 3250: train loss 5.3927, val loss 5.4123 +saving checkpoint to out-openwebtext_rerun_shared +iter 3250: loss 5.1623, time 19876.60ms, mfu 0.05% +step 3500: train loss 5.3078, val loss 5.3068 +saving checkpoint to out-openwebtext_rerun_shared +iter 3500: loss 5.4916, time 19833.63ms, mfu 0.05% +step 3750: train loss 5.2631, val loss 5.2906 +saving checkpoint to out-openwebtext_rerun_shared +iter 3750: loss 5.0661, time 19848.73ms, mfu 0.05% +step 4000: train loss 5.1906, val loss 5.1600 +saving checkpoint to out-openwebtext_rerun_shared +iter 4000: loss 5.1939, time 19866.61ms, mfu 0.05% +step 4250: train loss 5.1404, val loss 5.1263 +saving checkpoint to out-openwebtext_rerun_shared +iter 4250: loss 5.3693, time 20274.61ms, mfu 0.05% +step 4500: train loss 5.0661, val loss 5.0742 +saving checkpoint to out-openwebtext_rerun_shared +iter 4500: loss 5.0780, time 19849.42ms, mfu 0.05% +step 4750: train loss 5.0389, val loss 5.0468 +saving checkpoint to out-openwebtext_rerun_shared +iter 4750: loss 5.3133, time 20030.60ms, mfu 0.05% +step 5000: train loss 5.0141, val loss 4.9942 +saving checkpoint to out-openwebtext_rerun_shared +iter 5000: loss 5.2235, time 19803.22ms, mfu 0.05% +step 5250: train loss 4.9319, val loss 4.9673 +saving checkpoint to out-openwebtext_rerun_shared +iter 5250: loss 5.0381, time 19834.39ms, mfu 0.05% +step 5500: train loss 4.8865, val loss 4.8746 +saving checkpoint to out-openwebtext_rerun_shared +iter 5500: loss 4.7281, time 19848.01ms, mfu 0.05% +step 5750: train loss 4.8231, val loss 4.8450 +saving checkpoint to out-openwebtext_rerun_shared +iter 5750: loss 4.8421, time 19793.79ms, mfu 0.05% +step 6000: train loss 4.8229, val loss 4.8227 +saving checkpoint to out-openwebtext_rerun_shared +iter 6000: loss 4.4900, time 19799.87ms, mfu 0.05% +step 6250: train loss 4.7699, val loss 4.7595 +saving checkpoint to out-openwebtext_rerun_shared +iter 6250: loss 4.7564, time 19758.58ms, mfu 0.05% +step 6500: train loss 4.7190, val loss 4.7399 +saving checkpoint to out-openwebtext_rerun_shared +iter 6500: loss 4.8135, time 19867.88ms, mfu 0.05% +step 6750: train loss 4.6664, val loss 4.7110 +saving checkpoint to out-openwebtext_rerun_shared +iter 6750: loss 4.9939, time 19912.84ms, mfu 0.05% +step 7000: train loss 4.6534, val loss 4.6714 +saving checkpoint to out-openwebtext_rerun_shared +iter 7000: loss 4.3706, time 19815.23ms, mfu 0.05% +step 7250: train loss 4.5822, val loss 4.6491 +saving checkpoint to out-openwebtext_rerun_shared +iter 7250: loss 4.1715, time 19862.83ms, mfu 0.05% +step 7500: train loss 4.5747, val loss 4.5730 +saving checkpoint to out-openwebtext_rerun_shared +iter 7500: loss 4.4988, time 19853.09ms, mfu 0.05% +step 7750: train loss 4.5482, val loss 4.5878 +iter 7750: loss 4.8376, time 18377.82ms, mfu 0.05% +step 8000: train loss 4.5411, val loss 4.5516 +saving checkpoint to out-openwebtext_rerun_shared +iter 8000: loss 4.2756, time 19808.60ms, mfu 0.05% +step 8250: train loss 4.4973, val loss 4.5216 +saving checkpoint to out-openwebtext_rerun_shared +iter 8250: loss 4.7416, time 19900.62ms, mfu 0.05% +step 8500: train loss 4.5016, val loss 4.5138 +saving checkpoint to out-openwebtext_rerun_shared +iter 8500: loss 4.5660, time 20017.05ms, mfu 0.05% +step 8750: train loss 4.4868, val loss 4.5198 +iter 8750: loss 4.5555, time 18422.92ms, mfu 0.05% +step 9000: train loss 4.4641, val loss 4.4735 +saving checkpoint to out-openwebtext_rerun_shared +iter 9000: loss 4.4388, time 20050.18ms, mfu 0.05% +step 9250: train loss 4.4498, val loss 4.4738 +iter 9250: loss 4.5534, time 18439.13ms, mfu 0.05% +step 9500: train loss 4.4749, val loss 4.4447 +saving checkpoint to out-openwebtext_rerun_shared +iter 9500: loss 4.9183, time 19857.28ms, mfu 0.05% +step 9750: train loss 4.4216, val loss 4.4614 +iter 9750: loss 4.2104, time 18412.46ms, mfu 0.05% +step 10000: train loss 4.4299, val loss 4.4204 +saving checkpoint to out-openwebtext_rerun_shared +iter 10000: loss 4.5177, time 19926.80ms, mfu 0.05% + +--> RESULT | x_headshared | seed=42 | BEST=4.4204 | FINAL=4.4204 | ✓ ref=4.5863 delta=-0.1659 + + +==================================================================================================== +RUNNING: x_headshared | warmup=2000 | seed=1337 + head-shared X gate Linear(64->64) on x + NO CHECKPOINT SAVING — disk safe ✓ + Reference: 4.5863 ±0.0116 (DIRTY 3060) +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.64M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 124,010,496 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9310, val loss 10.9306 +iter 0: loss 10.9358, time 18576.77ms, mfu -100.00% +step 250: train loss 6.9042, val loss 6.9312 +saving checkpoint to out-openwebtext_rerun_shared +iter 250: loss 6.7944, time 19870.73ms, mfu 0.05% +step 500: train loss 6.4894, val loss 6.4932 +saving checkpoint to out-openwebtext_rerun_shared +iter 500: loss 6.6432, time 19802.99ms, mfu 0.05% +step 750: train loss 6.3007, val loss 6.3279 +saving checkpoint to out-openwebtext_rerun_shared +iter 750: loss 6.0934, time 19875.67ms, mfu 0.05% +step 1000: train loss 6.1596, val loss 6.1465 +saving checkpoint to out-openwebtext_rerun_shared +iter 1000: loss 6.0731, time 19921.11ms, mfu 0.05% +step 1250: train loss 6.0660, val loss 6.0354 +saving checkpoint to out-openwebtext_rerun_shared +iter 1250: loss 5.6900, time 19883.14ms, mfu 0.05% +step 1500: train loss 5.9485, val loss 5.9744 +saving checkpoint to out-openwebtext_rerun_shared +iter 1500: loss 6.2860, time 19652.32ms, mfu 0.05% +step 1750: train loss 5.8465, val loss 5.8606 +saving checkpoint to out-openwebtext_rerun_shared +iter 1750: loss 5.9694, time 19963.84ms, mfu 0.05% +step 2000: train loss 5.8157, val loss 5.7944 +saving checkpoint to out-openwebtext_rerun_shared +iter 2000: loss 5.8263, time 19983.62ms, mfu 0.05% +step 2250: train loss 5.6981, val loss 5.6443 +saving checkpoint to out-openwebtext_rerun_shared +iter 2250: loss 6.0843, time 19959.93ms, mfu 0.05% +step 2500: train loss 5.6294, val loss 5.5959 +saving checkpoint to out-openwebtext_rerun_shared +iter 2500: loss 5.1890, time 19902.76ms, mfu 0.05% +step 2750: train loss 5.4973, val loss 5.5124 +saving checkpoint to out-openwebtext_rerun_shared +iter 2750: loss 5.8531, time 20019.16ms, mfu 0.05% +step 3000: train loss 5.4136, val loss 5.4306 +saving checkpoint to out-openwebtext_rerun_shared +iter 3000: loss 5.1328, time 19885.08ms, mfu 0.05% +step 3250: train loss 5.3882, val loss 5.3949 +saving checkpoint to out-openwebtext_rerun_shared +iter 3250: loss 5.0801, time 19943.02ms, mfu 0.05% +step 3500: train loss 5.3263, val loss 5.3078 +saving checkpoint to out-openwebtext_rerun_shared +iter 3500: loss 5.4370, time 19941.91ms, mfu 0.05% +step 3750: train loss 5.2633, val loss 5.2769 +saving checkpoint to out-openwebtext_rerun_shared +iter 3750: loss 5.3708, time 19860.30ms, mfu 0.05% +step 4000: train loss 5.1917, val loss 5.2173 +saving checkpoint to out-openwebtext_rerun_shared +iter 4000: loss 5.0674, time 19910.44ms, mfu 0.05% +step 4250: train loss 5.1803, val loss 5.1808 +saving checkpoint to out-openwebtext_rerun_shared +iter 4250: loss 4.9804, time 20044.11ms, mfu 0.05% +step 4500: train loss 5.1002, val loss 5.1336 +saving checkpoint to out-openwebtext_rerun_shared +iter 4500: loss 5.6366, time 20555.90ms, mfu 0.05% +step 4750: train loss 5.0319, val loss 5.0696 +saving checkpoint to out-openwebtext_rerun_shared +iter 4750: loss 5.4312, time 20774.37ms, mfu 0.05% +step 5000: train loss 5.0069, val loss 5.0190 +saving checkpoint to out-openwebtext_rerun_shared +iter 5000: loss 4.9321, time 19711.72ms, mfu 0.05% +step 5250: train loss 4.9627, val loss 4.9532 +saving checkpoint to out-openwebtext_rerun_shared +iter 5250: loss 4.6505, time 19652.65ms, mfu 0.05% +step 5500: train loss 4.9114, val loss 4.9115 +saving checkpoint to out-openwebtext_rerun_shared +iter 5500: loss 4.8360, time 19878.56ms, mfu 0.05% +step 5750: train loss 4.8803, val loss 4.8925 +saving checkpoint to out-openwebtext_rerun_shared +iter 5750: loss 4.7528, time 19889.64ms, mfu 0.05% +step 6000: train loss 4.8027, val loss 4.8199 +saving checkpoint to out-openwebtext_rerun_shared +iter 6000: loss 4.9540, time 19761.38ms, mfu 0.05% +step 6250: train loss 4.7850, val loss 4.7989 +saving checkpoint to out-openwebtext_rerun_shared +iter 6250: loss 4.6306, time 19862.99ms, mfu 0.05% +step 6500: train loss 4.7253, val loss 4.7613 +saving checkpoint to out-openwebtext_rerun_shared +iter 6500: loss 4.6999, time 19930.18ms, mfu 0.05% +step 6750: train loss 4.6828, val loss 4.7092 +saving checkpoint to out-openwebtext_rerun_shared +iter 6750: loss 4.8369, time 19863.93ms, mfu 0.05% +step 7000: train loss 4.6674, val loss 4.6173 +saving checkpoint to out-openwebtext_rerun_shared +iter 7000: loss 4.1729, time 19807.55ms, mfu 0.05% +step 7250: train loss 4.5927, val loss 4.6594 +iter 7250: loss 4.3893, time 18431.31ms, mfu 0.05% +step 7500: train loss 4.5908, val loss 4.6060 +saving checkpoint to out-openwebtext_rerun_shared +iter 7500: loss 4.9469, time 19825.58ms, mfu 0.05% +step 7750: train loss 4.5817, val loss 4.5554 +saving checkpoint to out-openwebtext_rerun_shared +iter 7750: loss 4.9583, time 19683.09ms, mfu 0.05% +step 8000: train loss 4.5782, val loss 4.5606 +iter 8000: loss 4.4582, time 18164.08ms, mfu 0.05% +step 8250: train loss 4.5344, val loss 4.5147 +saving checkpoint to out-openwebtext_rerun_shared +iter 8250: loss 4.6526, time 19868.51ms, mfu 0.05% +step 8500: train loss 4.4960, val loss 4.4887 +saving checkpoint to out-openwebtext_rerun_shared +iter 8500: loss 4.2315, time 19913.07ms, mfu 0.05% +step 8750: train loss 4.5196, val loss 4.5344 +iter 8750: loss 4.4427, time 18414.06ms, mfu 0.05% +step 9000: train loss 4.4762, val loss 4.5232 +iter 9000: loss 4.7582, time 18149.48ms, mfu 0.05% +step 9250: train loss 4.4730, val loss 4.4475 +saving checkpoint to out-openwebtext_rerun_shared +iter 9250: loss 4.4652, time 19859.93ms, mfu 0.05% +step 9500: train loss 4.4701, val loss 4.4623 +iter 9500: loss 4.5340, time 18416.39ms, mfu 0.05% +step 9750: train loss 4.4377, val loss 4.4605 +iter 9750: loss 4.5111, time 18451.27ms, mfu 0.05% +step 10000: train loss 4.4256, val loss 4.4468 +saving checkpoint to out-openwebtext_rerun_shared +iter 10000: loss 4.6838, time 20161.55ms, mfu 0.05% + +--> RESULT | x_headshared | seed=1337 | BEST=4.4468 | FINAL=4.4468 | ✓ ref=4.5863 delta=-0.1395 + + +x_headshared running mean (2/3 seeds): 4.4336 ±0.0132 ref=4.5863 + + +==================================================================================================== +RUNNING: x_headshared | warmup=2000 | seed=123 + head-shared X gate Linear(64->64) on x + NO CHECKPOINT SAVING — disk safe ✓ + Reference: 4.5863 ±0.0116 (DIRTY 3060) +==================================================================================================== + +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.64M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 124,010,496 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9091, val loss 10.9097 +iter 0: loss 10.9225, time 18555.82ms, mfu -100.00% +step 250: train loss 6.9509, val loss 6.9769 +saving checkpoint to out-openwebtext_rerun_shared +iter 250: loss 7.0434, time 19910.07ms, mfu 0.05% +step 500: train loss 6.5218, val loss 6.5130 +saving checkpoint to out-openwebtext_rerun_shared +iter 500: loss 6.2355, time 19926.91ms, mfu 0.05% +step 750: train loss 6.3291, val loss 6.3567 +saving checkpoint to out-openwebtext_rerun_shared +iter 750: loss 5.8425, time 19862.82ms, mfu 0.05% +step 1000: train loss 6.1730, val loss 6.1793 +saving checkpoint to out-openwebtext_rerun_shared +iter 1000: loss 6.1374, time 19892.18ms, mfu 0.05% +step 1250: train loss 6.0575, val loss 6.0566 +saving checkpoint to out-openwebtext_rerun_shared +iter 1250: loss 6.1772, time 19871.34ms, mfu 0.05% +step 1500: train loss 5.9688, val loss 5.9663 +saving checkpoint to out-openwebtext_rerun_shared +iter 1500: loss 6.2928, time 19885.17ms, mfu 0.05% +step 1750: train loss 5.8836, val loss 5.8921 +saving checkpoint to out-openwebtext_rerun_shared +iter 1750: loss 5.6337, time 19862.16ms, mfu 0.05% +step 2000: train loss 5.7926, val loss 5.7870 +saving checkpoint to out-openwebtext_rerun_shared +iter 2000: loss 5.8497, time 19937.52ms, mfu 0.05% +step 2250: train loss 5.6862, val loss 5.6980 +saving checkpoint to out-openwebtext_rerun_shared +iter 2250: loss 5.8601, time 20193.99ms, mfu 0.05% +step 2500: train loss 5.6427, val loss 5.6243 +saving checkpoint to out-openwebtext_rerun_shared +iter 2500: loss 5.5160, time 19853.37ms, mfu 0.05% +step 2750: train loss 5.4920, val loss 5.5270 +saving checkpoint to out-openwebtext_rerun_shared +iter 2750: loss 5.1896, time 19868.91ms, mfu 0.05% +step 3000: train loss 5.4307, val loss 5.4181 +saving checkpoint to out-openwebtext_rerun_shared +iter 3000: loss 5.5906, time 19748.70ms, mfu 0.05% +step 3250: train loss 5.3533, val loss 5.3793 +saving checkpoint to out-openwebtext_rerun_shared +iter 3250: loss 5.3563, time 19873.90ms, mfu 0.05% +step 3500: train loss 5.3421, val loss 5.3323 +saving checkpoint to out-openwebtext_rerun_shared +iter 3500: loss 4.9575, time 19829.25ms, mfu 0.05% +step 3750: train loss 5.2816, val loss 5.2898 +saving checkpoint to out-openwebtext_rerun_shared +iter 3750: loss 5.5662, time 19850.87ms, mfu 0.05% +step 4000: train loss 5.1933, val loss 5.2153 +saving checkpoint to out-openwebtext_rerun_shared +iter 4000: loss 5.0788, time 19873.95ms, mfu 0.05% +step 4250: train loss 5.1517, val loss 5.1472 +saving checkpoint to out-openwebtext_rerun_shared +iter 4250: loss 5.1802, time 19799.85ms, mfu 0.05% +step 4500: train loss 5.0782, val loss 5.0826 +saving checkpoint to out-openwebtext_rerun_shared +iter 4500: loss 4.5145, time 20087.26ms, mfu 0.05% +step 4750: train loss 5.0454, val loss 5.0600 +saving checkpoint to out-openwebtext_rerun_shared +iter 4750: loss 4.8549, time 19941.46ms, mfu 0.05% +step 5000: train loss 5.0201, val loss 5.0041 +saving checkpoint to out-openwebtext_rerun_shared +iter 5000: loss 5.3351, time 19833.24ms, mfu 0.05% +step 5250: train loss 4.9594, val loss 4.9545 +saving checkpoint to out-openwebtext_rerun_shared +iter 5250: loss 4.9022, time 19818.52ms, mfu 0.05% +step 5500: train loss 4.9164, val loss 4.9279 +saving checkpoint to out-openwebtext_rerun_shared +iter 5500: loss 4.7828, time 19793.33ms, mfu 0.05% +step 5750: train loss 4.8561, val loss 4.8595 +saving checkpoint to out-openwebtext_rerun_shared +iter 5750: loss 5.6779, time 19840.76ms, mfu 0.05% +step 6000: train loss 4.8045, val loss 4.8156 +saving checkpoint to out-openwebtext_rerun_shared +iter 6000: loss 4.6865, time 19904.89ms, mfu 0.05% +step 6250: train loss 4.7694, val loss 4.7462 +saving checkpoint to out-openwebtext_rerun_shared +iter 6250: loss 5.0063, time 19943.38ms, mfu 0.05% +step 6500: train loss 4.7497, val loss 4.7520 +iter 6500: loss 4.6715, time 18380.00ms, mfu 0.05% +step 6750: train loss 4.6444, val loss 4.7219 +saving checkpoint to out-openwebtext_rerun_shared +iter 6750: loss 4.5487, time 21009.01ms, mfu 0.05% +step 7000: train loss 4.6407, val loss 4.6843 +saving checkpoint to out-openwebtext_rerun_shared +iter 7000: loss 5.0948, time 22166.25ms, mfu 0.05% +step 7250: train loss 4.6538, val loss 4.6577 +saving checkpoint to out-openwebtext_rerun_shared +iter 7250: loss 4.6525, time 20579.36ms, mfu 0.05% +step 7500: train loss 4.5842, val loss 4.5959 +saving checkpoint to out-openwebtext_rerun_shared +iter 7500: loss 5.0131, time 19803.40ms, mfu 0.05% +step 7750: train loss 4.5739, val loss 4.5874 +saving checkpoint to out-openwebtext_rerun_shared +iter 7750: loss 4.7534, time 19826.64ms, mfu 0.05% +step 8000: train loss 4.5363, val loss 4.5499 +saving checkpoint to out-openwebtext_rerun_shared +iter 8000: loss 4.3646, time 19813.23ms, mfu 0.05% +step 8250: train loss 4.5441, val loss 4.5359 +saving checkpoint to out-openwebtext_rerun_shared +iter 8250: loss 4.5213, time 19830.48ms, mfu 0.05% +step 8500: train loss 4.4933, val loss 4.4898 +saving checkpoint to out-openwebtext_rerun_shared +iter 8500: loss 4.5042, time 19837.09ms, mfu 0.05% +step 8750: train loss 4.4904, val loss 4.5055 +iter 8750: loss 4.2771, time 18337.57ms, mfu 0.05% +step 9000: train loss 4.4868, val loss 4.4916 +iter 9000: loss 4.5701, time 18329.49ms, mfu 0.05% +step 9250: train loss 4.4956, val loss 4.4543 +saving checkpoint to out-openwebtext_rerun_shared +iter 9250: loss 4.6956, time 19815.09ms, mfu 0.05% +step 9500: train loss 4.4703, val loss 4.4482 +saving checkpoint to out-openwebtext_rerun_shared +iter 9500: loss 4.5620, time 19919.09ms, mfu 0.05% +step 9750: train loss 4.4460, val loss 4.4679 +iter 9750: loss 4.1919, time 18374.70ms, mfu 0.05% +step 10000: train loss 4.4387, val loss 4.4367 +saving checkpoint to out-openwebtext_rerun_shared +iter 10000: loss 4.1024, time 19858.77ms, mfu 0.05% + +--> RESULT | x_headshared | seed=123 | BEST=4.4367 | FINAL=4.4367 | ✓ ref=4.5863 delta=-0.1496 + + +x_headshared running mean (3/3 seeds): 4.4346 ±0.0109 ref=4.5863 + + +==================================================================================================== +PRIORITY 2 RERUN COMPLETE +==================================================================================================== +Results CSV: p2_rerun_runs_20260323_1714.csv +Summary: p2_rerun_summary_20260323_1714.txt + +Paste summary here — then open fresh window with bombass_summary.md \ No newline at end of file diff --git a/results/logs/full_x_seed123_20260324_1712.log b/results/logs/full_x_seed123_20260324_1712.log new file mode 100644 index 0000000000000000000000000000000000000000..3fbd5fdbe988c68569790789506a4241f2a9c11b --- /dev/null +++ b/results/logs/full_x_seed123_20260324_1712.log @@ -0,0 +1,125 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 130.67M +C:\Users\brian\documents\nanogpt\train.py:199: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 131,039,232 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9733, val loss 10.9748 +iter 0: loss 10.9713, time 19254.34ms, mfu -100.00% +step 250: train loss 6.9195, val loss 6.9534 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 250: loss 7.0447, time 21175.66ms, mfu 0.05% +step 500: train loss 6.4888, val loss 6.5104 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 500: loss 6.5277, time 21005.39ms, mfu 0.05% +step 750: train loss 6.3046, val loss 6.3482 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 750: loss 6.5066, time 20700.36ms, mfu 0.05% +step 1000: train loss 6.1439, val loss 6.1735 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 1000: loss 6.0694, time 20874.44ms, mfu 0.05% +step 1250: train loss 6.0358, val loss 6.0085 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 1250: loss 5.6733, time 21712.67ms, mfu 0.05% +step 1500: train loss 5.9323, val loss 5.9638 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 1500: loss 6.0395, time 20801.75ms, mfu 0.05% +step 1750: train loss 5.8431, val loss 5.7927 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 1750: loss 5.6760, time 21139.75ms, mfu 0.05% +step 2000: train loss 5.7815, val loss 5.7700 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 2000: loss 5.6708, time 21171.30ms, mfu 0.05% +step 2250: train loss 5.6081, val loss 5.6363 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 2250: loss 5.4355, time 22024.32ms, mfu 0.05% +step 2500: train loss 5.5326, val loss 5.5446 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 2500: loss 5.4071, time 21466.90ms, mfu 0.05% +step 2750: train loss 5.4378, val loss 5.4487 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 2750: loss 5.2331, time 21284.63ms, mfu 0.05% +step 3000: train loss 5.3923, val loss 5.3970 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 3000: loss 5.1448, time 21199.33ms, mfu 0.05% +step 3250: train loss 5.3241, val loss 5.3256 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 3250: loss 4.9374, time 20759.96ms, mfu 0.05% +step 3500: train loss 5.2610, val loss 5.2920 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 3500: loss 5.2663, time 21994.24ms, mfu 0.05% +step 3750: train loss 5.2499, val loss 5.2205 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 3750: loss 5.0847, time 21110.11ms, mfu 0.05% +step 4000: train loss 5.1691, val loss 5.1677 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 4000: loss 5.4281, time 20660.29ms, mfu 0.05% +step 4250: train loss 5.1015, val loss 5.1165 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 4250: loss 5.2154, time 21354.28ms, mfu 0.05% +step 4500: train loss 5.0599, val loss 5.0493 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 4500: loss 5.0235, time 21094.51ms, mfu 0.05% +step 4750: train loss 4.9772, val loss 4.9851 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 4750: loss 5.0774, time 21087.65ms, mfu 0.05% +step 5000: train loss 4.9098, val loss 4.9407 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 5000: loss 5.0672, time 21395.85ms, mfu 0.05% +step 5250: train loss 4.8824, val loss 4.9154 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 5250: loss 5.0131, time 20580.24ms, mfu 0.05% +step 5500: train loss 4.8239, val loss 4.8101 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 5500: loss 4.8037, time 20772.13ms, mfu 0.05% +step 5750: train loss 4.7771, val loss 4.8149 +iter 5750: loss 5.2921, time 18912.89ms, mfu 0.05% +step 6000: train loss 4.7333, val loss 4.7464 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 6000: loss 5.0554, time 20848.71ms, mfu 0.05% +step 6250: train loss 4.6984, val loss 4.7246 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 6250: loss 4.7551, time 20929.54ms, mfu 0.05% +step 6500: train loss 4.6629, val loss 4.6574 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 6500: loss 4.4985, time 20656.72ms, mfu 0.05% +step 6750: train loss 4.6494, val loss 4.6230 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 6750: loss 4.4945, time 21004.06ms, mfu 0.05% +step 7000: train loss 4.6205, val loss 4.6289 +iter 7000: loss 4.6989, time 18926.85ms, mfu 0.05% +step 7250: train loss 4.5865, val loss 4.5812 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 7250: loss 4.6101, time 20603.85ms, mfu 0.05% +step 7500: train loss 4.5354, val loss 4.5673 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 7500: loss 4.4803, time 20822.16ms, mfu 0.05% +step 7750: train loss 4.5339, val loss 4.5501 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 7750: loss 4.4961, time 20727.30ms, mfu 0.05% +step 8000: train loss 4.5461, val loss 4.5395 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 8000: loss 4.5638, time 20562.20ms, mfu 0.05% +step 8250: train loss 4.4518, val loss 4.4885 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 8250: loss 4.4909, time 20623.02ms, mfu 0.05% +step 8500: train loss 4.4915, val loss 4.4837 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 8500: loss 4.5280, time 20851.26ms, mfu 0.05% +step 8750: train loss 4.4334, val loss 4.4955 +iter 8750: loss 4.6849, time 18920.30ms, mfu 0.05% +step 9000: train loss 4.4462, val loss 4.4510 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 9000: loss 4.2613, time 20546.29ms, mfu 0.05% +step 9250: train loss 4.4301, val loss 4.4293 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 9250: loss 4.2568, time 20760.32ms, mfu 0.05% +step 9500: train loss 4.4195, val loss 4.4112 +saving checkpoint to out-openwebtext_full_x_seed123 +iter 9500: loss 4.4223, time 20893.73ms, mfu 0.05% +step 9750: train loss 4.4064, val loss 4.4263 +iter 9750: loss 4.4524, time 19113.45ms, mfu 0.05% +step 10000: train loss 4.4426, val loss 4.4145 +iter 10000: loss 4.5798, time 19172.38ms, mfu 0.05% diff --git a/results/logs/train_baseline_w500_seed123.log b/results/logs/train_baseline_w500_seed123.log new file mode 100644 index 0000000000000000000000000000000000000000..ef2d0595c1924ca02234aba3c2224fea4230a12c --- /dev/null +++ b/results/logs/train_baseline_w500_seed123.log @@ -0,0 +1,126 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:200: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9288, val loss 10.9279 +iter 0: loss 10.9503, time 17611.94ms, mfu -100.00% +step 250: train loss 6.6933, val loss 6.6322 +saving checkpoint to out-openwebtext_warmup500_shared +iter 250: loss 6.4180, time 19375.30ms, mfu 0.05% +step 500: train loss 6.4072, val loss 6.4822 +saving checkpoint to out-openwebtext_warmup500_shared +iter 500: loss 6.4993, time 19560.06ms, mfu 0.05% +step 750: train loss 6.2324, val loss 6.2362 +saving checkpoint to out-openwebtext_warmup500_shared +iter 750: loss 5.5699, time 19485.45ms, mfu 0.05% +step 1000: train loss 6.1325, val loss 6.1269 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1000: loss 6.4573, time 19013.20ms, mfu 0.05% +step 1250: train loss 5.9485, val loss 5.9749 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1250: loss 5.6823, time 18938.62ms, mfu 0.05% +step 1500: train loss 5.8680, val loss 5.8361 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1500: loss 5.6763, time 19273.22ms, mfu 0.05% +step 1750: train loss 5.7441, val loss 5.7831 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1750: loss 6.3962, time 19231.46ms, mfu 0.05% +step 2000: train loss 5.6740, val loss 5.7084 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2000: loss 5.5998, time 19183.60ms, mfu 0.05% +step 2250: train loss 5.5757, val loss 5.6314 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2250: loss 6.0459, time 19694.06ms, mfu 0.05% +step 2500: train loss 5.5578, val loss 5.5592 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2500: loss 5.7746, time 19699.64ms, mfu 0.05% +step 2750: train loss 5.5184, val loss 5.4987 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2750: loss 6.0477, time 19019.01ms, mfu 0.05% +step 3000: train loss 5.4524, val loss 5.4455 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3000: loss 6.1303, time 19469.66ms, mfu 0.05% +step 3250: train loss 5.3656, val loss 5.3709 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3250: loss 5.3393, time 19973.71ms, mfu 0.05% +step 3500: train loss 5.3081, val loss 5.3547 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3500: loss 5.3478, time 19235.88ms, mfu 0.05% +step 3750: train loss 5.2857, val loss 5.2760 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3750: loss 5.0634, time 19228.70ms, mfu 0.05% +step 4000: train loss 5.2357, val loss 5.2514 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4000: loss 5.1545, time 19534.08ms, mfu 0.05% +step 4250: train loss 5.1996, val loss 5.2112 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4250: loss 5.2645, time 19130.38ms, mfu 0.05% +step 4500: train loss 5.1259, val loss 5.1321 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4500: loss 5.2162, time 19222.01ms, mfu 0.05% +step 4750: train loss 5.1096, val loss 5.1084 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4750: loss 5.0418, time 19045.00ms, mfu 0.05% +step 5000: train loss 5.0238, val loss 5.0268 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5000: loss 5.1290, time 19084.20ms, mfu 0.05% +step 5250: train loss 5.0045, val loss 4.9990 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5250: loss 5.1707, time 18698.63ms, mfu 0.05% +step 5500: train loss 4.9228, val loss 4.9645 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5500: loss 4.8808, time 18918.89ms, mfu 0.05% +step 5750: train loss 4.9460, val loss 4.9325 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5750: loss 5.2539, time 18767.79ms, mfu 0.05% +step 6000: train loss 4.9020, val loss 4.8717 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6000: loss 4.9175, time 18759.93ms, mfu 0.05% +step 6250: train loss 4.8323, val loss 4.8821 +iter 6250: loss 5.0751, time 17346.24ms, mfu 0.06% +step 6500: train loss 4.8032, val loss 4.8106 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6500: loss 4.6804, time 18970.63ms, mfu 0.06% +step 6750: train loss 4.7698, val loss 4.7932 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6750: loss 4.5265, time 19170.77ms, mfu 0.06% +step 7000: train loss 4.7405, val loss 4.7499 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7000: loss 4.6638, time 18810.65ms, mfu 0.06% +step 7250: train loss 4.7166, val loss 4.7427 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7250: loss 4.3998, time 19385.73ms, mfu 0.06% +step 7500: train loss 4.7299, val loss 4.7127 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7500: loss 4.6736, time 18977.30ms, mfu 0.06% +step 7750: train loss 4.6881, val loss 4.6832 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7750: loss 4.8593, time 18718.01ms, mfu 0.06% +step 8000: train loss 4.6647, val loss 4.6595 +saving checkpoint to out-openwebtext_warmup500_shared +iter 8000: loss 4.2191, time 18711.75ms, mfu 0.06% +step 8250: train loss 4.6329, val loss 4.6282 +saving checkpoint to out-openwebtext_warmup500_shared +iter 8250: loss 4.4576, time 20287.85ms, mfu 0.05% +step 8500: train loss 4.6177, val loss 4.6408 +iter 8500: loss 4.4438, time 17216.55ms, mfu 0.06% +step 8750: train loss 4.5959, val loss 4.6108 +saving checkpoint to out-openwebtext_warmup500_shared +iter 8750: loss 4.4521, time 19160.66ms, mfu 0.06% +step 9000: train loss 4.6151, val loss 4.5951 +saving checkpoint to out-openwebtext_warmup500_shared +iter 9000: loss 4.3484, time 18803.35ms, mfu 0.06% +step 9250: train loss 4.5655, val loss 4.5910 +saving checkpoint to out-openwebtext_warmup500_shared +iter 9250: loss 5.1633, time 19237.33ms, mfu 0.06% +step 9500: train loss 4.5638, val loss 4.5611 +saving checkpoint to out-openwebtext_warmup500_shared +iter 9500: loss 4.9816, time 19196.05ms, mfu 0.06% +step 9750: train loss 4.5866, val loss 4.6063 +iter 9750: loss 4.5079, time 17201.17ms, mfu 0.06% +step 10000: train loss 4.5739, val loss 4.5797 +iter 10000: loss 4.6881, time 17146.38ms, mfu 0.06% diff --git a/results/logs/train_baseline_w500_seed1337.log b/results/logs/train_baseline_w500_seed1337.log new file mode 100644 index 0000000000000000000000000000000000000000..5c0d41abb0d5aed6d9bec52e1136f166e26840d0 --- /dev/null +++ b/results/logs/train_baseline_w500_seed1337.log @@ -0,0 +1,126 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:200: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9989, val loss 10.9985 +iter 0: loss 11.0107, time 17652.37ms, mfu -100.00% +step 250: train loss 6.6653, val loss 6.6993 +saving checkpoint to out-openwebtext_warmup500_shared +iter 250: loss 6.4793, time 20432.92ms, mfu 0.05% +step 500: train loss 6.3895, val loss 6.4551 +saving checkpoint to out-openwebtext_warmup500_shared +iter 500: loss 6.1601, time 20355.42ms, mfu 0.05% +step 750: train loss 6.2236, val loss 6.2424 +saving checkpoint to out-openwebtext_warmup500_shared +iter 750: loss 6.0347, time 20327.26ms, mfu 0.05% +step 1000: train loss 6.1028, val loss 6.0774 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1000: loss 6.0589, time 20231.53ms, mfu 0.05% +step 1250: train loss 5.9461, val loss 5.9357 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1250: loss 5.9933, time 20449.05ms, mfu 0.05% +step 1500: train loss 5.8639, val loss 5.7867 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1500: loss 5.5845, time 20224.70ms, mfu 0.05% +step 1750: train loss 5.7859, val loss 5.7511 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1750: loss 5.9250, time 20908.79ms, mfu 0.05% +step 2000: train loss 5.7092, val loss 5.6832 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2000: loss 5.7289, time 20250.34ms, mfu 0.05% +step 2250: train loss 5.6158, val loss 5.6184 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2250: loss 5.8555, time 20364.28ms, mfu 0.05% +step 2500: train loss 5.5590, val loss 5.5414 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2500: loss 5.9874, time 20320.88ms, mfu 0.05% +step 2750: train loss 5.4950, val loss 5.4945 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2750: loss 5.4861, time 20171.81ms, mfu 0.05% +step 3000: train loss 5.4373, val loss 5.4515 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3000: loss 5.5295, time 20366.24ms, mfu 0.05% +step 3250: train loss 5.3601, val loss 5.3681 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3250: loss 5.3281, time 20421.34ms, mfu 0.05% +step 3500: train loss 5.3706, val loss 5.3329 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3500: loss 5.2516, time 20281.46ms, mfu 0.05% +step 3750: train loss 5.2896, val loss 5.2795 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3750: loss 5.5941, time 18727.58ms, mfu 0.05% +step 4000: train loss 5.2384, val loss 5.2475 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4000: loss 5.3152, time 19416.14ms, mfu 0.05% +step 4250: train loss 5.1860, val loss 5.2022 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4250: loss 4.8131, time 19452.55ms, mfu 0.05% +step 4500: train loss 5.1438, val loss 5.1401 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4500: loss 4.7466, time 18855.94ms, mfu 0.05% +step 4750: train loss 5.0717, val loss 5.0903 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4750: loss 5.1936, time 19327.07ms, mfu 0.05% +step 5000: train loss 5.0056, val loss 5.0451 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5000: loss 5.1751, time 19018.68ms, mfu 0.05% +step 5250: train loss 4.9975, val loss 4.9891 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5250: loss 4.6851, time 19322.67ms, mfu 0.05% +step 5500: train loss 4.9685, val loss 4.9426 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5500: loss 5.1831, time 18784.41ms, mfu 0.05% +step 5750: train loss 4.9294, val loss 4.8937 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5750: loss 5.1881, time 19051.61ms, mfu 0.05% +step 6000: train loss 4.8471, val loss 4.8545 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6000: loss 4.7655, time 19093.21ms, mfu 0.05% +step 6250: train loss 4.8296, val loss 4.8618 +iter 6250: loss 4.6455, time 17436.38ms, mfu 0.05% +step 6500: train loss 4.8030, val loss 4.8388 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6500: loss 4.9778, time 18801.05ms, mfu 0.05% +step 6750: train loss 4.7795, val loss 4.7830 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6750: loss 4.9318, time 18956.67ms, mfu 0.05% +step 7000: train loss 4.7509, val loss 4.7443 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7000: loss 4.6893, time 19511.26ms, mfu 0.05% +step 7250: train loss 4.7093, val loss 4.7403 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7250: loss 4.8058, time 19331.26ms, mfu 0.05% +step 7500: train loss 4.7232, val loss 4.6780 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7500: loss 4.5913, time 19245.61ms, mfu 0.05% +step 7750: train loss 4.6779, val loss 4.6734 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7750: loss 4.8722, time 19223.17ms, mfu 0.05% +step 8000: train loss 4.6434, val loss 4.6677 +saving checkpoint to out-openwebtext_warmup500_shared +iter 8000: loss 4.7233, time 19883.68ms, mfu 0.05% +step 8250: train loss 4.6262, val loss 4.6555 +saving checkpoint to out-openwebtext_warmup500_shared +iter 8250: loss 4.4135, time 19362.87ms, mfu 0.05% +step 8500: train loss 4.6292, val loss 4.6371 +saving checkpoint to out-openwebtext_warmup500_shared +iter 8500: loss 4.3582, time 19012.20ms, mfu 0.05% +step 8750: train loss 4.5930, val loss 4.5948 +saving checkpoint to out-openwebtext_warmup500_shared +iter 8750: loss 4.5661, time 19966.58ms, mfu 0.05% +step 9000: train loss 4.6080, val loss 4.6127 +iter 9000: loss 4.6713, time 17349.95ms, mfu 0.05% +step 9250: train loss 4.5706, val loss 4.6006 +iter 9250: loss 4.6763, time 17560.91ms, mfu 0.06% +step 9500: train loss 4.5767, val loss 4.5924 +saving checkpoint to out-openwebtext_warmup500_shared +iter 9500: loss 4.5896, time 19431.06ms, mfu 0.06% +step 9750: train loss 4.5820, val loss 4.5604 +saving checkpoint to out-openwebtext_warmup500_shared +iter 9750: loss 4.5296, time 19157.37ms, mfu 0.06% +step 10000: train loss 4.5752, val loss 4.6033 +iter 10000: loss 4.5815, time 17336.93ms, mfu 0.06% diff --git a/results/logs/train_baseline_w500_seed42.log b/results/logs/train_baseline_w500_seed42.log new file mode 100644 index 0000000000000000000000000000000000000000..bb952729a8db1982c2acdcda53fe68c313befcea --- /dev/null +++ b/results/logs/train_baseline_w500_seed42.log @@ -0,0 +1,126 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:200: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9665, val loss 10.9665 +iter 0: loss 10.9808, time 17549.59ms, mfu -100.00% +step 250: train loss 6.6943, val loss 6.6982 +saving checkpoint to out-openwebtext_warmup500_shared +iter 250: loss 6.8010, time 18575.29ms, mfu 0.06% +step 500: train loss 6.4147, val loss 6.4195 +saving checkpoint to out-openwebtext_warmup500_shared +iter 500: loss 6.0542, time 19211.38ms, mfu 0.06% +step 750: train loss 6.2331, val loss 6.2639 +saving checkpoint to out-openwebtext_warmup500_shared +iter 750: loss 6.5675, time 18990.73ms, mfu 0.06% +step 1000: train loss 6.1132, val loss 6.1075 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1000: loss 5.7999, time 19078.06ms, mfu 0.06% +step 1250: train loss 5.9700, val loss 5.9882 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1250: loss 6.5176, time 19244.30ms, mfu 0.06% +step 1500: train loss 5.8824, val loss 5.8821 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1500: loss 5.3886, time 19324.60ms, mfu 0.06% +step 1750: train loss 5.7704, val loss 5.7823 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1750: loss 6.0672, time 19965.75ms, mfu 0.06% +step 2000: train loss 5.7072, val loss 5.7170 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2000: loss 5.5736, time 19449.04ms, mfu 0.06% +step 2250: train loss 5.6176, val loss 5.6249 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2250: loss 5.7372, time 18962.52ms, mfu 0.06% +step 2500: train loss 5.5668, val loss 5.5683 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2500: loss 4.9305, time 20422.57ms, mfu 0.05% +step 2750: train loss 5.5078, val loss 5.5045 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2750: loss 5.5694, time 20250.29ms, mfu 0.05% +step 3000: train loss 5.4440, val loss 5.4300 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3000: loss 5.4781, time 20277.14ms, mfu 0.05% +step 3250: train loss 5.4105, val loss 5.3698 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3250: loss 5.5723, time 19924.74ms, mfu 0.05% +step 3500: train loss 5.3265, val loss 5.3767 +iter 3500: loss 5.4543, time 17440.04ms, mfu 0.05% +step 3750: train loss 5.2940, val loss 5.3152 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3750: loss 5.3425, time 20383.54ms, mfu 0.05% +step 4000: train loss 5.2492, val loss 5.2527 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4000: loss 5.9065, time 20209.41ms, mfu 0.05% +step 4250: train loss 5.2097, val loss 5.1946 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4250: loss 4.9340, time 20366.63ms, mfu 0.05% +step 4500: train loss 5.1261, val loss 5.1504 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4500: loss 5.2522, time 20342.62ms, mfu 0.05% +step 4750: train loss 5.1105, val loss 5.1170 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4750: loss 5.1851, time 20378.37ms, mfu 0.05% +step 5000: train loss 5.0615, val loss 5.0375 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5000: loss 5.3081, time 20337.28ms, mfu 0.05% +step 5250: train loss 5.0007, val loss 5.0051 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5250: loss 5.1187, time 20326.20ms, mfu 0.05% +step 5500: train loss 4.9896, val loss 4.9797 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5500: loss 4.7401, time 20350.81ms, mfu 0.05% +step 5750: train loss 4.9157, val loss 4.9299 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5750: loss 5.1292, time 20373.96ms, mfu 0.05% +step 6000: train loss 4.8692, val loss 4.8745 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6000: loss 4.8291, time 20884.22ms, mfu 0.05% +step 6250: train loss 4.8327, val loss 4.8483 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6250: loss 4.5410, time 20369.03ms, mfu 0.05% +step 6500: train loss 4.8378, val loss 4.8182 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6500: loss 4.6899, time 20257.64ms, mfu 0.05% +step 6750: train loss 4.7802, val loss 4.7709 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6750: loss 4.7397, time 20294.11ms, mfu 0.05% +step 7000: train loss 4.7352, val loss 4.7629 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7000: loss 4.6886, time 20373.70ms, mfu 0.05% +step 7250: train loss 4.7279, val loss 4.7238 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7250: loss 4.9065, time 20369.43ms, mfu 0.05% +step 7500: train loss 4.7261, val loss 4.7085 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7500: loss 4.8271, time 20404.24ms, mfu 0.05% +step 7750: train loss 4.7135, val loss 4.6891 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7750: loss 4.8001, time 20352.95ms, mfu 0.05% +step 8000: train loss 4.6693, val loss 4.6733 +saving checkpoint to out-openwebtext_warmup500_shared +iter 8000: loss 4.6379, time 20205.58ms, mfu 0.05% +step 8250: train loss 4.6306, val loss 4.6495 +saving checkpoint to out-openwebtext_warmup500_shared +iter 8250: loss 4.4571, time 20113.78ms, mfu 0.05% +step 8500: train loss 4.6441, val loss 4.6727 +iter 8500: loss 4.5185, time 17304.46ms, mfu 0.05% +step 8750: train loss 4.6008, val loss 4.6222 +saving checkpoint to out-openwebtext_warmup500_shared +iter 8750: loss 4.8055, time 20287.22ms, mfu 0.05% +step 9000: train loss 4.6163, val loss 4.6348 +iter 9000: loss 4.5752, time 17348.26ms, mfu 0.05% +step 9250: train loss 4.5952, val loss 4.5856 +saving checkpoint to out-openwebtext_warmup500_shared +iter 9250: loss 4.7088, time 20201.50ms, mfu 0.05% +step 9500: train loss 4.5657, val loss 4.5827 +saving checkpoint to out-openwebtext_warmup500_shared +iter 9500: loss 4.2960, time 20251.00ms, mfu 0.05% +step 9750: train loss 4.5635, val loss 4.5773 +saving checkpoint to out-openwebtext_warmup500_shared +iter 9750: loss 4.8632, time 20421.44ms, mfu 0.05% +step 10000: train loss 4.5614, val loss 4.5910 +iter 10000: loss 4.6172, time 17344.25ms, mfu 0.05% diff --git a/results/logs/train_bilinear_diag_w2000_seed123.log b/results/logs/train_bilinear_diag_w2000_seed123.log new file mode 100644 index 0000000000000000000000000000000000000000..64e5b504f751b6e30d435a8fd663fcf6b15c335b --- /dev/null +++ b/results/logs/train_bilinear_diag_w2000_seed123.log @@ -0,0 +1,126 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:200: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 37, with 19,968 parameters +using fused AdamW: True +step 0: train loss 10.9593, val loss 10.9584 +iter 0: loss 10.9762, time 18844.68ms, mfu -100.00% +step 250: train loss 6.9613, val loss 6.9070 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 250: loss 6.6578, time 20154.17ms, mfu 0.05% +step 500: train loss 6.4900, val loss 6.5666 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 500: loss 6.5983, time 20088.65ms, mfu 0.05% +step 750: train loss 6.3083, val loss 6.3132 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 750: loss 5.6359, time 20170.36ms, mfu 0.05% +step 1000: train loss 6.1900, val loss 6.1885 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 1000: loss 6.4922, time 20098.89ms, mfu 0.05% +step 1250: train loss 6.0102, val loss 6.0387 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 1250: loss 5.7546, time 20225.26ms, mfu 0.05% +step 1500: train loss 5.9845, val loss 5.9550 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 1500: loss 5.7949, time 20096.40ms, mfu 0.05% +step 1750: train loss 5.8646, val loss 5.9052 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 1750: loss 6.5171, time 20229.35ms, mfu 0.05% +step 2000: train loss 5.7973, val loss 5.8326 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 2000: loss 5.7210, time 19974.66ms, mfu 0.05% +step 2250: train loss 5.6468, val loss 5.7067 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 2250: loss 6.1426, time 20062.69ms, mfu 0.05% +step 2500: train loss 5.6091, val loss 5.6161 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 2500: loss 5.8414, time 20109.93ms, mfu 0.05% +step 2750: train loss 5.5572, val loss 5.5316 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 2750: loss 6.1474, time 19977.20ms, mfu 0.05% +step 3000: train loss 5.4685, val loss 5.4693 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 3000: loss 6.1973, time 20024.44ms, mfu 0.05% +step 3250: train loss 5.3707, val loss 5.3727 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 3250: loss 5.3271, time 20050.40ms, mfu 0.05% +step 3500: train loss 5.3083, val loss 5.3550 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 3500: loss 5.3500, time 20033.19ms, mfu 0.05% +step 3750: train loss 5.2759, val loss 5.2612 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 3750: loss 5.0195, time 20121.88ms, mfu 0.05% +step 4000: train loss 5.2238, val loss 5.2325 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 4000: loss 5.1434, time 20315.31ms, mfu 0.05% +step 4250: train loss 5.1886, val loss 5.2000 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 4250: loss 5.2228, time 20045.76ms, mfu 0.05% +step 4500: train loss 5.1098, val loss 5.1084 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 4500: loss 5.1721, time 20028.24ms, mfu 0.05% +step 4750: train loss 5.0950, val loss 5.0930 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 4750: loss 4.9882, time 20015.08ms, mfu 0.05% +step 5000: train loss 5.0036, val loss 4.9972 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 5000: loss 5.1236, time 20053.45ms, mfu 0.05% +step 5250: train loss 4.9957, val loss 4.9812 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 5250: loss 5.1646, time 20083.26ms, mfu 0.05% +step 5500: train loss 4.9114, val loss 4.9525 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 5500: loss 4.8223, time 20197.64ms, mfu 0.05% +step 5750: train loss 4.9381, val loss 4.9134 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 5750: loss 5.3020, time 20027.65ms, mfu 0.05% +step 6000: train loss 4.8696, val loss 4.8388 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 6000: loss 4.8796, time 20155.74ms, mfu 0.05% +step 6250: train loss 4.8004, val loss 4.8499 +iter 6250: loss 5.0689, time 18638.19ms, mfu 0.05% +step 6500: train loss 4.7615, val loss 4.7700 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 6500: loss 4.6697, time 20065.67ms, mfu 0.05% +step 6750: train loss 4.7161, val loss 4.7372 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 6750: loss 4.5692, time 20066.52ms, mfu 0.05% +step 7000: train loss 4.6800, val loss 4.6892 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 7000: loss 4.6276, time 20060.70ms, mfu 0.05% +step 7250: train loss 4.6546, val loss 4.6817 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 7250: loss 4.2934, time 20124.63ms, mfu 0.05% +step 7500: train loss 4.6593, val loss 4.6411 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 7500: loss 4.6086, time 20100.33ms, mfu 0.05% +step 7750: train loss 4.6121, val loss 4.6088 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 7750: loss 4.7819, time 20042.09ms, mfu 0.05% +step 8000: train loss 4.5858, val loss 4.5788 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 8000: loss 4.1795, time 20043.60ms, mfu 0.05% +step 8250: train loss 4.5423, val loss 4.5382 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 8250: loss 4.3805, time 20014.61ms, mfu 0.05% +step 8500: train loss 4.5288, val loss 4.5470 +iter 8500: loss 4.3089, time 18601.09ms, mfu 0.05% +step 8750: train loss 4.5043, val loss 4.5171 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 8750: loss 4.3512, time 20111.56ms, mfu 0.05% +step 9000: train loss 4.5187, val loss 4.5002 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 9000: loss 4.2681, time 20058.53ms, mfu 0.05% +step 9250: train loss 4.4672, val loss 4.4937 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 9250: loss 5.0458, time 20092.95ms, mfu 0.05% +step 9500: train loss 4.4649, val loss 4.4596 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 9500: loss 4.9030, time 20107.44ms, mfu 0.05% +step 9750: train loss 4.4884, val loss 4.5100 +iter 9750: loss 4.4117, time 18693.48ms, mfu 0.05% +step 10000: train loss 4.4720, val loss 4.4824 +iter 10000: loss 4.5961, time 18648.58ms, mfu 0.05% diff --git a/results/logs/train_bilinear_diag_w2000_seed1337.log b/results/logs/train_bilinear_diag_w2000_seed1337.log new file mode 100644 index 0000000000000000000000000000000000000000..6f0a034b1a6715b60e0026a0434614c71bb3225c --- /dev/null +++ b/results/logs/train_bilinear_diag_w2000_seed1337.log @@ -0,0 +1,126 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:200: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 37, with 19,968 parameters +using fused AdamW: True +step 0: train loss 10.9796, val loss 10.9790 +iter 0: loss 11.0014, time 19265.45ms, mfu -100.00% +step 250: train loss 6.9363, val loss 6.9696 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 250: loss 6.7255, time 21953.38ms, mfu 0.05% +step 500: train loss 6.4777, val loss 6.5452 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 500: loss 6.2620, time 22044.24ms, mfu 0.05% +step 750: train loss 6.3019, val loss 6.3194 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 750: loss 6.0884, time 21988.14ms, mfu 0.05% +step 1000: train loss 6.1839, val loss 6.1571 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 1000: loss 6.1591, time 22129.15ms, mfu 0.05% +step 1250: train loss 6.0659, val loss 6.0596 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 1250: loss 6.1079, time 20456.81ms, mfu 0.05% +step 1500: train loss 5.9837, val loss 5.9093 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 1500: loss 5.6964, time 20041.17ms, mfu 0.05% +step 1750: train loss 5.9055, val loss 5.8664 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 1750: loss 6.0750, time 20057.72ms, mfu 0.05% +step 2000: train loss 5.8094, val loss 5.7808 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 2000: loss 5.8461, time 20104.99ms, mfu 0.05% +step 2250: train loss 5.7124, val loss 5.7166 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 2250: loss 5.9072, time 20032.26ms, mfu 0.05% +step 2500: train loss 5.5910, val loss 5.5715 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 2500: loss 6.0378, time 20038.45ms, mfu 0.05% +step 2750: train loss 5.4965, val loss 5.4900 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out-openwebtext_phase3_bilinear_diag +iter 4250: loss 4.7363, time 20031.22ms, mfu 0.05% +step 4500: train loss 5.1068, val loss 5.0991 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 4500: loss 4.8455, time 19981.10ms, mfu 0.05% +step 4750: train loss 5.0472, val loss 5.0527 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 4750: loss 5.1526, time 20161.86ms, mfu 0.05% +step 5000: train loss 4.9730, val loss 5.0132 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 5000: loss 5.1642, time 20060.80ms, mfu 0.05% +step 5250: train loss 4.9667, val loss 4.9530 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 5250: loss 4.6291, time 20174.70ms, mfu 0.05% +step 5500: train loss 4.9486, val loss 4.9089 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 5500: loss 5.2276, time 20063.67ms, mfu 0.05% +step 5750: train loss 4.8773, val loss 4.8354 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 5750: loss 5.1470, time 20015.91ms, mfu 0.05% +step 6000: train loss 4.7877, val loss 4.7894 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 6000: loss 4.7179, time 20022.77ms, mfu 0.05% +step 6250: train loss 4.7667, val loss 4.7981 +iter 6250: loss 4.5764, time 18691.41ms, mfu 0.05% +step 6500: train loss 4.7264, val loss 4.7618 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 6500: loss 4.8876, time 20090.01ms, mfu 0.05% +step 6750: train loss 4.6922, val loss 4.6995 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 6750: loss 4.8659, time 20083.51ms, mfu 0.05% +step 7000: train loss 4.6543, val loss 4.6521 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 7000: loss 4.5746, time 20021.65ms, mfu 0.05% +step 7250: train loss 4.6135, val loss 4.6430 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 7250: loss 4.7347, time 20087.66ms, mfu 0.05% +step 7500: train loss 4.6204, val loss 4.5752 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 7500: loss 4.4788, time 20032.16ms, mfu 0.05% +step 7750: train loss 4.5663, val loss 4.5614 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 7750: loss 4.7550, time 20031.41ms, mfu 0.05% +step 8000: train loss 4.5291, val loss 4.5556 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 8000: loss 4.5906, time 20034.98ms, mfu 0.05% +step 8250: train loss 4.5101, val loss 4.5397 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 8250: loss 4.2982, time 20055.88ms, mfu 0.05% +step 8500: train loss 4.5051, val loss 4.5149 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 8500: loss 4.2578, time 20055.76ms, mfu 0.05% +step 8750: train loss 4.4700, val loss 4.4720 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 8750: loss 4.4890, time 20097.83ms, mfu 0.05% +step 9000: train loss 4.4840, val loss 4.4880 +iter 9000: loss 4.5583, time 18634.16ms, mfu 0.05% +step 9250: train loss 4.4467, val loss 4.4741 +iter 9250: loss 4.5266, time 18630.86ms, mfu 0.05% +step 9500: train loss 4.4510, val loss 4.4615 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 9500: loss 4.4885, time 20061.69ms, mfu 0.05% +step 9750: train loss 4.4561, val loss 4.4329 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 9750: loss 4.3793, time 20084.43ms, mfu 0.05% +step 10000: train loss 4.4469, val loss 4.4729 +iter 10000: loss 4.4893, time 18604.32ms, mfu 0.05% diff --git a/results/logs/train_bilinear_diag_w2000_seed42.log b/results/logs/train_bilinear_diag_w2000_seed42.log new file mode 100644 index 0000000000000000000000000000000000000000..886bd3604854a47d8696524350837adf0d166291 --- /dev/null +++ b/results/logs/train_bilinear_diag_w2000_seed42.log @@ -0,0 +1,126 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:200: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 37, with 19,968 parameters +using fused AdamW: True +step 0: train loss 10.9682, val loss 10.9678 +iter 0: loss 10.9622, time 19336.59ms, mfu -100.00% +step 250: train loss 6.9876, val loss 6.9838 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 250: loss 7.0964, time 20451.23ms, mfu 0.05% +step 500: train loss 6.5061, val loss 6.5111 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 500: loss 6.1545, time 20484.67ms, mfu 0.05% +step 750: train loss 6.2892, val loss 6.3212 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 750: loss 6.6257, time 20591.68ms, mfu 0.05% +step 1000: train loss 6.1533, val loss 6.1498 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 1000: loss 5.7973, time 20619.47ms, mfu 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+saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 2750: loss 5.5399, time 20671.49ms, mfu 0.05% +step 3000: train loss 5.4348, val loss 5.4203 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 3000: loss 5.4755, time 20611.97ms, mfu 0.05% +step 3250: train loss 5.3958, val loss 5.3533 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 3250: loss 5.5779, time 20731.54ms, mfu 0.05% +step 3500: train loss 5.3051, val loss 5.3563 +iter 3500: loss 5.4114, time 19283.82ms, mfu 0.05% +step 3750: train loss 5.2658, val loss 5.2904 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 3750: loss 5.3097, time 20969.84ms, mfu 0.05% +step 4000: train loss 5.2175, val loss 5.2228 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 4000: loss 5.8916, time 20667.58ms, mfu 0.05% +step 4250: train loss 5.1783, val loss 5.1641 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 4250: loss 4.9031, time 20865.79ms, mfu 0.05% 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out-openwebtext_phase3_bilinear_diag +iter 7500: loss 4.7646, time 22041.47ms, mfu 0.05% +step 7750: train loss 4.6159, val loss 4.5923 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 7750: loss 4.7115, time 21941.12ms, mfu 0.05% +step 8000: train loss 4.5723, val loss 4.5745 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 8000: loss 4.5803, time 21922.99ms, mfu 0.05% +step 8250: train loss 4.5272, val loss 4.5474 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 8250: loss 4.3700, time 21954.59ms, mfu 0.05% +step 8500: train loss 4.5395, val loss 4.5662 +iter 8500: loss 4.3995, time 19022.94ms, mfu 0.05% +step 8750: train loss 4.4912, val loss 4.5129 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 8750: loss 4.6557, time 21992.16ms, mfu 0.05% +step 9000: train loss 4.5056, val loss 4.5234 +iter 9000: loss 4.4932, time 18965.34ms, mfu 0.05% +step 9250: train loss 4.4826, val loss 4.4732 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 9250: loss 4.6057, time 22130.53ms, mfu 0.05% +step 9500: train loss 4.4521, val loss 4.4702 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 9500: loss 4.1881, time 22033.34ms, mfu 0.05% +step 9750: train loss 4.4468, val loss 4.4624 +saving checkpoint to out-openwebtext_phase3_bilinear_diag +iter 9750: loss 4.7519, time 22030.87ms, mfu 0.05% +step 10000: train loss 4.4476, val loss 4.4745 +iter 10000: loss 4.4573, time 18977.83ms, mfu 0.05% diff --git a/results/logs/train_cheap_qa_w500_seed123.log b/results/logs/train_cheap_qa_w500_seed123.log new file mode 100644 index 0000000000000000000000000000000000000000..78f5779a07554dfc65f7cccb92349d2415db210f --- /dev/null +++ b/results/logs/train_cheap_qa_w500_seed123.log @@ -0,0 +1,124 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.69M +C:\Users\brian\documents\nanogpt\train.py:200: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 124,059,648 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9516, val loss 10.9516 +iter 0: loss 10.9851, time 18756.77ms, mfu -100.00% +step 250: train loss 6.6408, val loss 6.6622 +saving checkpoint to out-openwebtext_warmup500_shared +iter 250: loss 6.9128, time 21392.46ms, mfu 0.05% +step 500: train loss 6.3985, val loss 6.4311 +saving checkpoint to out-openwebtext_warmup500_shared +iter 500: loss 6.2144, time 21467.95ms, mfu 0.05% +step 750: train loss 6.2479, val loss 6.2115 +saving checkpoint to out-openwebtext_warmup500_shared +iter 750: loss 6.6526, time 21458.08ms, mfu 0.05% +step 1000: train loss 6.0747, val loss 6.0679 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1000: loss 5.9325, time 21364.45ms, mfu 0.05% +step 1250: train loss 5.9238, val loss 5.9185 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1250: loss 6.3675, time 21341.72ms, mfu 0.05% +step 1500: train loss 5.7864, val loss 5.7832 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1500: loss 5.4784, time 21275.07ms, mfu 0.05% +step 1750: train loss 5.7080, val loss 5.7210 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1750: loss 5.8382, time 21452.43ms, mfu 0.05% +step 2000: train loss 5.6138, val loss 5.6432 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2000: loss 5.4260, time 21487.79ms, mfu 0.05% +step 2250: train loss 5.5732, val loss 5.5653 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2250: loss 5.3411, time 21385.14ms, mfu 0.05% +step 2500: train loss 5.4707, val loss 5.5069 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2500: loss 5.6108, time 21975.14ms, mfu 0.05% +step 2750: train loss 5.4761, val loss 5.4789 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2750: loss 5.5002, time 21970.15ms, mfu 0.05% +step 3000: train loss 5.3979, val loss 5.4324 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3000: loss 5.3512, time 21873.85ms, mfu 0.05% +step 3250: train loss 5.3769, val loss 5.3433 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3250: loss 4.8884, time 22042.23ms, mfu 0.05% +step 3500: train loss 5.3282, val loss 5.3226 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3500: loss 5.1221, time 21800.39ms, mfu 0.05% +step 3750: train loss 5.2566, val loss 5.2779 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3750: loss 5.1657, time 22037.71ms, mfu 0.05% +step 4000: train loss 5.2332, val loss 5.2575 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4000: loss 5.2003, time 22102.67ms, mfu 0.05% +step 4250: train loss 5.1773, val loss 5.1682 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4250: loss 5.0796, time 21715.25ms, mfu 0.05% +step 4500: train loss 5.1545, val loss 5.1385 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4500: loss 4.9793, time 21943.84ms, mfu 0.05% +step 4750: train loss 5.0893, val loss 5.0682 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4750: loss 5.6070, time 21991.09ms, mfu 0.05% +step 5000: train loss 5.0711, val loss 5.0343 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5000: loss 4.7128, time 21873.61ms, mfu 0.05% +step 5250: train loss 5.0476, val loss 5.0438 +iter 5250: loss 4.5138, time 18784.49ms, mfu 0.05% +step 5500: train loss 4.9713, val loss 5.0068 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5500: loss 4.7704, time 22352.87ms, mfu 0.05% +step 5750: train loss 4.9485, val loss 4.9346 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5750: loss 5.0444, time 22695.91ms, mfu 0.05% +step 6000: train loss 4.9152, val loss 4.9333 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6000: loss 4.9092, time 22132.68ms, mfu 0.05% +step 6250: train loss 4.8637, val loss 4.8324 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6250: loss 4.8655, time 22140.22ms, mfu 0.05% +step 6500: train loss 4.8329, val loss 4.8536 +iter 6500: loss 5.3521, time 18792.88ms, mfu 0.05% +step 6750: train loss 4.7668, val loss 4.7770 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6750: loss 4.4791, time 21876.74ms, mfu 0.05% +step 7000: train loss 4.7888, val loss 4.7774 +iter 7000: loss 5.0901, time 18803.81ms, mfu 0.05% +step 7250: train loss 4.7480, val loss 4.7477 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7250: loss 4.7379, time 21861.13ms, mfu 0.05% +step 7500: train loss 4.6902, val loss 4.7094 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7500: loss 4.9007, time 21931.78ms, mfu 0.05% +step 7750: train loss 4.6605, val loss 4.6822 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7750: loss 5.2211, time 21993.32ms, mfu 0.05% +step 8000: train loss 4.6778, val loss 4.6863 +iter 8000: loss 4.7657, time 18756.58ms, mfu 0.05% +step 8250: train loss 4.6154, val loss 4.6659 +saving checkpoint to out-openwebtext_warmup500_shared +iter 8250: loss 4.9556, time 21844.58ms, mfu 0.05% +step 8500: train loss 4.6276, val loss 4.6481 +saving checkpoint to out-openwebtext_warmup500_shared +iter 8500: loss 4.0983, time 21886.34ms, mfu 0.05% +step 8750: train loss 4.6401, val loss 4.6519 +iter 8750: loss 4.9526, time 18788.47ms, mfu 0.05% +step 9000: train loss 4.5971, val loss 4.6226 +saving checkpoint to out-openwebtext_warmup500_shared +iter 9000: loss 4.0914, time 21792.58ms, mfu 0.05% +step 9250: train loss 4.5710, val loss 4.6019 +saving checkpoint to out-openwebtext_warmup500_shared +iter 9250: loss 4.3691, time 21931.06ms, mfu 0.05% +step 9500: train loss 4.5868, val loss 4.5977 +saving checkpoint to out-openwebtext_warmup500_shared +iter 9500: loss 4.8289, time 21838.89ms, mfu 0.05% +step 9750: train loss 4.5557, val loss 4.5851 +saving checkpoint to out-openwebtext_warmup500_shared +iter 9750: loss 4.7804, time 21975.07ms, mfu 0.05% +step 10000: train loss 4.5657, val loss 4.5875 +iter 10000: loss 4.6502, time 18857.30ms, mfu 0.05% diff --git a/results/logs/train_cheap_qa_w500_seed1337.log b/results/logs/train_cheap_qa_w500_seed1337.log new file mode 100644 index 0000000000000000000000000000000000000000..acb7574f395839640505f17df54f4781e5d0d081 --- /dev/null +++ b/results/logs/train_cheap_qa_w500_seed1337.log @@ -0,0 +1,120 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.69M +C:\Users\brian\documents\nanogpt\train.py:200: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 124,059,648 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9681, val loss 10.9657 +iter 0: loss 10.9667, time 18917.77ms, mfu -100.00% +step 250: train loss 6.6481, val loss 6.5858 +saving checkpoint to out-openwebtext_warmup500_shared +iter 250: loss 6.8850, time 21013.97ms, mfu 0.05% +step 500: train loss 6.4108, val loss 6.4127 +saving checkpoint to out-openwebtext_warmup500_shared +iter 500: loss 6.8728, time 20375.47ms, mfu 0.05% +step 750: train loss 6.1760, val loss 6.1849 +saving checkpoint to out-openwebtext_warmup500_shared +iter 750: loss 6.2386, time 20011.68ms, mfu 0.05% +step 1000: train loss 6.1083, val loss 6.0688 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1000: loss 6.0201, time 20298.41ms, mfu 0.05% +step 1250: train loss 5.9140, val loss 5.9109 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1250: loss 6.1449, time 19920.42ms, mfu 0.05% +step 1500: train loss 5.7944, val loss 5.7887 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1500: loss 6.8627, time 20238.03ms, mfu 0.05% +step 1750: train loss 5.7124, val loss 5.7054 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1750: loss 5.3493, time 20438.31ms, mfu 0.05% +step 2000: train loss 5.6868, val loss 5.6673 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2000: loss 5.1718, time 22225.06ms, mfu 0.05% +step 2250: train loss 5.6417, val loss 5.6570 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2250: loss 5.1268, time 22303.25ms, mfu 0.05% +step 2500: train loss 5.5904, val loss 5.5977 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2500: loss 4.9937, time 21838.77ms, mfu 0.05% +step 2750: train loss 5.5327, val loss 5.5483 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2750: loss 5.2443, time 21843.81ms, mfu 0.05% +step 3000: train loss 5.4845, val loss 5.5168 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3000: loss 5.4154, time 21358.17ms, mfu 0.05% +step 3250: train loss 5.4349, val loss 5.4040 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3250: loss 5.3853, time 22108.21ms, mfu 0.05% +step 3500: train loss 5.4217, val loss 5.4144 +iter 3500: loss 5.9338, time 18883.32ms, mfu 0.05% +step 3750: train loss 5.3954, val loss 5.4175 +iter 3750: loss 5.3109, time 18781.36ms, mfu 0.05% +step 4000: train loss 5.3774, val loss 5.3333 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4000: loss 5.4897, time 21971.80ms, mfu 0.05% +step 4250: train loss 5.3240, val loss 5.3440 +iter 4250: loss 5.4547, time 18848.66ms, mfu 0.05% +step 4500: train loss 5.2669, val loss 5.3097 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4500: loss 5.5260, time 21982.87ms, mfu 0.05% +step 4750: train loss 5.2306, val loss 5.2062 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4750: loss 5.3123, time 21975.15ms, mfu 0.05% +step 5000: train loss 5.1849, val loss 5.2177 +iter 5000: loss 5.4104, time 18792.56ms, mfu 0.05% +step 5250: train loss 5.1598, val loss 5.1755 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5250: loss 4.8541, time 21953.07ms, mfu 0.05% +step 5500: train loss 5.1317, val loss 5.1246 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5500: loss 5.0241, time 21795.90ms, mfu 0.05% +step 5750: train loss 5.0838, val loss 5.0814 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5750: loss 5.1067, time 21732.11ms, mfu 0.05% +step 6000: train loss 5.1122, val loss 5.0869 +iter 6000: loss 5.1008, time 18821.55ms, mfu 0.05% +step 6250: train loss 5.0108, val loss 5.0261 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6250: loss 4.6799, time 21876.82ms, mfu 0.05% +step 6500: train loss 5.0333, val loss 5.0257 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6500: loss 5.0561, time 21843.53ms, mfu 0.05% +step 6750: train loss 4.9675, val loss 4.9504 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6750: loss 5.2968, time 21835.43ms, mfu 0.05% +step 7000: train loss 4.9174, val loss 4.9119 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7000: loss 5.2979, time 21721.61ms, mfu 0.05% +step 7250: train loss 4.9213, val loss 4.8813 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7250: loss 4.9166, time 21927.43ms, mfu 0.05% +step 7500: train loss 4.8515, val loss 4.8472 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7500: loss 5.0117, time 21790.36ms, mfu 0.05% +step 7750: train loss 4.8595, val loss 4.8267 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7750: loss 4.6645, time 21978.18ms, mfu 0.05% +step 8000: train loss 4.8025, val loss 4.8438 +iter 8000: loss 4.7051, time 18793.71ms, mfu 0.05% +step 8250: train loss 4.7810, val loss 4.8030 +saving checkpoint to out-openwebtext_warmup500_shared +iter 8250: loss 4.2875, time 21850.35ms, mfu 0.05% +step 8500: train loss 4.7573, val loss 4.8135 +iter 8500: loss 4.8167, time 18835.62ms, mfu 0.05% +step 8750: train loss 4.7644, val loss 4.7561 +saving checkpoint to out-openwebtext_warmup500_shared +iter 8750: loss 5.0675, time 21839.04ms, mfu 0.05% +step 9000: train loss 4.7160, val loss 4.7106 +saving checkpoint to out-openwebtext_warmup500_shared +iter 9000: loss 4.2723, time 21902.41ms, mfu 0.05% +step 9250: train loss 4.7140, val loss 4.7182 +iter 9250: loss 4.5840, time 18757.96ms, mfu 0.05% +step 9500: train loss 4.7495, val loss 4.7120 +iter 9500: loss 4.9199, time 18776.71ms, mfu 0.05% +step 9750: train loss 4.6962, val loss 4.6741 +saving checkpoint to out-openwebtext_warmup500_shared +iter 9750: loss 4.7117, time 22047.51ms, mfu 0.05% +step 10000: train loss 4.7118, val loss 4.7138 +iter 10000: loss 4.9623, time 18816.41ms, mfu 0.05% diff --git a/results/logs/train_cheap_qa_w500_seed42.log b/results/logs/train_cheap_qa_w500_seed42.log new file mode 100644 index 0000000000000000000000000000000000000000..dd0589ffc1877e56ea54ce5b024a6bb1bfe38899 --- /dev/null +++ b/results/logs/train_cheap_qa_w500_seed42.log @@ -0,0 +1,127 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.69M +C:\Users\brian\documents\nanogpt\train.py:200: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 124,059,648 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9432, val loss 10.9442 +iter 0: loss 10.9369, time 18487.01ms, mfu -100.00% +step 250: train loss 6.6534, val loss 6.6478 +saving checkpoint to out-openwebtext_warmup500_shared +iter 250: loss 6.5149, time 19853.15ms, mfu 0.05% +step 500: train loss 6.4157, val loss 6.3905 +saving checkpoint to out-openwebtext_warmup500_shared +iter 500: loss 6.3067, time 19876.18ms, mfu 0.05% +step 750: train loss 6.2312, val loss 6.2210 +saving checkpoint to out-openwebtext_warmup500_shared +iter 750: loss 6.4491, time 19986.59ms, mfu 0.05% +step 1000: train loss 6.0774, val loss 6.0805 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1000: loss 5.8275, time 19813.51ms, mfu 0.05% +step 1250: train loss 5.9502, val loss 5.9771 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1250: loss 5.8131, time 20018.89ms, mfu 0.05% +step 1500: train loss 5.8104, val loss 5.8379 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1500: loss 5.6412, time 19861.85ms, mfu 0.05% +step 1750: train loss 5.7112, val loss 5.7157 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1750: loss 6.6651, time 20683.56ms, mfu 0.05% +step 2000: train loss 5.6130, val loss 5.6172 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2000: loss 5.5606, time 20034.21ms, mfu 0.05% +step 2250: train loss 5.5934, val loss 5.6143 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2250: loss 5.5281, time 20415.09ms, mfu 0.05% +step 2500: train loss 5.5350, val loss 5.5175 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2500: loss 5.5253, time 20721.81ms, mfu 0.05% +step 2750: train loss 5.4838, val loss 5.4882 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2750: loss 5.7181, time 21040.31ms, mfu 0.05% +step 3000: train loss 5.4696, val loss 5.4355 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3000: loss 5.4870, time 20456.84ms, mfu 0.05% +step 3250: train loss 5.3894, val loss 5.4024 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3250: loss 5.5764, time 20688.69ms, mfu 0.05% +step 3500: train loss 5.3390, val loss 5.3632 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3500: loss 5.4216, time 20448.96ms, mfu 0.05% +step 3750: train loss 5.3203, val loss 5.3016 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3750: loss 5.4467, time 20499.54ms, mfu 0.05% +step 4000: train loss 5.2757, val loss 5.2779 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4000: loss 5.1266, time 20596.59ms, mfu 0.05% +step 4250: train loss 5.2086, val loss 5.2073 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4250: loss 5.0014, time 20933.84ms, mfu 0.05% +step 4500: train loss 5.1786, val loss 5.2027 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4500: loss 5.3439, time 20896.61ms, mfu 0.05% +step 4750: train loss 5.1435, val loss 5.1563 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4750: loss 5.3604, time 21418.99ms, mfu 0.05% +step 5000: train loss 5.0818, val loss 5.1065 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5000: loss 5.0177, time 20568.76ms, mfu 0.05% +step 5250: train loss 5.0586, val loss 5.0742 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5250: loss 5.3171, time 20900.46ms, mfu 0.05% +step 5500: train loss 5.0271, val loss 5.0377 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5500: loss 4.8868, time 20540.70ms, mfu 0.05% +step 5750: train loss 4.9846, val loss 5.0093 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5750: loss 4.8902, time 21029.45ms, mfu 0.05% +step 6000: train loss 4.9349, val loss 4.9497 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6000: loss 4.8426, time 20591.09ms, mfu 0.05% +step 6250: train loss 4.8841, val loss 4.9225 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6250: loss 5.0677, time 20581.78ms, mfu 0.05% +step 6500: train loss 4.8574, val loss 4.8423 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6500: loss 4.7546, time 20927.22ms, mfu 0.05% +step 6750: train loss 4.8238, val loss 4.8198 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6750: loss 4.7048, time 20521.25ms, mfu 0.05% +step 7000: train loss 4.7992, val loss 4.7875 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7000: loss 4.5110, time 21446.60ms, mfu 0.05% +step 7250: train loss 4.7796, val loss 4.7610 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7250: loss 4.9815, time 21476.10ms, mfu 0.05% +step 7500: train loss 4.7003, val loss 4.7522 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7500: loss 4.8373, time 20560.18ms, mfu 0.05% +step 7750: train loss 4.7138, val loss 4.7196 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7750: loss 4.5031, time 20533.04ms, mfu 0.05% +step 8000: train loss 4.6551, val loss 4.6958 +saving checkpoint to out-openwebtext_warmup500_shared +iter 8000: loss 4.8436, time 20786.30ms, mfu 0.05% +step 8250: train loss 4.6979, val loss 4.6864 +saving checkpoint to out-openwebtext_warmup500_shared +iter 8250: loss 4.2401, time 21207.97ms, mfu 0.05% +step 8500: train loss 4.6915, val loss 4.6604 +saving checkpoint to out-openwebtext_warmup500_shared +iter 8500: loss 4.8830, time 21628.49ms, mfu 0.05% +step 8750: train loss 4.6172, val loss 4.6722 +iter 8750: loss 4.5270, time 18884.96ms, mfu 0.05% +step 9000: train loss 4.6317, val loss 4.6352 +saving checkpoint to out-openwebtext_warmup500_shared +iter 9000: loss 4.2703, time 20485.89ms, mfu 0.05% +step 9250: train loss 4.6258, val loss 4.6554 +iter 9250: loss 4.4299, time 18860.43ms, mfu 0.05% +step 9500: train loss 4.6204, val loss 4.5907 +saving checkpoint to out-openwebtext_warmup500_shared +iter 9500: loss 4.6402, time 20552.06ms, mfu 0.05% +step 9750: train loss 4.6139, val loss 4.6220 +iter 9750: loss 4.0580, time 18932.42ms, mfu 0.05% +step 10000: train loss 4.5727, val loss 4.5797 +saving checkpoint to out-openwebtext_warmup500_shared +iter 10000: loss 4.3960, time 20617.49ms, mfu 0.05% diff --git a/results/logs/train_dot_elementwise_seed123.log b/results/logs/train_dot_elementwise_seed123.log new file mode 100644 index 0000000000000000000000000000000000000000..7ad002248d8c8fe4bfd6aff0e95f52859e86b10d --- /dev/null +++ b/results/logs/train_dot_elementwise_seed123.log @@ -0,0 +1,126 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:199: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9593, val loss 10.9583 +iter 0: loss 10.9760, time 18616.92ms, mfu -100.00% +step 250: train loss 6.9613, val loss 6.9070 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 250: loss 6.6580, time 22311.69ms, mfu 0.05% +step 500: train loss 6.4901, val loss 6.5665 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 500: loss 6.5977, time 22371.65ms, mfu 0.05% +step 750: train loss 6.3089, val loss 6.3140 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 750: loss 5.6354, time 21354.18ms, mfu 0.05% +step 1000: train loss 6.1910, val loss 6.1901 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 1000: loss 6.4836, time 21357.54ms, mfu 0.05% +step 1250: train loss 6.0133, val loss 6.0439 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 1250: loss 5.7500, time 21198.50ms, mfu 0.05% +step 1500: train loss 5.9849, val loss 5.9521 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 1500: loss 5.7799, time 21473.31ms, mfu 0.05% +step 1750: train loss 5.8593, val loss 5.8974 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 1750: loss 6.4887, time 21373.76ms, mfu 0.05% +step 2000: train loss 5.8044, val loss 5.8448 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 2000: loss 5.7305, time 21305.85ms, mfu 0.05% +step 2250: train loss 5.6502, val loss 5.7069 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 2250: loss 6.1438, time 21288.79ms, mfu 0.05% +step 2500: train loss 5.6104, val loss 5.6132 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 2500: loss 5.8316, time 21250.85ms, mfu 0.05% +step 2750: train loss 5.5629, val loss 5.5344 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 2750: loss 6.0621, time 21221.10ms, mfu 0.05% +step 3000: train loss 5.4816, val loss 5.4885 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 3000: loss 6.1870, time 21188.14ms, mfu 0.05% +step 3250: train loss 5.3774, val loss 5.3776 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 3250: loss 5.3149, time 21333.74ms, mfu 0.05% +step 3500: train loss 5.3088, val loss 5.3567 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 3500: loss 5.3508, time 21179.13ms, mfu 0.05% +step 3750: train loss 5.2780, val loss 5.2642 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 3750: loss 5.0226, time 21351.70ms, mfu 0.05% +step 4000: train loss 5.2210, val loss 5.2297 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 4000: loss 5.1420, time 21436.75ms, mfu 0.05% +step 4250: train loss 5.1917, val loss 5.2014 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 4250: loss 5.2480, time 21301.86ms, mfu 0.05% +step 4500: train loss 5.1140, val loss 5.1152 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 4500: loss 5.1632, time 21265.97ms, mfu 0.05% +step 4750: train loss 5.0912, val loss 5.0901 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 4750: loss 4.9805, time 21115.88ms, mfu 0.05% +step 5000: train loss 5.0096, val loss 5.0037 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 5000: loss 5.1373, time 21249.09ms, mfu 0.05% +step 5250: train loss 4.9955, val loss 4.9835 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 5250: loss 5.1680, time 21113.54ms, mfu 0.05% +step 5500: train loss 4.9145, val loss 4.9559 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 5500: loss 4.8479, time 21343.74ms, mfu 0.05% +step 5750: train loss 4.9378, val loss 4.9157 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 5750: loss 5.2980, time 21215.62ms, mfu 0.05% +step 6000: train loss 4.8847, val loss 4.8539 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 6000: loss 4.9106, time 21354.24ms, mfu 0.05% +step 6250: train loss 4.8053, val loss 4.8577 +iter 6250: loss 5.0498, time 18422.93ms, mfu 0.05% +step 6500: train loss 4.7705, val loss 4.7786 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 6500: loss 4.6439, time 21349.20ms, mfu 0.05% +step 6750: train loss 4.7284, val loss 4.7511 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 6750: loss 4.4644, time 21331.62ms, mfu 0.05% +step 7000: train loss 4.6923, val loss 4.6983 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 7000: loss 4.6068, time 21215.54ms, mfu 0.05% +step 7250: train loss 4.6634, val loss 4.6856 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 7250: loss 4.2839, time 21796.60ms, mfu 0.05% +step 7500: train loss 4.6686, val loss 4.6505 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 7500: loss 4.6099, time 21891.26ms, mfu 0.05% +step 7750: train loss 4.6168, val loss 4.6141 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 7750: loss 4.7828, time 21872.66ms, mfu 0.05% +step 8000: train loss 4.5929, val loss 4.5856 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 8000: loss 4.2185, time 21387.06ms, mfu 0.05% +step 8250: train loss 4.5486, val loss 4.5455 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 8250: loss 4.3911, time 21357.12ms, mfu 0.05% +step 8500: train loss 4.5357, val loss 4.5546 +iter 8500: loss 4.3294, time 18294.71ms, mfu 0.05% +step 8750: train loss 4.5091, val loss 4.5246 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 8750: loss 4.3438, time 21379.42ms, mfu 0.05% +step 9000: train loss 4.5249, val loss 4.5060 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 9000: loss 4.2822, time 21386.57ms, mfu 0.05% +step 9250: train loss 4.4734, val loss 4.4982 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 9250: loss 5.0134, time 21317.64ms, mfu 0.05% +step 9500: train loss 4.4701, val loss 4.4662 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 9500: loss 4.9098, time 21371.72ms, mfu 0.05% +step 9750: train loss 4.4939, val loss 4.5147 +iter 9750: loss 4.4429, time 18282.87ms, mfu 0.05% +step 10000: train loss 4.4780, val loss 4.4847 +iter 10000: loss 4.6232, time 18338.30ms, mfu 0.05% diff --git a/results/logs/train_dot_elementwise_seed1337.log b/results/logs/train_dot_elementwise_seed1337.log new file mode 100644 index 0000000000000000000000000000000000000000..113f1776c7d0a4cf973244468c6a2e0d15ca8639 --- /dev/null +++ b/results/logs/train_dot_elementwise_seed1337.log @@ -0,0 +1,126 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:199: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9795, val loss 10.9789 +iter 0: loss 11.0007, time 18571.63ms, mfu -100.00% +step 250: train loss 6.9364, val loss 6.9696 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 250: loss 6.7257, time 21333.42ms, mfu 0.05% +step 500: train loss 6.4777, val loss 6.5451 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 500: loss 6.2618, time 21296.22ms, mfu 0.05% +step 750: train loss 6.3006, val loss 6.3182 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 750: loss 6.0881, time 21129.88ms, mfu 0.05% +step 1000: train loss 6.1729, val loss 6.1461 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 1000: loss 6.1423, time 21201.95ms, mfu 0.05% +step 1250: train loss 6.0707, val loss 6.0631 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 1250: loss 6.1124, time 21161.51ms, mfu 0.05% +step 1500: train loss 5.9830, val loss 5.9110 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 1500: loss 5.7030, time 21203.03ms, mfu 0.05% +step 1750: train loss 5.9064, val loss 5.8710 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 1750: loss 6.0679, time 21203.47ms, mfu 0.05% +step 2000: train loss 5.8000, val loss 5.7732 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 2000: loss 5.8516, time 21340.31ms, mfu 0.05% +step 2250: train loss 5.6919, val loss 5.6962 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 2250: loss 5.9000, time 21570.12ms, mfu 0.05% +step 2500: train loss 5.5901, val loss 5.5711 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 2500: loss 6.0801, time 21365.91ms, mfu 0.05% +step 2750: train loss 5.5034, val loss 5.5007 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 2750: loss 5.4930, time 21499.41ms, mfu 0.05% +step 3000: train loss 5.4336, val loss 5.4487 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 3000: loss 5.5091, time 21379.19ms, mfu 0.05% +step 3250: train loss 5.3468, val loss 5.3518 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 3250: loss 5.3241, time 21218.44ms, mfu 0.05% +step 3500: train loss 5.3555, val loss 5.3138 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 3500: loss 5.2600, time 21292.16ms, mfu 0.05% +step 3750: train loss 5.2604, val loss 5.2430 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 3750: loss 5.5489, time 21258.47ms, mfu 0.05% +step 4000: train loss 5.2137, val loss 5.2132 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 4000: loss 5.2934, time 21431.59ms, mfu 0.05% +step 4250: train loss 5.1521, val loss 5.1660 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 4250: loss 4.7351, time 21338.17ms, mfu 0.05% +step 4500: train loss 5.1111, val loss 5.1025 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 4500: loss 4.8370, time 21290.82ms, mfu 0.05% +step 4750: train loss 5.0548, val loss 5.0648 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 4750: loss 5.1715, time 21293.42ms, mfu 0.05% +step 5000: train loss 4.9836, val loss 5.0194 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 5000: loss 5.1628, time 21309.56ms, mfu 0.05% +step 5250: train loss 4.9793, val loss 4.9614 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 5250: loss 4.6227, time 21484.56ms, mfu 0.05% +step 5500: train loss 4.9490, val loss 4.9103 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 5500: loss 5.1721, time 21305.27ms, mfu 0.05% +step 5750: train loss 4.8919, val loss 4.8484 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 5750: loss 5.1753, time 21244.32ms, mfu 0.05% +step 6000: train loss 4.7978, val loss 4.8005 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 6000: loss 4.7117, time 21269.19ms, mfu 0.05% +step 6250: train loss 4.7806, val loss 4.8079 +iter 6250: loss 4.6017, time 18312.38ms, mfu 0.05% +step 6500: train loss 4.7362, val loss 4.7719 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 6500: loss 4.9249, time 21388.15ms, mfu 0.05% +step 6750: train loss 4.7034, val loss 4.7044 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 6750: loss 4.8880, time 21301.28ms, mfu 0.05% +step 7000: train loss 4.6659, val loss 4.6621 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 7000: loss 4.5558, time 21242.25ms, mfu 0.05% +step 7250: train loss 4.6186, val loss 4.6468 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 7250: loss 4.7227, time 21070.07ms, mfu 0.05% +step 7500: train loss 4.6276, val loss 4.5832 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 7500: loss 4.5001, time 21465.43ms, mfu 0.05% +step 7750: train loss 4.5771, val loss 4.5701 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 7750: loss 4.7642, time 21323.07ms, mfu 0.05% +step 8000: train loss 4.5366, val loss 4.5635 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 8000: loss 4.5939, time 21334.04ms, mfu 0.05% +step 8250: train loss 4.5186, val loss 4.5473 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 8250: loss 4.3011, time 21412.39ms, mfu 0.05% +step 8500: train loss 4.5114, val loss 4.5233 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 8500: loss 4.2676, time 21286.35ms, mfu 0.05% +step 8750: train loss 4.4759, val loss 4.4773 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 8750: loss 4.4944, time 21323.93ms, mfu 0.05% +step 9000: train loss 4.4907, val loss 4.4935 +iter 9000: loss 4.5788, time 18368.15ms, mfu 0.05% +step 9250: train loss 4.4502, val loss 4.4793 +iter 9250: loss 4.5374, time 18283.96ms, mfu 0.05% +step 9500: train loss 4.4581, val loss 4.4678 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 9500: loss 4.4842, time 21401.30ms, mfu 0.05% +step 9750: train loss 4.4615, val loss 4.4374 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 9750: loss 4.3793, time 21161.72ms, mfu 0.05% +step 10000: train loss 4.4544, val loss 4.4806 +iter 10000: loss 4.4831, time 18248.43ms, mfu 0.05% diff --git a/results/logs/train_dot_elementwise_seed42.log b/results/logs/train_dot_elementwise_seed42.log new file mode 100644 index 0000000000000000000000000000000000000000..ca64409fb31195a271eb031759f8f77ce2511c1f --- /dev/null +++ b/results/logs/train_dot_elementwise_seed42.log @@ -0,0 +1,127 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:199: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9682, val loss 10.9678 +iter 0: loss 10.9624, time 19001.64ms, mfu -100.00% +step 250: train loss 6.9875, val loss 6.9838 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 250: loss 7.0965, time 22530.73ms, mfu 0.05% +step 500: train loss 6.5061, val loss 6.5111 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 500: loss 6.1544, time 22628.33ms, mfu 0.05% +step 750: train loss 6.2891, val loss 6.3209 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 750: loss 6.6225, time 26305.43ms, mfu 0.05% +step 1000: train loss 6.1594, val loss 6.1576 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 1000: loss 5.7977, time 24908.58ms, mfu 0.05% +step 1250: train loss 6.0121, val loss 6.0310 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 1250: loss 6.5299, time 23927.30ms, mfu 0.05% +step 1500: train loss 5.9694, val loss 5.9634 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 1500: loss 5.4807, time 25648.37ms, mfu 0.04% +step 1750: train loss 5.8663, val loss 5.8788 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 1750: loss 6.1823, time 32322.77ms, mfu 0.04% +step 2000: train loss 5.8149, val loss 5.8289 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 2000: loss 5.6224, time 28206.30ms, mfu 0.04% +step 2250: train loss 5.6645, val loss 5.6771 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 2250: loss 5.7664, time 24650.11ms, mfu 0.04% +step 2500: train loss 5.5932, val loss 5.5860 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 2500: loss 4.9037, time 22820.25ms, mfu 0.04% +step 2750: train loss 5.5070, val loss 5.5058 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 2750: loss 5.5161, time 25611.20ms, mfu 0.04% +step 3000: train loss 5.4293, val loss 5.4142 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 3000: loss 5.4806, time 29198.09ms, mfu 0.04% +step 3250: train loss 5.3864, val loss 5.3429 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 3250: loss 5.5491, time 24008.33ms, mfu 0.04% +step 3500: train loss 5.2909, val loss 5.3404 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 3500: loss 5.4112, time 25706.32ms, mfu 0.04% +step 3750: train loss 5.2658, val loss 5.2893 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 3750: loss 5.3241, time 22767.72ms, mfu 0.04% +step 4000: train loss 5.2127, val loss 5.2173 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 4000: loss 5.9016, time 20781.50ms, mfu 0.04% +step 4250: train loss 5.1768, val loss 5.1609 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 4250: loss 4.8918, time 20488.61ms, mfu 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out-openwebtext_dot_gate_shared +iter 6000: loss 4.7500, time 19741.13ms, mfu 0.05% +step 6250: train loss 4.7741, val loss 4.7863 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 6250: loss 4.4547, time 21809.88ms, mfu 0.05% +step 6500: train loss 4.7652, val loss 4.7456 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 6500: loss 4.6225, time 21671.38ms, mfu 0.05% +step 6750: train loss 4.7047, val loss 4.6923 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 6750: loss 4.6679, time 21874.99ms, mfu 0.05% +step 7000: train loss 4.6549, val loss 4.6795 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 7000: loss 4.6344, time 21726.46ms, mfu 0.05% +step 7250: train loss 4.6389, val loss 4.6345 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 7250: loss 4.8557, time 21668.56ms, mfu 0.05% +step 7500: train loss 4.6335, val loss 4.6134 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 7500: loss 4.7470, time 21843.61ms, mfu 0.05% +step 7750: train loss 4.6110, val loss 4.5860 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 7750: loss 4.7129, time 21326.06ms, mfu 0.05% +step 8000: train loss 4.5669, val loss 4.5648 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 8000: loss 4.5233, time 21087.59ms, mfu 0.05% +step 8250: train loss 4.5212, val loss 4.5407 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 8250: loss 4.3764, time 21365.32ms, mfu 0.05% +step 8500: train loss 4.5304, val loss 4.5595 +iter 8500: loss 4.4001, time 18279.96ms, mfu 0.05% +step 8750: train loss 4.4846, val loss 4.5041 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 8750: loss 4.6658, time 21277.66ms, mfu 0.05% +step 9000: train loss 4.4983, val loss 4.5177 +iter 9000: loss 4.4957, time 18356.88ms, mfu 0.05% +step 9250: train loss 4.4751, val loss 4.4660 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 9250: loss 4.5935, time 21322.21ms, mfu 0.05% +step 9500: train loss 4.4470, val loss 4.4637 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 9500: loss 4.1646, time 21366.13ms, mfu 0.05% +step 9750: train loss 4.4428, val loss 4.4574 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 9750: loss 4.7786, time 21184.27ms, mfu 0.05% +step 10000: train loss 4.4404, val loss 4.4707 +iter 10000: loss 4.4349, time 18273.80ms, mfu 0.05% diff --git a/results/logs/train_dot_scalar_seed123.log b/results/logs/train_dot_scalar_seed123.log new file mode 100644 index 0000000000000000000000000000000000000000..04736131095a5a44caabdadd18d4861578f34290 --- /dev/null +++ b/results/logs/train_dot_scalar_seed123.log @@ -0,0 +1,126 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:199: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9598, val loss 10.9588 +iter 0: loss 10.9760, time 18624.24ms, mfu -100.00% +step 250: train loss 6.9614, val loss 6.9074 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 250: loss 6.6569, time 21106.00ms, mfu 0.05% +step 500: train loss 6.4873, val loss 6.5632 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 500: loss 6.5967, time 21022.60ms, mfu 0.05% +step 750: train loss 6.2941, val loss 6.2999 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 750: loss 5.6175, time 21137.36ms, mfu 0.05% +step 1000: train loss 6.1673, val loss 6.1653 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 1000: loss 6.4646, time 21237.18ms, mfu 0.05% +step 1250: train loss 6.0164, val loss 6.0463 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 1250: loss 5.7347, time 21247.91ms, mfu 0.05% +step 1500: train loss 5.9943, val loss 5.9621 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 1500: loss 5.7769, time 21323.18ms, mfu 0.05% +step 1750: train loss 5.8568, val loss 5.8991 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 1750: loss 6.5183, time 21129.15ms, mfu 0.05% +step 2000: train loss 5.7939, val loss 5.8284 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 2000: loss 5.7359, time 21173.46ms, mfu 0.05% +step 2250: train loss 5.6459, val loss 5.7023 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 2250: loss 6.1367, time 21116.13ms, mfu 0.05% +step 2500: train loss 5.6005, val loss 5.6041 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 2500: loss 5.8437, time 21335.24ms, mfu 0.05% +step 2750: train loss 5.5531, val loss 5.5267 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 2750: loss 6.0929, time 21099.85ms, mfu 0.05% +step 3000: train loss 5.4748, val loss 5.4784 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 3000: loss 6.1949, time 21244.43ms, mfu 0.05% +step 3250: train loss 5.3734, val loss 5.3708 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 3250: loss 5.3266, time 21306.65ms, mfu 0.05% +step 3500: train loss 5.3078, val loss 5.3575 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 3500: loss 5.3476, time 21325.79ms, mfu 0.05% +step 3750: train loss 5.2737, val loss 5.2571 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 3750: loss 5.0149, time 21207.07ms, mfu 0.05% +step 4000: train loss 5.2251, val loss 5.2360 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 4000: loss 5.1705, time 21127.57ms, mfu 0.05% +step 4250: train loss 5.1850, val loss 5.1933 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 4250: loss 5.2156, time 21244.74ms, mfu 0.05% +step 4500: train loss 5.1000, val loss 5.1006 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 4500: loss 5.1665, time 21369.39ms, mfu 0.05% +step 4750: train loss 5.0858, val loss 5.0827 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 4750: loss 4.9727, time 21318.32ms, mfu 0.05% +step 5000: train loss 4.9976, val loss 4.9952 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 5000: loss 5.0868, time 21180.75ms, mfu 0.05% +step 5250: train loss 4.9809, val loss 4.9728 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 5250: loss 5.1414, time 21653.46ms, mfu 0.05% +step 5500: train loss 4.8864, val loss 4.9294 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 5500: loss 4.8457, time 21291.13ms, mfu 0.05% +step 5750: train loss 4.9140, val loss 4.8969 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 5750: loss 5.2288, time 21156.75ms, mfu 0.05% +step 6000: train loss 4.8522, val loss 4.8229 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 6000: loss 4.8568, time 21134.54ms, mfu 0.05% +step 6250: train loss 4.7804, val loss 4.8325 +iter 6250: loss 5.0537, time 18252.39ms, mfu 0.05% +step 6500: train loss 4.7441, val loss 4.7526 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 6500: loss 4.6231, time 21318.20ms, mfu 0.05% +step 6750: train loss 4.7000, val loss 4.7216 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 6750: loss 4.3430, time 21230.08ms, mfu 0.05% +step 7000: train loss 4.6693, val loss 4.6772 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 7000: loss 4.6019, time 21504.43ms, mfu 0.05% +step 7250: train loss 4.6414, val loss 4.6712 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 7250: loss 4.1754, time 20862.58ms, mfu 0.05% +step 7500: train loss 4.6483, val loss 4.6301 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 7500: loss 4.6076, time 21435.13ms, mfu 0.05% +step 7750: train loss 4.6038, val loss 4.5988 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 7750: loss 4.7864, time 21133.58ms, mfu 0.05% +step 8000: train loss 4.5784, val loss 4.5731 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 8000: loss 4.1676, time 21241.51ms, mfu 0.05% +step 8250: train loss 4.5357, val loss 4.5322 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 8250: loss 4.3513, time 21164.27ms, mfu 0.05% +step 8500: train loss 4.5246, val loss 4.5426 +iter 8500: loss 4.3124, time 18304.12ms, mfu 0.05% +step 8750: train loss 4.4998, val loss 4.5141 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 8750: loss 4.3494, time 21363.98ms, mfu 0.05% +step 9000: train loss 4.5164, val loss 4.4962 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 9000: loss 4.2787, time 21272.81ms, mfu 0.05% +step 9250: train loss 4.4638, val loss 4.4910 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 9250: loss 4.9882, time 21332.83ms, mfu 0.05% +step 9500: train loss 4.4639, val loss 4.4608 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 9500: loss 4.8983, time 21208.05ms, mfu 0.05% +step 9750: train loss 4.4866, val loss 4.5089 +iter 9750: loss 4.4128, time 18266.79ms, mfu 0.05% +step 10000: train loss 4.4712, val loss 4.4814 +iter 10000: loss 4.6037, time 18266.52ms, mfu 0.05% diff --git a/results/logs/train_dot_scalar_seed1337.log b/results/logs/train_dot_scalar_seed1337.log new file mode 100644 index 0000000000000000000000000000000000000000..a3dc860d9b2f917e8eca814be78eb2c85f54fdd0 --- /dev/null +++ b/results/logs/train_dot_scalar_seed1337.log @@ -0,0 +1,126 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:199: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9796, val loss 10.9789 +iter 0: loss 11.0008, time 18647.34ms, mfu -100.00% +step 250: train loss 6.9371, val loss 6.9703 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 250: loss 6.7253, time 21562.29ms, mfu 0.05% +step 500: train loss 6.4747, val loss 6.5423 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 500: loss 6.2577, time 21666.84ms, mfu 0.05% +step 750: train loss 6.2859, val loss 6.3015 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 750: loss 6.0662, time 21084.02ms, mfu 0.05% +step 1000: train loss 6.1568, val loss 6.1298 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 1000: loss 6.1291, time 21344.54ms, mfu 0.05% +step 1250: train loss 6.0640, val loss 6.0566 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 1250: loss 6.1245, time 21293.71ms, mfu 0.05% +step 1500: train loss 5.9788, val loss 5.9058 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 1500: loss 5.7034, time 21417.49ms, mfu 0.05% +step 1750: train loss 5.9035, val loss 5.8681 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 1750: loss 6.0752, time 21247.88ms, mfu 0.05% +step 2000: train loss 5.8081, val loss 5.7837 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 2000: loss 5.8713, time 21116.55ms, mfu 0.05% +step 2250: train loss 5.7007, val loss 5.7038 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 2250: loss 5.9310, time 21099.62ms, mfu 0.05% +step 2500: train loss 5.6119, val loss 5.5914 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 2500: loss 6.0405, time 21155.77ms, mfu 0.05% +step 2750: train loss 5.5109, val loss 5.5072 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 2750: loss 5.5034, time 21115.96ms, mfu 0.05% +step 3000: train loss 5.4340, val loss 5.4506 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 3000: loss 5.5285, time 21238.29ms, mfu 0.05% +step 3250: train loss 5.3541, val loss 5.3579 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 3250: loss 5.3427, time 21251.68ms, mfu 0.05% +step 3500: train loss 5.3628, val loss 5.3250 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 3500: loss 5.2770, time 21166.37ms, mfu 0.05% +step 3750: train loss 5.2699, val loss 5.2557 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 3750: loss 5.5767, time 21296.24ms, mfu 0.05% +step 4000: train loss 5.2269, val loss 5.2305 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 4000: loss 5.3071, time 21255.31ms, mfu 0.05% +step 4250: train loss 5.1632, val loss 5.1754 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 4250: loss 4.7382, time 21286.74ms, mfu 0.05% +step 4500: train loss 5.1212, val loss 5.1127 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 4500: loss 4.8522, time 21091.84ms, mfu 0.05% +step 4750: train loss 5.0592, val loss 5.0662 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 4750: loss 5.1720, time 21327.84ms, mfu 0.05% +step 5000: train loss 4.9890, val loss 5.0277 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 5000: loss 5.1676, time 21265.95ms, mfu 0.05% +step 5250: train loss 4.9836, val loss 4.9690 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 5250: loss 4.6578, time 21303.21ms, mfu 0.05% +step 5500: train loss 4.9588, val loss 4.9222 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 5500: loss 5.1851, time 21180.68ms, mfu 0.05% +step 5750: train loss 4.8940, val loss 4.8532 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 5750: loss 5.1484, time 21940.34ms, mfu 0.05% +step 6000: train loss 4.8019, val loss 4.8065 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 6000: loss 4.7106, time 21598.70ms, mfu 0.05% +step 6250: train loss 4.7809, val loss 4.8126 +iter 6250: loss 4.5861, time 18306.99ms, mfu 0.05% +step 6500: train loss 4.7454, val loss 4.7797 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 6500: loss 4.9142, time 21256.21ms, mfu 0.05% +step 6750: train loss 4.7127, val loss 4.7193 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 6750: loss 4.8925, time 21093.66ms, mfu 0.05% +step 7000: train loss 4.6765, val loss 4.6755 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 7000: loss 4.6088, time 21166.67ms, mfu 0.05% +step 7250: train loss 4.6289, val loss 4.6578 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 7250: loss 4.7405, time 21232.99ms, mfu 0.05% +step 7500: train loss 4.6391, val loss 4.5923 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 7500: loss 4.5010, time 21217.93ms, mfu 0.05% +step 7750: train loss 4.5907, val loss 4.5824 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 7750: loss 4.7637, time 21161.81ms, mfu 0.05% +step 8000: train loss 4.5509, val loss 4.5768 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 8000: loss 4.6031, time 21015.95ms, mfu 0.05% +step 8250: train loss 4.5299, val loss 4.5582 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 8250: loss 4.3228, time 21301.57ms, mfu 0.05% +step 8500: train loss 4.5279, val loss 4.5370 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 8500: loss 4.2818, time 21229.69ms, mfu 0.05% +step 8750: train loss 4.4933, val loss 4.4939 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 8750: loss 4.5029, time 21200.09ms, mfu 0.05% +step 9000: train loss 4.5051, val loss 4.5099 +iter 9000: loss 4.5813, time 18200.09ms, mfu 0.05% +step 9250: train loss 4.4680, val loss 4.4966 +iter 9250: loss 4.5797, time 18317.47ms, mfu 0.05% +step 9500: train loss 4.4730, val loss 4.4836 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 9500: loss 4.4821, time 21064.44ms, mfu 0.05% +step 9750: train loss 4.4791, val loss 4.4549 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 9750: loss 4.3933, time 21244.18ms, mfu 0.05% +step 10000: train loss 4.4714, val loss 4.4988 +iter 10000: loss 4.5170, time 18176.59ms, mfu 0.05% diff --git a/results/logs/train_dot_scalar_seed42.log b/results/logs/train_dot_scalar_seed42.log new file mode 100644 index 0000000000000000000000000000000000000000..463d66574b39ef458622eff95e0d720e86bec39d --- /dev/null +++ b/results/logs/train_dot_scalar_seed42.log @@ -0,0 +1,127 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +C:\Users\brian\documents\nanogpt\train.py:199: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9681, val loss 10.9677 +iter 0: loss 10.9626, time 18647.49ms, mfu -100.00% +step 250: train loss 6.9906, val loss 6.9867 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 250: loss 7.0962, time 21308.37ms, mfu 0.05% +step 500: train loss 6.5039, val loss 6.5087 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 500: loss 6.1499, time 21167.45ms, mfu 0.05% +step 750: train loss 6.2735, val loss 6.3078 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 750: loss 6.6168, time 21275.41ms, mfu 0.05% +step 1000: train loss 6.1481, val loss 6.1451 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 1000: loss 5.7916, time 21233.02ms, mfu 0.05% +step 1250: train loss 5.9988, val loss 6.0153 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 1250: loss 6.5249, time 21337.18ms, mfu 0.05% +step 1500: train loss 5.9633, val loss 5.9553 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 1500: loss 5.4977, time 21335.38ms, mfu 0.05% +step 1750: train loss 5.8742, val loss 5.8868 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 1750: loss 6.1772, time 21331.42ms, mfu 0.05% +step 2000: train loss 5.7990, val loss 5.8110 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 2000: loss 5.5853, time 21116.69ms, mfu 0.05% +step 2250: train loss 5.6641, val loss 5.6732 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 2250: loss 5.7390, time 21348.21ms, mfu 0.05% +step 2500: train loss 5.5922, val loss 5.5843 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 2500: loss 4.8860, time 21361.69ms, mfu 0.05% +step 2750: train loss 5.5244, val loss 5.5175 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 2750: loss 5.5467, time 21305.10ms, mfu 0.05% +step 3000: train loss 5.4388, val loss 5.4234 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 3000: loss 5.4779, time 21211.68ms, mfu 0.05% +step 3250: train loss 5.3913, val loss 5.3489 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 3250: loss 5.5644, time 21037.03ms, mfu 0.05% +step 3500: train loss 5.2987, val loss 5.3487 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 3500: loss 5.4089, time 21170.59ms, mfu 0.05% +step 3750: train loss 5.2571, val loss 5.2814 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 3750: loss 5.3328, time 21216.56ms, mfu 0.05% +step 4000: train loss 5.2051, val loss 5.2084 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 4000: loss 5.8799, time 21404.62ms, mfu 0.05% +step 4250: train loss 5.1720, val loss 5.1545 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 4250: loss 4.8785, time 21308.94ms, mfu 0.05% +step 4500: train loss 5.0775, val loss 5.1040 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 4500: loss 5.1856, time 21308.70ms, mfu 0.05% +step 4750: train loss 5.0605, val loss 5.0700 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 4750: loss 5.1338, time 21359.81ms, mfu 0.05% +step 5000: train loss 5.0087, val loss 4.9828 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 5000: loss 5.4544, time 21146.30ms, mfu 0.05% +step 5250: train loss 4.9447, val loss 4.9457 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 5250: loss 5.0537, time 21354.80ms, mfu 0.05% +step 5500: train loss 4.9255, val loss 4.9118 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 5500: loss 4.6491, time 21249.03ms, mfu 0.05% +step 5750: train loss 4.8492, val loss 4.8652 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 5750: loss 5.0782, time 21325.95ms, mfu 0.05% +step 6000: train loss 4.7976, val loss 4.8044 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 6000: loss 4.7693, time 21360.74ms, mfu 0.05% +step 6250: train loss 4.7572, val loss 4.7711 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 6250: loss 4.4800, time 21287.63ms, mfu 0.05% +step 6500: train loss 4.7577, val loss 4.7323 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 6500: loss 4.6113, time 21369.67ms, mfu 0.05% +step 6750: train loss 4.6882, val loss 4.6777 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 6750: loss 4.6624, time 21247.58ms, mfu 0.05% +step 7000: train loss 4.6436, val loss 4.6699 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 7000: loss 4.6297, time 21446.17ms, mfu 0.05% +step 7250: train loss 4.6346, val loss 4.6322 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 7250: loss 4.8205, time 21333.58ms, mfu 0.05% +step 7500: train loss 4.6297, val loss 4.6124 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 7500: loss 4.7403, time 21309.14ms, mfu 0.05% +step 7750: train loss 4.6053, val loss 4.5851 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 7750: loss 4.7244, time 21221.59ms, mfu 0.05% +step 8000: train loss 4.5649, val loss 4.5659 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 8000: loss 4.5252, time 21287.37ms, mfu 0.05% +step 8250: train loss 4.5228, val loss 4.5465 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 8250: loss 4.3653, time 21211.66ms, mfu 0.05% +step 8500: train loss 4.5339, val loss 4.5606 +iter 8500: loss 4.4023, time 18219.80ms, mfu 0.05% +step 8750: train loss 4.4873, val loss 4.5081 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 8750: loss 4.6409, time 21057.47ms, mfu 0.05% +step 9000: train loss 4.5013, val loss 4.5242 +iter 9000: loss 4.4927, time 18225.28ms, mfu 0.05% +step 9250: train loss 4.4782, val loss 4.4717 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 9250: loss 4.6040, time 21442.24ms, mfu 0.05% +step 9500: train loss 4.4529, val loss 4.4682 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 9500: loss 4.2061, time 21375.45ms, mfu 0.05% +step 9750: train loss 4.4494, val loss 4.4646 +saving checkpoint to out-openwebtext_dot_gate_shared +iter 9750: loss 4.7640, time 21292.07ms, mfu 0.05% +step 10000: train loss 4.4507, val loss 4.4780 +iter 10000: loss 4.4617, time 18184.59ms, mfu 0.05% diff --git a/results/logs/train_full_q_w2000_seed123.log b/results/logs/train_full_q_w2000_seed123.log new file mode 100644 index 0000000000000000000000000000000000000000..6b17e5493b910996227282967304da12510675c8 --- /dev/null +++ b/results/logs/train_full_q_w2000_seed123.log @@ -0,0 +1,125 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 130.67M +C:\Users\brian\documents\nanogpt\train.py:200: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 131,039,232 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9713, val loss 10.9729 +iter 0: loss 10.9677, time 18573.81ms, mfu -100.00% +step 250: train loss 6.9200, val loss 6.9539 +saving checkpoint to out-openwebtext_phase2_full_q +iter 250: loss 7.0427, time 20174.25ms, mfu 0.05% +step 500: train loss 6.4904, val loss 6.5116 +saving checkpoint to out-openwebtext_phase2_full_q +iter 500: loss 6.5270, time 19892.09ms, mfu 0.05% +step 750: train loss 6.3090, val loss 6.3523 +saving checkpoint to out-openwebtext_phase2_full_q +iter 750: loss 6.5136, time 19918.94ms, mfu 0.05% +step 1000: train loss 6.1367, val loss 6.1683 +saving checkpoint to out-openwebtext_phase2_full_q +iter 1000: loss 6.0699, time 20068.42ms, mfu 0.05% +step 1250: train loss 6.0334, val loss 6.0040 +saving checkpoint to out-openwebtext_phase2_full_q +iter 1250: loss 5.6693, time 19906.35ms, mfu 0.05% +step 1500: train loss 5.9275, val loss 5.9622 +saving checkpoint to out-openwebtext_phase2_full_q +iter 1500: loss 6.0480, time 20144.67ms, mfu 0.05% +step 1750: train loss 5.8355, val loss 5.7857 +saving checkpoint to out-openwebtext_phase2_full_q +iter 1750: loss 5.6933, time 19932.43ms, mfu 0.05% +step 2000: train loss 5.7551, val loss 5.7453 +saving checkpoint to out-openwebtext_phase2_full_q +iter 2000: loss 5.6508, time 20406.01ms, mfu 0.05% +step 2250: train loss 5.5917, val loss 5.6228 +saving checkpoint to out-openwebtext_phase2_full_q +iter 2250: loss 5.4404, time 20129.43ms, mfu 0.05% +step 2500: train loss 5.5273, val loss 5.5383 +saving checkpoint to out-openwebtext_phase2_full_q +iter 2500: loss 5.3875, time 20060.87ms, mfu 0.05% +step 2750: train loss 5.4336, val loss 5.4431 +saving checkpoint to out-openwebtext_phase2_full_q +iter 2750: loss 5.2511, time 20059.80ms, mfu 0.05% +step 3000: train loss 5.3861, val loss 5.3897 +saving checkpoint to out-openwebtext_phase2_full_q +iter 3000: loss 5.1440, time 20726.36ms, mfu 0.05% +step 3250: train loss 5.3208, val loss 5.3240 +saving checkpoint to out-openwebtext_phase2_full_q +iter 3250: loss 4.9683, time 20207.69ms, mfu 0.05% +step 3500: train loss 5.2538, val loss 5.2891 +saving checkpoint to out-openwebtext_phase2_full_q +iter 3500: loss 5.2738, time 19873.97ms, mfu 0.05% +step 3750: train loss 5.2493, val loss 5.2183 +saving checkpoint to out-openwebtext_phase2_full_q +iter 3750: loss 5.0773, time 19910.71ms, mfu 0.05% +step 4000: train loss 5.1684, val loss 5.1650 +saving checkpoint to out-openwebtext_phase2_full_q +iter 4000: loss 5.3927, time 19922.63ms, mfu 0.05% +step 4250: train loss 5.1002, val loss 5.1169 +saving checkpoint to out-openwebtext_phase2_full_q +iter 4250: loss 5.2028, time 20207.73ms, mfu 0.05% +step 4500: train loss 5.0632, val loss 5.0539 +saving checkpoint to out-openwebtext_phase2_full_q +iter 4500: loss 5.0169, time 20047.79ms, mfu 0.05% +step 4750: train loss 4.9891, val loss 5.0003 +saving checkpoint to out-openwebtext_phase2_full_q +iter 4750: loss 5.0830, time 19892.41ms, mfu 0.05% +step 5000: train loss 4.9133, val loss 4.9434 +saving checkpoint to out-openwebtext_phase2_full_q +iter 5000: loss 5.0528, time 20224.11ms, mfu 0.05% +step 5250: train loss 4.8899, val loss 4.9215 +saving checkpoint to out-openwebtext_phase2_full_q +iter 5250: loss 5.0005, time 19933.39ms, mfu 0.05% +step 5500: train loss 4.8269, val loss 4.8164 +saving checkpoint to out-openwebtext_phase2_full_q +iter 5500: loss 4.7725, time 19898.55ms, mfu 0.06% +step 5750: train loss 4.7797, val loss 4.8168 +iter 5750: loss 5.3277, time 18386.32ms, mfu 0.06% +step 6000: train loss 4.7351, val loss 4.7485 +saving checkpoint to out-openwebtext_phase2_full_q +iter 6000: loss 5.0590, time 20152.61ms, mfu 0.06% +step 6250: train loss 4.7015, val loss 4.7270 +saving checkpoint to out-openwebtext_phase2_full_q +iter 6250: loss 4.7737, time 20133.35ms, mfu 0.06% +step 6500: train loss 4.6714, val loss 4.6624 +saving checkpoint to out-openwebtext_phase2_full_q +iter 6500: loss 4.4953, time 19932.17ms, mfu 0.06% +step 6750: train loss 4.6536, val loss 4.6285 +saving checkpoint to out-openwebtext_phase2_full_q +iter 6750: loss 4.5085, time 19870.36ms, mfu 0.06% +step 7000: train loss 4.6302, val loss 4.6392 +iter 7000: loss 4.7237, time 18332.20ms, mfu 0.06% +step 7250: train loss 4.5923, val loss 4.5871 +saving checkpoint to out-openwebtext_phase2_full_q +iter 7250: loss 4.6132, time 20007.23ms, mfu 0.06% +step 7500: train loss 4.5423, val loss 4.5773 +saving checkpoint to out-openwebtext_phase2_full_q +iter 7500: loss 4.5123, time 19905.38ms, mfu 0.06% +step 7750: train loss 4.5411, val loss 4.5566 +saving checkpoint to out-openwebtext_phase2_full_q +iter 7750: loss 4.4795, time 20305.05ms, mfu 0.06% +step 8000: train loss 4.5532, val loss 4.5453 +saving checkpoint to out-openwebtext_phase2_full_q +iter 8000: loss 4.5641, time 20408.25ms, mfu 0.06% +step 8250: train loss 4.4598, val loss 4.4973 +saving checkpoint to out-openwebtext_phase2_full_q +iter 8250: loss 4.4820, time 20412.74ms, mfu 0.06% +step 8500: train loss 4.5027, val loss 4.4939 +saving checkpoint to out-openwebtext_phase2_full_q +iter 8500: loss 4.5136, time 19886.66ms, mfu 0.06% +step 8750: train loss 4.4410, val loss 4.5044 +iter 8750: loss 4.7138, time 18365.07ms, mfu 0.06% +step 9000: train loss 4.4552, val loss 4.4572 +saving checkpoint to out-openwebtext_phase2_full_q +iter 9000: loss 4.2683, time 20019.08ms, mfu 0.06% +step 9250: train loss 4.4402, val loss 4.4383 +saving checkpoint to out-openwebtext_phase2_full_q +iter 9250: loss 4.2895, time 19935.00ms, mfu 0.06% +step 9500: train loss 4.4269, val loss 4.4202 +saving checkpoint to out-openwebtext_phase2_full_q +iter 9500: loss 4.4367, time 20025.16ms, mfu 0.06% +step 9750: train loss 4.4148, val loss 4.4335 +iter 9750: loss 4.4719, time 18345.40ms, mfu 0.06% +step 10000: train loss 4.4522, val loss 4.4233 +iter 10000: loss 4.5422, time 18340.33ms, mfu 0.06% diff --git a/results/logs/train_full_q_w2000_seed1337.log b/results/logs/train_full_q_w2000_seed1337.log new file mode 100644 index 0000000000000000000000000000000000000000..adeb6ef9490b63159e726b7049d58f78a5155e77 --- /dev/null +++ b/results/logs/train_full_q_w2000_seed1337.log @@ -0,0 +1,127 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 130.67M +C:\Users\brian\documents\nanogpt\train.py:200: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 131,039,232 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9660, val loss 10.9625 +iter 0: loss 10.9583, time 18558.55ms, mfu -100.00% +step 250: train loss 6.9473, val loss 6.9369 +saving checkpoint to out-openwebtext_phase2_full_q +iter 250: loss 6.7696, time 20050.98ms, mfu 0.06% +step 500: train loss 6.5310, val loss 6.5194 +saving checkpoint to out-openwebtext_phase2_full_q +iter 500: loss 6.1739, time 20043.64ms, mfu 0.06% +step 750: train loss 6.2891, val loss 6.2950 +saving checkpoint to out-openwebtext_phase2_full_q +iter 750: loss 6.5774, time 20356.93ms, mfu 0.05% +step 1000: train loss 6.1364, val loss 6.1558 +saving checkpoint to out-openwebtext_phase2_full_q +iter 1000: loss 6.1453, time 19961.89ms, mfu 0.05% +step 1250: train loss 6.0300, val loss 6.0361 +saving checkpoint to out-openwebtext_phase2_full_q +iter 1250: loss 5.8688, time 19936.24ms, mfu 0.06% +step 1500: train loss 5.9725, val loss 5.9466 +saving checkpoint to out-openwebtext_phase2_full_q +iter 1500: loss 6.1110, time 20278.62ms, mfu 0.05% +step 1750: train loss 5.8133, val loss 5.8064 +saving checkpoint to out-openwebtext_phase2_full_q +iter 1750: loss 5.4822, time 19913.74ms, mfu 0.06% +step 2000: train loss 5.7269, val loss 5.7262 +saving checkpoint to out-openwebtext_phase2_full_q +iter 2000: loss 5.6340, time 19956.60ms, mfu 0.06% +step 2250: train loss 5.6559, val loss 5.6554 +saving checkpoint to out-openwebtext_phase2_full_q +iter 2250: loss 5.9406, time 19873.51ms, mfu 0.06% +step 2500: train loss 5.5680, val loss 5.5414 +saving checkpoint to out-openwebtext_phase2_full_q +iter 2500: loss 6.1138, time 19982.82ms, mfu 0.06% +step 2750: train loss 5.4207, val loss 5.4711 +saving checkpoint to out-openwebtext_phase2_full_q +iter 2750: loss 5.7863, time 19902.03ms, mfu 0.06% +step 3000: train loss 5.4095, val loss 5.4300 +saving checkpoint to out-openwebtext_phase2_full_q +iter 3000: loss 4.9353, time 20243.84ms, mfu 0.06% +step 3250: train loss 5.3303, val loss 5.3424 +saving checkpoint to out-openwebtext_phase2_full_q +iter 3250: loss 5.2514, time 19945.27ms, mfu 0.06% +step 3500: train loss 5.2831, val loss 5.2869 +saving checkpoint to out-openwebtext_phase2_full_q +iter 3500: loss 5.0642, time 19965.03ms, mfu 0.06% +step 3750: train loss 5.1805, val loss 5.1927 +saving checkpoint to out-openwebtext_phase2_full_q +iter 3750: loss 5.2106, time 19896.07ms, mfu 0.06% +step 4000: train loss 5.1891, val loss 5.1800 +saving checkpoint to out-openwebtext_phase2_full_q +iter 4000: loss 5.3505, time 21028.58ms, mfu 0.05% +step 4250: train loss 5.1296, val loss 5.0907 +saving checkpoint to out-openwebtext_phase2_full_q +iter 4250: loss 5.5074, time 20196.86ms, mfu 0.05% +step 4500: train loss 5.0976, val loss 5.1121 +iter 4500: loss 4.8459, time 18342.18ms, mfu 0.06% +step 4750: train loss 5.0110, val loss 5.0421 +saving checkpoint to out-openwebtext_phase2_full_q +iter 4750: loss 4.9572, time 20005.75ms, mfu 0.06% +step 5000: train loss 5.0186, val loss 4.9513 +saving checkpoint to out-openwebtext_phase2_full_q +iter 5000: loss 5.0625, time 19872.94ms, mfu 0.06% +step 5250: train loss 4.9432, val loss 4.9420 +saving checkpoint to out-openwebtext_phase2_full_q +iter 5250: loss 5.1044, time 19830.62ms, mfu 0.06% +step 5500: train loss 4.8891, val loss 4.8871 +saving checkpoint to out-openwebtext_phase2_full_q +iter 5500: loss 4.8232, time 20102.97ms, mfu 0.06% +step 5750: train loss 4.8190, val loss 4.8485 +saving checkpoint to out-openwebtext_phase2_full_q +iter 5750: loss 4.8374, time 19856.18ms, mfu 0.06% +step 6000: train loss 4.7599, val loss 4.7673 +saving checkpoint to out-openwebtext_phase2_full_q +iter 6000: loss 4.3405, time 20239.04ms, mfu 0.06% +step 6250: train loss 4.7316, val loss 4.7404 +saving checkpoint to out-openwebtext_phase2_full_q +iter 6250: loss 4.3561, time 20511.03ms, mfu 0.06% +step 6500: train loss 4.6932, val loss 4.7066 +saving checkpoint to out-openwebtext_phase2_full_q +iter 6500: loss 4.7791, time 20664.93ms, mfu 0.05% +step 6750: train loss 4.6771, val loss 4.6720 +saving checkpoint to out-openwebtext_phase2_full_q +iter 6750: loss 5.2330, time 19908.74ms, mfu 0.06% +step 7000: train loss 4.6579, val loss 4.6433 +saving checkpoint to out-openwebtext_phase2_full_q +iter 7000: loss 4.6831, time 20100.97ms, mfu 0.06% +step 7250: train loss 4.5865, val loss 4.6237 +saving checkpoint to out-openwebtext_phase2_full_q +iter 7250: loss 4.6677, time 19847.75ms, mfu 0.06% +step 7500: train loss 4.5816, val loss 4.6184 +saving checkpoint to out-openwebtext_phase2_full_q +iter 7500: loss 4.7193, time 20207.11ms, mfu 0.06% +step 7750: train loss 4.5685, val loss 4.5727 +saving checkpoint to out-openwebtext_phase2_full_q +iter 7750: loss 4.7023, time 20015.83ms, mfu 0.06% +step 8000: train loss 4.5373, val loss 4.5514 +saving checkpoint to out-openwebtext_phase2_full_q +iter 8000: loss 4.7750, time 19943.16ms, mfu 0.06% +step 8250: train loss 4.5014, val loss 4.5230 +saving checkpoint to out-openwebtext_phase2_full_q +iter 8250: loss 4.7540, time 20311.80ms, mfu 0.05% +step 8500: train loss 4.4956, val loss 4.4980 +saving checkpoint to out-openwebtext_phase2_full_q +iter 8500: loss 4.4776, time 20159.96ms, mfu 0.05% +step 8750: train loss 4.4938, val loss 4.4949 +saving checkpoint to out-openwebtext_phase2_full_q +iter 8750: loss 4.5145, time 20344.70ms, mfu 0.05% +step 9000: train loss 4.4365, val loss 4.4958 +iter 9000: loss 4.5904, time 18355.51ms, mfu 0.06% +step 9250: train loss 4.4650, val loss 4.4565 +saving checkpoint to out-openwebtext_phase2_full_q +iter 9250: loss 4.3938, time 19900.51ms, mfu 0.06% +step 9500: train loss 4.4322, val loss 4.4429 +saving checkpoint to out-openwebtext_phase2_full_q +iter 9500: loss 4.8495, time 19854.91ms, mfu 0.06% +step 9750: train loss 4.4461, val loss 4.4477 +iter 9750: loss 4.4670, time 18351.33ms, mfu 0.06% +step 10000: train loss 4.3944, val loss 4.4099 +saving checkpoint to out-openwebtext_phase2_full_q +iter 10000: loss 4.9555, time 19942.68ms, mfu 0.06% diff --git a/results/logs/train_full_q_w2000_seed42.log b/results/logs/train_full_q_w2000_seed42.log new file mode 100644 index 0000000000000000000000000000000000000000..89a790350b47f845d2077325caedd68193ce8e0f --- /dev/null +++ b/results/logs/train_full_q_w2000_seed42.log @@ -0,0 +1,127 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 130.67M +C:\Users\brian\documents\nanogpt\train.py:200: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 131,039,232 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9269, val loss 10.9291 +iter 0: loss 10.9415, time 18768.77ms, mfu -100.00% +step 250: train loss 6.9157, val loss 6.9358 +saving checkpoint to out-openwebtext_phase2_full_q +iter 250: loss 6.5246, time 19819.31ms, mfu 0.06% +step 500: train loss 6.5153, val loss 6.5234 +saving checkpoint to out-openwebtext_phase2_full_q +iter 500: loss 6.4254, time 20413.40ms, mfu 0.06% +step 750: train loss 6.3115, val loss 6.3058 +saving checkpoint to out-openwebtext_phase2_full_q +iter 750: loss 6.6303, time 20432.68ms, mfu 0.06% +step 1000: train loss 6.1464, val loss 6.1576 +saving checkpoint to out-openwebtext_phase2_full_q +iter 1000: loss 6.1927, time 20415.68ms, mfu 0.06% +step 1250: train loss 6.0286, val loss 6.0112 +saving checkpoint to out-openwebtext_phase2_full_q +iter 1250: loss 6.0488, time 20646.10ms, mfu 0.06% +step 1500: train loss 5.9168, val loss 5.9803 +saving checkpoint to out-openwebtext_phase2_full_q +iter 1500: loss 5.6084, time 20535.11ms, mfu 0.05% +step 1750: train loss 5.8440, val loss 5.8979 +saving checkpoint to out-openwebtext_phase2_full_q +iter 1750: loss 5.9140, time 20801.56ms, mfu 0.05% +step 2000: train loss 5.7802, val loss 5.7484 +saving checkpoint to out-openwebtext_phase2_full_q +iter 2000: loss 5.8332, time 20707.49ms, mfu 0.05% +step 2250: train loss 5.6313, val loss 5.6360 +saving checkpoint to out-openwebtext_phase2_full_q +iter 2250: loss 5.6091, time 20774.11ms, mfu 0.05% +step 2500: train loss 5.5565, val loss 5.5537 +saving checkpoint to out-openwebtext_phase2_full_q +iter 2500: loss 5.7505, time 20828.74ms, mfu 0.05% +step 2750: train loss 5.4762, val loss 5.4626 +saving checkpoint to out-openwebtext_phase2_full_q +iter 2750: loss 5.5355, time 20887.93ms, mfu 0.05% +step 3000: train loss 5.3795, val loss 5.3750 +saving checkpoint to out-openwebtext_phase2_full_q +iter 3000: loss 5.9369, time 20574.24ms, mfu 0.05% +step 3250: train loss 5.3360, val loss 5.3406 +saving checkpoint to out-openwebtext_phase2_full_q +iter 3250: loss 5.3131, time 20645.70ms, mfu 0.05% +step 3500: train loss 5.2404, val loss 5.2371 +saving checkpoint to out-openwebtext_phase2_full_q +iter 3500: loss 5.6752, time 20720.87ms, mfu 0.05% +step 3750: train loss 5.2192, val loss 5.2130 +saving checkpoint to out-openwebtext_phase2_full_q +iter 3750: loss 5.3480, time 20460.51ms, mfu 0.05% +step 4000: train loss 5.1599, val loss 5.1781 +saving checkpoint to out-openwebtext_phase2_full_q +iter 4000: loss 5.1636, time 20639.25ms, mfu 0.05% +step 4250: train loss 5.1018, val loss 5.1065 +saving checkpoint to out-openwebtext_phase2_full_q +iter 4250: loss 4.9705, time 20388.05ms, mfu 0.05% +step 4500: train loss 5.0578, val loss 5.0497 +saving checkpoint to out-openwebtext_phase2_full_q +iter 4500: loss 4.8941, time 20381.67ms, mfu 0.05% +step 4750: train loss 4.9760, val loss 5.0097 +saving checkpoint to out-openwebtext_phase2_full_q +iter 4750: loss 4.8812, time 20692.87ms, mfu 0.05% +step 5000: train loss 4.9444, val loss 4.9536 +saving checkpoint to out-openwebtext_phase2_full_q +iter 5000: loss 4.9710, time 21030.81ms, mfu 0.05% +step 5250: train loss 4.8975, val loss 4.8908 +saving checkpoint to out-openwebtext_phase2_full_q +iter 5250: loss 4.8454, time 20732.94ms, mfu 0.05% +step 5500: train loss 4.8381, val loss 4.8449 +saving checkpoint to out-openwebtext_phase2_full_q +iter 5500: loss 5.0014, time 20535.57ms, mfu 0.05% +step 5750: train loss 4.8002, val loss 4.7945 +saving checkpoint to out-openwebtext_phase2_full_q +iter 5750: loss 4.8967, time 20759.84ms, mfu 0.05% +step 6000: train loss 4.7704, val loss 4.7541 +saving checkpoint to out-openwebtext_phase2_full_q +iter 6000: loss 4.5533, time 20766.04ms, mfu 0.05% +step 6250: train loss 4.7160, val loss 4.7138 +saving checkpoint to out-openwebtext_phase2_full_q +iter 6250: loss 4.5355, time 20380.61ms, mfu 0.05% +step 6500: train loss 4.6800, val loss 4.7150 +iter 6500: loss 4.5030, time 18747.44ms, mfu 0.05% +step 6750: train loss 4.6510, val loss 4.6535 +saving checkpoint to out-openwebtext_phase2_full_q +iter 6750: loss 4.6419, time 20724.69ms, mfu 0.05% +step 7000: train loss 4.6137, val loss 4.6416 +saving checkpoint to out-openwebtext_phase2_full_q +iter 7000: loss 4.8848, time 20686.09ms, mfu 0.05% +step 7250: train loss 4.6022, val loss 4.6013 +saving checkpoint to out-openwebtext_phase2_full_q +iter 7250: loss 5.0602, time 21527.15ms, mfu 0.05% +step 7500: train loss 4.5949, val loss 4.5894 +saving checkpoint to out-openwebtext_phase2_full_q +iter 7500: loss 4.5598, time 20582.59ms, mfu 0.05% +step 7750: train loss 4.5372, val loss 4.5609 +saving checkpoint to out-openwebtext_phase2_full_q +iter 7750: loss 4.9583, time 20892.54ms, mfu 0.05% +step 8000: train loss 4.5330, val loss 4.5076 +saving checkpoint to out-openwebtext_phase2_full_q +iter 8000: loss 4.1355, time 20653.60ms, mfu 0.05% +step 8250: train loss 4.5232, val loss 4.4904 +saving checkpoint to out-openwebtext_phase2_full_q +iter 8250: loss 4.6589, time 20716.21ms, mfu 0.05% +step 8500: train loss 4.4848, val loss 4.4815 +saving checkpoint to out-openwebtext_phase2_full_q +iter 8500: loss 4.5819, time 20418.22ms, mfu 0.05% +step 8750: train loss 4.4773, val loss 4.4611 +saving checkpoint to out-openwebtext_phase2_full_q +iter 8750: loss 4.3585, time 20478.49ms, mfu 0.05% +step 9000: train loss 4.4546, val loss 4.4527 +saving checkpoint to out-openwebtext_phase2_full_q +iter 9000: loss 4.5609, time 20492.35ms, mfu 0.05% +step 9250: train loss 4.4542, val loss 4.4249 +saving checkpoint to out-openwebtext_phase2_full_q +iter 9250: loss 4.2450, time 20659.27ms, mfu 0.05% +step 9500: train loss 4.4222, val loss 4.4424 +iter 9500: loss 4.2153, time 18726.67ms, mfu 0.05% +step 9750: train loss 4.4278, val loss 4.4408 +iter 9750: loss 4.2734, time 18773.39ms, mfu 0.05% +step 10000: train loss 4.4156, val loss 4.4027 +saving checkpoint to out-openwebtext_phase2_full_q +iter 10000: loss 4.6601, time 20421.11ms, mfu 0.05% diff --git a/results/logs/train_full_x_seed123.log b/results/logs/train_full_x_seed123.log new file mode 100644 index 0000000000000000000000000000000000000000..496b433bf834de06dbd9949ab1c6bf5f47f03b4f --- /dev/null +++ b/results/logs/train_full_x_seed123.log @@ -0,0 +1,8 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 130.67M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 131,039,232 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True diff --git a/results/logs/train_full_x_seed1337.log b/results/logs/train_full_x_seed1337.log new file mode 100644 index 0000000000000000000000000000000000000000..2e01767a2d364fafeb4fce55435cbce338826ba5 --- /dev/null +++ b/results/logs/train_full_x_seed1337.log @@ -0,0 +1,127 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 130.67M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 131,039,232 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9622, val loss 10.9587 +iter 0: loss 10.9558, time 19309.82ms, mfu -100.00% +step 250: train loss 6.9475, val loss 6.9370 +saving checkpoint to out-openwebtext_rerun_shared +iter 250: loss 6.7684, time 20799.91ms, mfu 0.05% +step 500: train loss 6.5307, val loss 6.5190 +saving checkpoint to out-openwebtext_rerun_shared +iter 500: loss 6.1714, time 21800.07ms, mfu 0.05% +step 750: train loss 6.2888, val loss 6.2941 +saving checkpoint to out-openwebtext_rerun_shared +iter 750: loss 6.5801, time 22081.82ms, mfu 0.05% +step 1000: train loss 6.1255, val loss 6.1427 +saving checkpoint to out-openwebtext_rerun_shared +iter 1000: loss 6.1494, time 23116.42ms, mfu 0.05% +step 1250: train loss 6.0369, val loss 6.0447 +saving checkpoint to out-openwebtext_rerun_shared +iter 1250: loss 5.8991, time 22164.74ms, mfu 0.05% +step 1500: train loss 5.9685, val loss 5.9420 +saving checkpoint to out-openwebtext_rerun_shared +iter 1500: loss 6.0893, time 22073.11ms, mfu 0.05% +step 1750: train loss 5.8133, val loss 5.8078 +saving checkpoint to out-openwebtext_rerun_shared +iter 1750: loss 5.4630, time 22096.70ms, mfu 0.05% +step 2000: train loss 5.7433, val loss 5.7429 +saving checkpoint to out-openwebtext_rerun_shared +iter 2000: loss 5.6494, time 22009.14ms, mfu 0.05% +step 2250: train loss 5.6492, val loss 5.6433 +saving checkpoint to out-openwebtext_rerun_shared +iter 2250: loss 5.9185, time 22138.39ms, mfu 0.05% +step 2500: train loss 5.5626, val loss 5.5347 +saving checkpoint to out-openwebtext_rerun_shared +iter 2500: loss 6.0863, time 22099.61ms, mfu 0.05% +step 2750: train loss 5.4159, val loss 5.4615 +saving checkpoint to out-openwebtext_rerun_shared +iter 2750: loss 5.7968, time 22166.26ms, mfu 0.05% +step 3000: train loss 5.3953, val loss 5.4148 +saving checkpoint to out-openwebtext_rerun_shared +iter 3000: loss 4.8958, time 22133.76ms, mfu 0.05% +step 3250: train loss 5.3116, val loss 5.3244 +saving checkpoint to out-openwebtext_rerun_shared +iter 3250: loss 5.2315, time 22169.38ms, mfu 0.05% +step 3500: train loss 5.2704, val loss 5.2726 +saving checkpoint to out-openwebtext_rerun_shared +iter 3500: loss 5.0322, time 22041.67ms, mfu 0.05% +step 3750: train loss 5.1632, val loss 5.1729 +saving checkpoint to out-openwebtext_rerun_shared +iter 3750: loss 5.1488, time 22045.19ms, mfu 0.05% +step 4000: train loss 5.1663, val loss 5.1553 +saving checkpoint to out-openwebtext_rerun_shared +iter 4000: loss 5.3464, time 21972.18ms, mfu 0.05% +step 4250: train loss 5.1070, val loss 5.0698 +saving checkpoint to out-openwebtext_rerun_shared +iter 4250: loss 5.5115, time 21980.61ms, mfu 0.05% +step 4500: train loss 5.0632, val loss 5.0741 +iter 4500: loss 4.8094, time 18906.77ms, mfu 0.05% +step 4750: train loss 4.9833, val loss 5.0168 +saving checkpoint to out-openwebtext_rerun_shared +iter 4750: loss 4.9448, time 22229.91ms, mfu 0.05% +step 5000: train loss 4.9854, val loss 4.9203 +saving checkpoint to out-openwebtext_rerun_shared +iter 5000: loss 5.0599, time 22048.05ms, mfu 0.05% +step 5250: train loss 4.9081, val loss 4.9042 +saving checkpoint to out-openwebtext_rerun_shared +iter 5250: loss 5.0660, time 22051.86ms, mfu 0.05% +step 5500: train loss 4.8525, val loss 4.8534 +saving checkpoint to out-openwebtext_rerun_shared +iter 5500: loss 4.7744, time 22053.63ms, mfu 0.05% +step 5750: train loss 4.7794, val loss 4.8107 +saving checkpoint to out-openwebtext_rerun_shared +iter 5750: loss 4.8149, time 22046.99ms, mfu 0.05% +step 6000: train loss 4.7210, val loss 4.7304 +saving checkpoint to out-openwebtext_rerun_shared +iter 6000: loss 4.2898, time 21992.04ms, mfu 0.05% +step 6250: train loss 4.6971, val loss 4.7060 +saving checkpoint to out-openwebtext_rerun_shared +iter 6250: loss 4.3267, time 21995.14ms, mfu 0.05% +step 6500: train loss 4.6595, val loss 4.6745 +saving checkpoint to out-openwebtext_rerun_shared +iter 6500: loss 4.7315, time 22150.84ms, mfu 0.05% +step 6750: train loss 4.6432, val loss 4.6403 +saving checkpoint to out-openwebtext_rerun_shared +iter 6750: loss 5.1942, time 22180.68ms, mfu 0.05% +step 7000: train loss 4.6246, val loss 4.6116 +saving checkpoint to out-openwebtext_rerun_shared +iter 7000: loss 4.6395, time 22137.66ms, mfu 0.05% +step 7250: train loss 4.5563, val loss 4.5964 +saving checkpoint to out-openwebtext_rerun_shared +iter 7250: loss 4.6274, time 22097.22ms, mfu 0.05% +step 7500: train loss 4.5485, val loss 4.5865 +saving checkpoint to out-openwebtext_rerun_shared +iter 7500: loss 4.6290, time 22046.79ms, mfu 0.05% +step 7750: train loss 4.5392, val loss 4.5414 +saving checkpoint to out-openwebtext_rerun_shared +iter 7750: loss 4.7038, time 22026.53ms, mfu 0.05% +step 8000: train loss 4.5076, val loss 4.5206 +saving checkpoint to out-openwebtext_rerun_shared +iter 8000: loss 4.7464, time 22381.47ms, mfu 0.05% +step 8250: train loss 4.4756, val loss 4.4942 +saving checkpoint to out-openwebtext_rerun_shared +iter 8250: loss 4.7371, time 22057.15ms, mfu 0.05% +step 8500: train loss 4.4653, val loss 4.4707 +saving checkpoint to out-openwebtext_rerun_shared +iter 8500: loss 4.4583, time 22073.25ms, mfu 0.05% +step 8750: train loss 4.4675, val loss 4.4640 +saving checkpoint to out-openwebtext_rerun_shared +iter 8750: loss 4.5219, time 21906.75ms, mfu 0.05% +step 9000: train loss 4.4072, val loss 4.4665 +iter 9000: loss 4.4988, time 19042.02ms, mfu 0.05% +step 9250: train loss 4.4372, val loss 4.4327 +saving checkpoint to out-openwebtext_rerun_shared +iter 9250: loss 4.3705, time 22165.08ms, mfu 0.05% +step 9500: train loss 4.4077, val loss 4.4170 +saving checkpoint to out-openwebtext_rerun_shared +iter 9500: loss 4.8012, time 23740.18ms, mfu 0.05% +step 9750: train loss 4.4187, val loss 4.4192 +iter 9750: loss 4.4308, time 18830.98ms, mfu 0.05% +step 10000: train loss 4.3686, val loss 4.3842 +saving checkpoint to out-openwebtext_rerun_shared +iter 10000: loss 4.9276, time 20411.83ms, mfu 0.05% diff --git a/results/logs/train_full_x_seed42.log b/results/logs/train_full_x_seed42.log new file mode 100644 index 0000000000000000000000000000000000000000..904809083513ec7df643cce06b569b2ab623c261 --- /dev/null +++ b/results/logs/train_full_x_seed42.log @@ -0,0 +1,127 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 130.67M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 131,039,232 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9285, val loss 10.9307 +iter 0: loss 10.9425, time 18613.26ms, mfu -100.00% +step 250: train loss 6.9144, val loss 6.9347 +saving checkpoint to out-openwebtext_rerun_shared +iter 250: loss 6.5241, time 20016.14ms, mfu 0.06% +step 500: train loss 6.5146, val loss 6.5229 +saving checkpoint to out-openwebtext_rerun_shared +iter 500: loss 6.4255, time 21000.03ms, mfu 0.05% +step 750: train loss 6.3131, val loss 6.3068 +saving checkpoint to out-openwebtext_rerun_shared +iter 750: loss 6.6228, time 20501.28ms, mfu 0.05% +step 1000: train loss 6.1492, val loss 6.1616 +saving checkpoint to out-openwebtext_rerun_shared +iter 1000: loss 6.2063, time 20952.72ms, mfu 0.05% +step 1250: train loss 6.0261, val loss 6.0082 +saving checkpoint to out-openwebtext_rerun_shared +iter 1250: loss 5.9983, time 20582.42ms, mfu 0.05% +step 1500: train loss 5.9283, val loss 5.9923 +saving checkpoint to out-openwebtext_rerun_shared +iter 1500: loss 5.6249, time 20954.07ms, mfu 0.05% +step 1750: train loss 5.8572, val loss 5.9159 +saving checkpoint to out-openwebtext_rerun_shared +iter 1750: loss 5.8913, time 21191.69ms, mfu 0.05% +step 2000: train loss 5.7952, val loss 5.7668 +saving checkpoint to out-openwebtext_rerun_shared +iter 2000: loss 5.8754, time 20827.53ms, mfu 0.05% +step 2250: train loss 5.6396, val loss 5.6429 +saving checkpoint to out-openwebtext_rerun_shared +iter 2250: loss 5.6144, time 20627.01ms, mfu 0.05% +step 2500: train loss 5.5551, val loss 5.5489 +saving checkpoint to out-openwebtext_rerun_shared +iter 2500: loss 5.7884, time 20646.33ms, mfu 0.05% +step 2750: train loss 5.4817, val loss 5.4644 +saving checkpoint to out-openwebtext_rerun_shared +iter 2750: loss 5.5370, time 20846.94ms, mfu 0.05% +step 3000: train loss 5.3793, val loss 5.3767 +saving checkpoint to out-openwebtext_rerun_shared +iter 3000: loss 5.9443, time 20585.55ms, mfu 0.05% +step 3250: train loss 5.3299, val loss 5.3340 +saving checkpoint to out-openwebtext_rerun_shared +iter 3250: loss 5.2913, time 20780.19ms, mfu 0.05% +step 3500: train loss 5.2434, val loss 5.2421 +saving checkpoint to out-openwebtext_rerun_shared +iter 3500: loss 5.6948, time 21408.23ms, mfu 0.05% +step 3750: train loss 5.2180, val loss 5.2112 +saving checkpoint to out-openwebtext_rerun_shared +iter 3750: loss 5.3472, time 20982.33ms, mfu 0.05% +step 4000: train loss 5.1540, val loss 5.1706 +saving checkpoint to out-openwebtext_rerun_shared +iter 4000: loss 5.1421, time 21413.50ms, mfu 0.05% +step 4250: train loss 5.0985, val loss 5.1041 +saving checkpoint to out-openwebtext_rerun_shared +iter 4250: loss 4.9729, time 21169.48ms, mfu 0.05% +step 4500: train loss 5.0545, val loss 5.0480 +saving checkpoint to out-openwebtext_rerun_shared +iter 4500: loss 4.8987, time 21249.05ms, mfu 0.05% +step 4750: train loss 4.9629, val loss 4.9967 +saving checkpoint to out-openwebtext_rerun_shared +iter 4750: loss 4.9016, time 20752.09ms, mfu 0.05% +step 5000: train loss 4.9408, val loss 4.9471 +saving checkpoint to out-openwebtext_rerun_shared +iter 5000: loss 4.9525, time 20647.30ms, mfu 0.05% +step 5250: train loss 4.8890, val loss 4.8812 +saving checkpoint to out-openwebtext_rerun_shared +iter 5250: loss 4.8073, time 21255.21ms, mfu 0.05% +step 5500: train loss 4.8295, val loss 4.8371 +saving checkpoint to out-openwebtext_rerun_shared +iter 5500: loss 5.0168, time 20618.01ms, mfu 0.05% +step 5750: train loss 4.7918, val loss 4.7844 +saving checkpoint to out-openwebtext_rerun_shared +iter 5750: loss 4.8882, time 20503.20ms, mfu 0.05% +step 6000: train loss 4.7652, val loss 4.7484 +saving checkpoint to out-openwebtext_rerun_shared +iter 6000: loss 4.5550, time 20683.87ms, mfu 0.05% +step 6250: train loss 4.7062, val loss 4.7031 +saving checkpoint to out-openwebtext_rerun_shared +iter 6250: loss 4.5142, time 20862.16ms, mfu 0.05% +step 6500: train loss 4.6721, val loss 4.7066 +iter 6500: loss 4.4941, time 19018.76ms, mfu 0.05% +step 6750: train loss 4.6424, val loss 4.6431 +saving checkpoint to out-openwebtext_rerun_shared +iter 6750: loss 4.6010, time 20650.63ms, mfu 0.05% +step 7000: train loss 4.6049, val loss 4.6303 +saving checkpoint to out-openwebtext_rerun_shared +iter 7000: loss 4.8959, time 20619.01ms, mfu 0.05% +step 7250: train loss 4.5929, val loss 4.5929 +saving checkpoint to out-openwebtext_rerun_shared +iter 7250: loss 5.0342, time 21849.97ms, mfu 0.05% +step 7500: train loss 4.5827, val loss 4.5758 +saving checkpoint to out-openwebtext_rerun_shared +iter 7500: loss 4.5337, time 21527.56ms, mfu 0.05% +step 7750: train loss 4.5223, val loss 4.5464 +saving checkpoint to out-openwebtext_rerun_shared +iter 7750: loss 4.8966, time 20801.43ms, mfu 0.05% +step 8000: train loss 4.5235, val loss 4.4956 +saving checkpoint to out-openwebtext_rerun_shared +iter 8000: loss 4.1206, time 21340.77ms, mfu 0.05% +step 8250: train loss 4.5100, val loss 4.4775 +saving checkpoint to out-openwebtext_rerun_shared +iter 8250: loss 4.6659, time 20915.50ms, mfu 0.05% +step 8500: train loss 4.4690, val loss 4.4648 +saving checkpoint to out-openwebtext_rerun_shared +iter 8500: loss 4.5679, time 21302.63ms, mfu 0.05% +step 8750: train loss 4.4647, val loss 4.4492 +saving checkpoint to out-openwebtext_rerun_shared +iter 8750: loss 4.3315, time 20571.26ms, mfu 0.05% +step 9000: train loss 4.4418, val loss 4.4417 +saving checkpoint to out-openwebtext_rerun_shared +iter 9000: loss 4.5420, time 21047.08ms, mfu 0.05% +step 9250: train loss 4.4395, val loss 4.4142 +saving checkpoint to out-openwebtext_rerun_shared +iter 9250: loss 4.2225, time 22070.41ms, mfu 0.05% +step 9500: train loss 4.4096, val loss 4.4299 +iter 9500: loss 4.1981, time 20052.92ms, mfu 0.05% +step 9750: train loss 4.4143, val loss 4.4290 +iter 9750: loss 4.2634, time 19170.79ms, mfu 0.05% +step 10000: train loss 4.4021, val loss 4.3897 +saving checkpoint to out-openwebtext_rerun_shared +iter 10000: loss 4.6448, time 21442.38ms, mfu 0.05% diff --git a/results/logs/train_full_x_w500_seed123.log b/results/logs/train_full_x_w500_seed123.log new file mode 100644 index 0000000000000000000000000000000000000000..df9afd14ed3b3c51fa1f463b257204fc0ed81f83 --- /dev/null +++ b/results/logs/train_full_x_w500_seed123.log @@ -0,0 +1,124 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 130.67M +C:\Users\brian\documents\nanogpt\train.py:200: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 131,039,232 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9733, val loss 10.9748 +iter 0: loss 10.9713, time 19496.82ms, mfu -100.00% +step 250: train loss 6.6355, val loss 6.6649 +saving checkpoint to out-openwebtext_warmup500_shared +iter 250: loss 6.7768, time 22248.85ms, mfu 0.05% +step 500: train loss 6.3721, val loss 6.3908 +saving checkpoint to out-openwebtext_warmup500_shared +iter 500: loss 6.3970, time 22430.42ms, mfu 0.05% +step 750: train loss 6.2027, val loss 6.2425 +saving checkpoint to out-openwebtext_warmup500_shared +iter 750: loss 6.4453, time 22248.44ms, mfu 0.05% +step 1000: train loss 6.0592, val loss 6.0852 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1000: loss 5.9592, time 22343.78ms, mfu 0.05% +step 1250: train loss 5.9039, val loss 5.8706 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1250: loss 5.5621, time 22235.42ms, mfu 0.05% +step 1500: train loss 5.7921, val loss 5.8265 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1500: loss 5.9170, time 22483.77ms, mfu 0.05% +step 1750: train loss 5.7430, val loss 5.6938 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1750: loss 5.5316, time 22212.09ms, mfu 0.05% +step 2000: train loss 5.6749, val loss 5.6680 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2000: loss 5.5849, time 22208.21ms, mfu 0.05% +step 2250: train loss 5.6123, val loss 5.6369 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2250: loss 5.4792, time 22168.46ms, mfu 0.05% +step 2500: train loss 5.5828, val loss 5.5960 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2500: loss 5.4230, time 22381.43ms, mfu 0.05% +step 2750: train loss 5.5608, val loss 5.5730 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2750: loss 5.3647, time 22337.94ms, mfu 0.05% +step 3000: train loss 5.5669, val loss 5.5668 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3000: loss 5.3087, time 22321.82ms, mfu 0.05% +step 3250: train loss 5.5365, val loss 5.5404 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3250: loss 5.1972, time 22286.09ms, mfu 0.05% +step 3500: train loss 5.5040, val loss 5.5368 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3500: loss 5.5258, time 22056.99ms, mfu 0.05% +step 3750: train loss 5.5401, val loss 5.5053 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3750: loss 5.3446, time 22176.41ms, mfu 0.05% +step 4000: train loss 5.4905, val loss 5.4873 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4000: loss 5.7325, time 22253.45ms, mfu 0.05% +step 4250: train loss 5.4598, val loss 5.4723 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4250: loss 5.5583, time 22292.60ms, mfu 0.05% +step 4500: train loss 5.4859, val loss 5.4793 +iter 4500: loss 5.4300, time 19053.77ms, mfu 0.05% +step 4750: train loss 5.4032, val loss 5.4129 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4750: loss 5.4709, time 22197.43ms, mfu 0.05% +step 5000: train loss 5.3716, val loss 5.3940 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5000: loss 5.6264, time 22180.19ms, mfu 0.05% +step 5250: train loss 5.3557, val loss 5.3671 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5250: loss 5.4670, time 22315.83ms, mfu 0.05% +step 5500: train loss 5.3174, val loss 5.2835 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5500: loss 5.2682, time 22238.83ms, mfu 0.05% +step 5750: train loss 5.2737, val loss 5.2843 +iter 5750: loss 5.7529, time 19074.16ms, mfu 0.05% +step 6000: train loss 5.2487, val loss 5.2457 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6000: loss 5.5489, time 22255.49ms, mfu 0.05% +step 6250: train loss 5.2134, val loss 5.2357 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6250: loss 5.2693, time 22265.59ms, mfu 0.05% +step 6500: train loss 5.1806, val loss 5.1829 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6500: loss 4.9135, time 22268.75ms, mfu 0.05% +step 6750: train loss 5.1732, val loss 5.1488 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6750: loss 4.9220, time 22481.11ms, mfu 0.05% +step 7000: train loss 5.1434, val loss 5.1518 +iter 7000: loss 5.2065, time 19196.52ms, mfu 0.05% +step 7250: train loss 5.1093, val loss 5.1134 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7250: loss 4.9914, time 22512.07ms, mfu 0.05% +step 7500: train loss 5.0839, val loss 5.1197 +iter 7500: loss 4.9692, time 19131.18ms, mfu 0.05% +step 7750: train loss 5.0669, val loss 5.0746 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7750: loss 5.3322, time 22279.34ms, mfu 0.05% +step 8000: train loss 5.0620, val loss 5.0624 +saving checkpoint to out-openwebtext_warmup500_shared +iter 8000: loss 5.0018, time 22458.09ms, mfu 0.05% +step 8250: train loss 4.9939, val loss 5.0200 +saving checkpoint to out-openwebtext_warmup500_shared +iter 8250: loss 4.8967, time 22307.10ms, mfu 0.05% +step 8500: train loss 5.0293, val loss 5.0185 +saving checkpoint to out-openwebtext_warmup500_shared +iter 8500: loss 5.0740, time 22285.79ms, mfu 0.05% +step 8750: train loss 4.9914, val loss 5.0146 +saving checkpoint to out-openwebtext_warmup500_shared +iter 8750: loss 5.4480, time 22034.53ms, mfu 0.05% +step 9000: train loss 4.9991, val loss 4.9975 +saving checkpoint to out-openwebtext_warmup500_shared +iter 9000: loss 4.7252, time 22376.16ms, mfu 0.05% +step 9250: train loss 4.9656, val loss 4.9718 +saving checkpoint to out-openwebtext_warmup500_shared +iter 9250: loss 4.6423, time 22184.28ms, mfu 0.05% +step 9500: train loss 4.9703, val loss 4.9434 +saving checkpoint to out-openwebtext_warmup500_shared +iter 9500: loss 5.1078, time 22244.51ms, mfu 0.05% +step 9750: train loss 4.9408, val loss 4.9638 +iter 9750: loss 4.9188, time 19104.25ms, mfu 0.05% +step 10000: train loss 4.9876, val loss 4.9500 +iter 10000: loss 5.4109, time 19078.14ms, mfu 0.05% diff --git a/results/logs/train_full_x_w500_seed1337.log b/results/logs/train_full_x_w500_seed1337.log new file mode 100644 index 0000000000000000000000000000000000000000..9113e89b90253ffad40550e31d44db8865a19610 --- /dev/null +++ b/results/logs/train_full_x_w500_seed1337.log @@ -0,0 +1,123 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 130.67M +C:\Users\brian\documents\nanogpt\train.py:200: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 131,039,232 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9622, val loss 10.9587 +iter 0: loss 10.9558, time 19425.05ms, mfu -100.00% +step 250: train loss 6.6882, val loss 6.6748 +saving checkpoint to out-openwebtext_warmup500_shared +iter 250: loss 6.5103, time 22178.91ms, mfu 0.05% +step 500: train loss 6.4259, val loss 6.4157 +saving checkpoint to out-openwebtext_warmup500_shared +iter 500: loss 6.0825, time 22303.32ms, mfu 0.05% +step 750: train loss 6.1882, val loss 6.1925 +saving checkpoint to out-openwebtext_warmup500_shared +iter 750: loss 6.5146, time 22250.67ms, mfu 0.05% +step 1000: train loss 6.0553, val loss 6.0765 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1000: loss 6.0564, time 22492.97ms, mfu 0.05% +step 1250: train loss 5.9771, val loss 5.9791 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1250: loss 5.8222, time 22385.67ms, mfu 0.05% +step 1500: train loss 5.8787, val loss 5.8470 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1500: loss 5.9900, time 22233.68ms, mfu 0.05% +step 1750: train loss 5.7233, val loss 5.7183 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1750: loss 5.4005, time 22364.24ms, mfu 0.05% +step 2000: train loss 5.6456, val loss 5.6453 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2000: loss 5.5313, time 22162.96ms, mfu 0.05% +step 2250: train loss 5.6228, val loss 5.6149 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2250: loss 5.8809, time 22211.15ms, mfu 0.05% +step 2500: train loss 5.5844, val loss 5.5606 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2500: loss 6.1367, time 22217.00ms, mfu 0.05% +step 2750: train loss 5.5138, val loss 5.5603 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2750: loss 5.8684, time 22601.94ms, mfu 0.05% +step 3000: train loss 5.5289, val loss 5.5476 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3000: loss 5.0520, time 23395.80ms, mfu 0.05% +step 3250: train loss 5.4589, val loss 5.4715 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3250: loss 5.3943, time 22492.93ms, mfu 0.05% +step 3500: train loss 5.4604, val loss 5.4659 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3500: loss 5.2224, time 22074.62ms, mfu 0.05% +step 3750: train loss 5.4001, val loss 5.4116 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3750: loss 5.4059, time 22331.72ms, mfu 0.05% +step 4000: train loss 5.4334, val loss 5.4243 +iter 4000: loss 5.6200, time 19004.88ms, mfu 0.05% +step 4250: train loss 5.3866, val loss 5.3482 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4250: loss 5.8005, time 22082.17ms, mfu 0.05% +step 4500: train loss 5.3613, val loss 5.3605 +iter 4500: loss 5.1063, time 19148.67ms, mfu 0.05% +step 4750: train loss 5.3034, val loss 5.3247 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4750: loss 5.2668, time 22355.21ms, mfu 0.05% +step 5000: train loss 5.3186, val loss 5.2427 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5000: loss 5.3485, time 22157.60ms, mfu 0.05% +step 5250: train loss 5.2534, val loss 5.2522 +iter 5250: loss 5.3513, time 19135.93ms, mfu 0.05% +step 5500: train loss 5.2179, val loss 5.2095 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5500: loss 5.1099, time 22091.88ms, mfu 0.05% +step 5750: train loss 5.1700, val loss 5.1872 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5750: loss 5.1414, time 22393.76ms, mfu 0.05% +step 6000: train loss 5.1288, val loss 5.1264 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6000: loss 4.6886, time 22211.51ms, mfu 0.05% +step 6250: train loss 5.1171, val loss 5.1103 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6250: loss 4.7904, time 22197.31ms, mfu 0.05% +step 6500: train loss 5.0877, val loss 5.0865 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6500: loss 5.1603, time 22240.03ms, mfu 0.05% +step 6750: train loss 5.0631, val loss 5.0565 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6750: loss 5.5108, time 22365.08ms, mfu 0.05% +step 7000: train loss 5.0377, val loss 5.0223 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7000: loss 5.0036, time 22361.44ms, mfu 0.05% +step 7250: train loss 4.9978, val loss 5.0165 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7250: loss 5.0583, time 22422.01ms, mfu 0.05% +step 7500: train loss 4.9872, val loss 5.0199 +iter 7500: loss 5.6378, time 19035.23ms, mfu 0.05% +step 7750: train loss 4.9671, val loss 4.9632 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7750: loss 5.0082, time 22182.85ms, mfu 0.05% +step 8000: train loss 4.9336, val loss 4.9563 +saving checkpoint to out-openwebtext_warmup500_shared +iter 8000: loss 5.2558, time 22930.59ms, mfu 0.05% +step 8250: train loss 4.9047, val loss 4.9077 +saving checkpoint to out-openwebtext_warmup500_shared +iter 8250: loss 5.0754, time 22362.45ms, mfu 0.05% +step 8500: train loss 4.8863, val loss 4.8939 +saving checkpoint to out-openwebtext_warmup500_shared +iter 8500: loss 4.8031, time 22166.18ms, mfu 0.05% +step 8750: train loss 4.9035, val loss 4.9091 +iter 8750: loss 4.8866, time 19027.21ms, mfu 0.05% +step 9000: train loss 4.8424, val loss 4.8985 +iter 9000: loss 5.1394, time 19070.44ms, mfu 0.05% +step 9250: train loss 4.8650, val loss 4.8485 +saving checkpoint to out-openwebtext_warmup500_shared +iter 9250: loss 4.6799, time 22260.54ms, mfu 0.05% +step 9500: train loss 4.8275, val loss 4.8454 +saving checkpoint to out-openwebtext_warmup500_shared +iter 9500: loss 5.2152, time 22221.67ms, mfu 0.05% +step 9750: train loss 4.8503, val loss 4.8499 +iter 9750: loss 4.7678, time 19151.50ms, mfu 0.05% +step 10000: train loss 4.8049, val loss 4.8156 +saving checkpoint to out-openwebtext_warmup500_shared +iter 10000: loss 5.3287, time 22314.70ms, mfu 0.05% diff --git a/results/logs/train_full_x_w500_seed42.log b/results/logs/train_full_x_w500_seed42.log new file mode 100644 index 0000000000000000000000000000000000000000..1623de299a37d943c52195473a61ba1bfc014e6f --- /dev/null +++ b/results/logs/train_full_x_w500_seed42.log @@ -0,0 +1,127 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 130.67M +C:\Users\brian\documents\nanogpt\train.py:200: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 131,039,232 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9285, val loss 10.9307 +iter 0: loss 10.9425, time 19395.24ms, mfu -100.00% +step 250: train loss 6.6342, val loss 6.6555 +saving checkpoint to out-openwebtext_warmup500_shared +iter 250: loss 6.2395, time 22204.32ms, mfu 0.05% +step 500: train loss 6.4115, val loss 6.4215 +saving checkpoint to out-openwebtext_warmup500_shared +iter 500: loss 6.3084, time 22181.55ms, mfu 0.05% +step 750: train loss 6.2200, val loss 6.2152 +saving checkpoint to out-openwebtext_warmup500_shared +iter 750: loss 6.5118, time 22238.95ms, mfu 0.05% +step 1000: train loss 6.0525, val loss 6.0640 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1000: loss 6.1001, time 22175.45ms, mfu 0.05% +step 1250: train loss 5.8977, val loss 5.8735 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1250: loss 5.8614, time 22269.44ms, mfu 0.05% +step 1500: train loss 5.7660, val loss 5.8333 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1500: loss 5.4592, time 22307.65ms, mfu 0.05% +step 1750: train loss 5.7004, val loss 5.7600 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1750: loss 5.7274, time 22187.01ms, mfu 0.05% +step 2000: train loss 5.7008, val loss 5.6723 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2000: loss 5.7924, time 22007.69ms, mfu 0.05% +step 2250: train loss 5.6099, val loss 5.6083 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2250: loss 5.5732, time 22273.25ms, mfu 0.05% +step 2500: train loss 5.5985, val loss 5.5965 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2500: loss 5.8530, time 22154.43ms, mfu 0.05% +step 2750: train loss 5.6020, val loss 5.5813 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2750: loss 5.6392, time 22395.21ms, mfu 0.05% +step 3000: train loss 5.5498, val loss 5.5445 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3000: loss 6.1040, time 22345.56ms, mfu 0.05% +step 3250: train loss 5.5338, val loss 5.5407 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3250: loss 5.5094, time 22246.20ms, mfu 0.05% +step 3500: train loss 5.4804, val loss 5.4775 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3500: loss 5.9138, time 22213.01ms, mfu 0.05% +step 3750: train loss 5.4792, val loss 5.4704 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3750: loss 5.6074, time 22333.51ms, mfu 0.05% +step 4000: train loss 5.4409, val loss 5.4547 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4000: loss 5.4200, time 22268.91ms, mfu 0.05% +step 4250: train loss 5.3938, val loss 5.3972 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4250: loss 5.2825, time 22139.65ms, mfu 0.05% +step 4500: train loss 5.3679, val loss 5.3581 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4500: loss 5.1374, time 22275.59ms, mfu 0.05% +step 4750: train loss 5.2901, val loss 5.3197 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4750: loss 5.1724, time 22276.49ms, mfu 0.05% +step 5000: train loss 5.2969, val loss 5.2901 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5000: loss 5.2785, time 22348.77ms, mfu 0.05% +step 5250: train loss 5.2598, val loss 5.2486 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5250: loss 5.1260, time 22197.59ms, mfu 0.05% +step 5500: train loss 5.2164, val loss 5.2139 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5500: loss 5.6235, time 22359.97ms, mfu 0.05% +step 5750: train loss 5.1884, val loss 5.1637 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5750: loss 5.2780, time 22356.85ms, mfu 0.05% +step 6000: train loss 5.1657, val loss 5.1282 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6000: loss 4.9986, time 22432.99ms, mfu 0.05% +step 6250: train loss 5.1208, val loss 5.1083 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6250: loss 4.9103, time 22280.66ms, mfu 0.05% +step 6500: train loss 5.0759, val loss 5.1154 +iter 6500: loss 4.9320, time 19052.55ms, mfu 0.05% +step 6750: train loss 5.0564, val loss 5.0541 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6750: loss 4.9997, time 22393.17ms, mfu 0.05% +step 7000: train loss 5.0247, val loss 5.0328 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7000: loss 5.2309, time 22166.25ms, mfu 0.05% +step 7250: train loss 5.0085, val loss 4.9965 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7250: loss 5.4538, time 22318.57ms, mfu 0.05% +step 7500: train loss 4.9885, val loss 4.9770 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7500: loss 4.9213, time 22368.57ms, mfu 0.05% +step 7750: train loss 4.9520, val loss 4.9710 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7750: loss 5.2900, time 22187.05ms, mfu 0.05% +step 8000: train loss 4.9311, val loss 4.9051 +saving checkpoint to out-openwebtext_warmup500_shared +iter 8000: loss 4.4420, time 22359.39ms, mfu 0.05% +step 8250: train loss 4.9368, val loss 4.8858 +saving checkpoint to out-openwebtext_warmup500_shared +iter 8250: loss 5.1722, time 22337.16ms, mfu 0.05% +step 8500: train loss 4.8749, val loss 4.8744 +saving checkpoint to out-openwebtext_warmup500_shared +iter 8500: loss 4.9019, time 22280.34ms, mfu 0.05% +step 8750: train loss 4.8836, val loss 4.8679 +saving checkpoint to out-openwebtext_warmup500_shared +iter 8750: loss 5.0847, time 22275.57ms, mfu 0.05% +step 9000: train loss 4.8639, val loss 4.8631 +saving checkpoint to out-openwebtext_warmup500_shared +iter 9000: loss 4.9047, time 22208.41ms, mfu 0.05% +step 9250: train loss 4.8495, val loss 4.8254 +saving checkpoint to out-openwebtext_warmup500_shared +iter 9250: loss 4.5589, time 22289.78ms, mfu 0.05% +step 9500: train loss 4.8293, val loss 4.8331 +iter 9500: loss 4.6962, time 19074.21ms, mfu 0.05% +step 9750: train loss 4.8324, val loss 4.8303 +iter 9750: loss 4.5985, time 19119.26ms, mfu 0.05% +step 10000: train loss 4.8256, val loss 4.7963 +saving checkpoint to out-openwebtext_warmup500_shared +iter 10000: loss 4.9634, time 22160.98ms, mfu 0.05% diff --git a/results/logs/train_midtier_q_w500_seed123.log b/results/logs/train_midtier_q_w500_seed123.log new file mode 100644 index 0000000000000000000000000000000000000000..c1691d3e5a3b39eafe14074788419986d1de13af --- /dev/null +++ b/results/logs/train_midtier_q_w500_seed123.log @@ -0,0 +1,127 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.78M +C:\Users\brian\documents\nanogpt\train.py:200: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 74, with 124,157,952 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 11.0155, val loss 11.0141 +iter 0: loss 10.9955, time 18688.97ms, mfu -100.00% +step 250: train loss 6.6311, val loss 6.6443 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 250: loss 6.6107, time 20158.30ms, mfu 0.05% +step 500: train loss 6.3717, val loss 6.3973 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 500: loss 6.0281, time 20216.72ms, mfu 0.05% +step 750: train loss 6.2478, val loss 6.2659 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 750: loss 6.2993, time 20045.11ms, mfu 0.05% +step 1000: train loss 6.1228, val loss 6.0679 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 1000: loss 5.7360, time 19887.53ms, mfu 0.05% +step 1250: train loss 5.9236, val loss 5.9581 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 1250: loss 5.9063, time 20022.33ms, mfu 0.05% +step 1500: train loss 5.8140, val loss 5.8706 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 1500: loss 5.5921, time 20302.00ms, mfu 0.05% +step 1750: train loss 5.7477, val loss 5.7486 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 1750: loss 5.5771, time 20021.31ms, mfu 0.05% +step 2000: train loss 5.6383, val loss 5.6821 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 2000: loss 5.8546, time 20150.23ms, mfu 0.05% +step 2250: train loss 5.6324, val loss 5.6458 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 2250: loss 5.6616, time 20394.43ms, mfu 0.05% +step 2500: train loss 5.5973, val loss 5.5801 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 2500: loss 5.1341, time 20637.31ms, mfu 0.05% +step 2750: train loss 5.5672, val loss 5.5603 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 2750: loss 5.7897, time 20794.64ms, mfu 0.05% +step 3000: train loss 5.4784, val loss 5.5136 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 3000: loss 5.3354, time 20756.46ms, mfu 0.05% +step 3250: train loss 5.4449, val loss 5.4534 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 3250: loss 5.5141, time 20773.83ms, mfu 0.05% +step 3500: train loss 5.4065, val loss 5.4338 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 3500: loss 5.5527, time 20697.93ms, mfu 0.05% +step 3750: train loss 5.3374, val loss 5.3644 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 3750: loss 5.3712, time 20451.67ms, mfu 0.05% +step 4000: train loss 5.3247, val loss 5.2925 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 4000: loss 5.2619, time 20565.30ms, mfu 0.05% +step 4250: train loss 5.2591, val loss 5.2810 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 4250: loss 5.4261, time 20345.60ms, mfu 0.05% +step 4500: train loss 5.2129, val loss 5.2410 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 4500: loss 5.0040, time 20675.57ms, mfu 0.05% +step 4750: train loss 5.1781, val loss 5.1855 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 4750: loss 4.7968, time 20914.50ms, mfu 0.05% +step 5000: train loss 5.1474, val loss 5.1557 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 5000: loss 5.2571, time 20431.60ms, mfu 0.05% +step 5250: train loss 5.1137, val loss 5.1443 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 5250: loss 5.2033, time 20824.21ms, mfu 0.05% +step 5500: train loss 5.0669, val loss 5.0439 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 5500: loss 4.8045, time 20744.05ms, mfu 0.05% +step 5750: train loss 5.0526, val loss 5.0453 +iter 5750: loss 4.8335, time 18828.86ms, mfu 0.05% +step 6000: train loss 4.9799, val loss 4.9697 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 6000: loss 5.1486, time 20788.89ms, mfu 0.05% +step 6250: train loss 4.9450, val loss 4.9425 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 6250: loss 5.0164, time 20925.72ms, mfu 0.05% +step 6500: train loss 4.9266, val loss 4.8950 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 6500: loss 4.8007, time 20817.40ms, mfu 0.05% +step 6750: train loss 4.8546, val loss 4.8556 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 6750: loss 4.7977, time 20296.13ms, mfu 0.05% +step 7000: train loss 4.8566, val loss 4.8587 +iter 7000: loss 4.7220, time 18829.11ms, mfu 0.05% +step 7250: train loss 4.8255, val loss 4.8223 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 7250: loss 4.7679, time 20389.86ms, mfu 0.05% +step 7500: train loss 4.7770, val loss 4.7812 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 7500: loss 4.5777, time 20309.26ms, mfu 0.05% +step 7750: train loss 4.7371, val loss 4.7615 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 7750: loss 5.0871, time 20485.99ms, mfu 0.05% +step 8000: train loss 4.7148, val loss 4.7303 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 8000: loss 4.6941, time 20410.66ms, mfu 0.05% +step 8250: train loss 4.7061, val loss 4.7250 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 8250: loss 4.5462, time 20440.65ms, mfu 0.05% +step 8500: train loss 4.6856, val loss 4.7078 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 8500: loss 4.4911, time 20382.40ms, mfu 0.05% +step 8750: train loss 4.6865, val loss 4.6796 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 8750: loss 4.7948, time 20299.99ms, mfu 0.05% +step 9000: train loss 4.6688, val loss 4.6741 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 9000: loss 4.8085, time 20756.87ms, mfu 0.05% +step 9250: train loss 4.6568, val loss 4.6773 +iter 9250: loss 4.9459, time 18835.86ms, mfu 0.05% +step 9500: train loss 4.6633, val loss 4.6633 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 9500: loss 4.5799, time 21039.98ms, mfu 0.05% +step 9750: train loss 4.6068, val loss 4.6369 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 9750: loss 4.4495, time 20747.08ms, mfu 0.05% +step 10000: train loss 4.6458, val loss 4.6143 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 10000: loss 4.9633, time 20480.91ms, mfu 0.05% diff --git a/results/logs/train_midtier_q_w500_seed1337.log b/results/logs/train_midtier_q_w500_seed1337.log new file mode 100644 index 0000000000000000000000000000000000000000..afa6a9efafb5a75b44516134839a898a9c0445ef --- /dev/null +++ b/results/logs/train_midtier_q_w500_seed1337.log @@ -0,0 +1,126 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.78M +C:\Users\brian\documents\nanogpt\train.py:200: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 74, with 124,157,952 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9835, val loss 10.9830 +iter 0: loss 10.9713, time 19155.65ms, mfu -100.00% +step 250: train loss 6.6586, val loss 6.6606 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 250: loss 6.7566, time 21309.72ms, mfu 0.05% +step 500: train loss 6.3866, val loss 6.4229 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 500: loss 6.2104, time 21288.49ms, mfu 0.05% +step 750: train loss 6.1852, val loss 6.2101 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 750: loss 6.1047, time 21378.01ms, mfu 0.05% +step 1000: train loss 6.0727, val loss 6.0770 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 1000: loss 6.1266, time 21253.77ms, mfu 0.05% +step 1250: train loss 5.9448, val loss 5.9333 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 1250: loss 6.9032, time 21644.28ms, mfu 0.05% +step 1500: train loss 5.8093, val loss 5.8334 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 1500: loss 5.7561, time 21235.44ms, mfu 0.05% +step 1750: train loss 5.7402, val loss 5.7259 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 1750: loss 5.7953, time 21708.58ms, mfu 0.05% +step 2000: train loss 5.6477, val loss 5.6305 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 2000: loss 6.1017, time 20913.75ms, mfu 0.05% +step 2250: train loss 5.6102, val loss 5.6073 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 2250: loss 5.1860, time 20636.89ms, mfu 0.05% +step 2500: train loss 5.5733, val loss 5.5775 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 2500: loss 5.7895, time 20420.46ms, mfu 0.05% +step 2750: train loss 5.5161, val loss 5.5075 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 2750: loss 5.3124, time 20402.93ms, mfu 0.05% +step 3000: train loss 5.4758, val loss 5.4581 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 3000: loss 5.3095, time 21247.82ms, mfu 0.05% +step 3250: train loss 5.4212, val loss 5.4561 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 3250: loss 5.4872, time 20746.90ms, mfu 0.05% +step 3500: train loss 5.3941, val loss 5.3598 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 3500: loss 5.4462, time 20601.50ms, mfu 0.05% +step 3750: train loss 5.3231, val loss 5.3690 +iter 3750: loss 5.4992, time 18854.47ms, mfu 0.05% +step 4000: train loss 5.3022, val loss 5.2779 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 4000: loss 5.5271, time 20572.81ms, mfu 0.05% +step 4250: train loss 5.2332, val loss 5.2653 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 4250: loss 5.3135, time 20602.47ms, mfu 0.05% +step 4500: train loss 5.2188, val loss 5.2165 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 4500: loss 5.0387, time 20364.39ms, mfu 0.05% +step 4750: train loss 5.1678, val loss 5.1596 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 4750: loss 4.4602, time 20512.65ms, mfu 0.05% +step 5000: train loss 5.1289, val loss 5.1440 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 5000: loss 4.6067, time 20704.08ms, mfu 0.05% +step 5250: train loss 5.0354, val loss 5.0966 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 5250: loss 5.2226, time 20512.80ms, mfu 0.05% +step 5500: train loss 5.0480, val loss 5.0413 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 5500: loss 4.9225, time 20304.60ms, mfu 0.05% +step 5750: train loss 5.0114, val loss 5.0020 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 5750: loss 4.8759, time 20403.84ms, mfu 0.05% +step 6000: train loss 4.9374, val loss 4.9821 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 6000: loss 4.8994, time 20825.22ms, mfu 0.05% +step 6250: train loss 4.8757, val loss 4.8974 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 6250: loss 5.2282, time 20765.54ms, mfu 0.05% +step 6500: train loss 4.8769, val loss 4.8773 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 6500: loss 4.9975, time 20792.89ms, mfu 0.05% +step 6750: train loss 4.8061, val loss 4.8573 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 6750: loss 4.6406, time 20778.42ms, mfu 0.05% +step 7000: train loss 4.8074, val loss 4.8495 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 7000: loss 5.0139, time 20314.80ms, mfu 0.05% +step 7250: train loss 4.7769, val loss 4.7802 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 7250: loss 4.9697, time 20433.48ms, mfu 0.05% +step 7500: train loss 4.7354, val loss 4.7557 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 7500: loss 4.9789, time 20724.21ms, mfu 0.05% +step 7750: train loss 4.7479, val loss 4.7116 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 7750: loss 4.5673, time 20544.36ms, mfu 0.05% +step 8000: train loss 4.7072, val loss 4.7055 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 8000: loss 4.6697, time 20373.85ms, mfu 0.05% +step 8250: train loss 4.6879, val loss 4.6958 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 8250: loss 5.0599, time 20466.74ms, mfu 0.05% +step 8500: train loss 4.6546, val loss 4.7024 +iter 8500: loss 4.7320, time 18785.97ms, mfu 0.05% +step 8750: train loss 4.6578, val loss 4.6491 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 8750: loss 4.9792, time 20686.36ms, mfu 0.05% +step 9000: train loss 4.6432, val loss 4.6523 +iter 9000: loss 4.6599, time 18828.36ms, mfu 0.05% +step 9250: train loss 4.6379, val loss 4.6241 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 9250: loss 4.6936, time 20376.00ms, mfu 0.05% +step 9500: train loss 4.6385, val loss 4.6297 +iter 9500: loss 4.6438, time 18783.64ms, mfu 0.05% +step 9750: train loss 4.6014, val loss 4.6226 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 9750: loss 4.2937, time 20635.69ms, mfu 0.05% +step 10000: train loss 4.6077, val loss 4.6151 +saving checkpoint to out-openwebtext_warmup500_midtierq_recovery +iter 10000: loss 4.4887, time 20497.84ms, mfu 0.05% diff --git a/results/logs/train_midtier_q_w500_seed42.log b/results/logs/train_midtier_q_w500_seed42.log new file mode 100644 index 0000000000000000000000000000000000000000..8ec36665a525b044fa25b26c296eda34af0652dd --- /dev/null +++ b/results/logs/train_midtier_q_w500_seed42.log @@ -0,0 +1,128 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.78M +C:\Users\brian\documents\nanogpt\train.py:200: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 74, with 124,157,952 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9295, val loss 10.9324 +iter 0: loss 10.9417, time 19608.03ms, mfu -100.00% +step 250: train loss 6.6612, val loss 6.6686 +saving checkpoint to out-openwebtext_warmup500_shared +iter 250: loss 6.6631, time 22378.03ms, mfu 0.05% +step 500: train loss 6.3854, val loss 6.4143 +saving checkpoint to out-openwebtext_warmup500_shared +iter 500: loss 6.4632, time 22236.78ms, mfu 0.05% +step 750: train loss 6.2102, val loss 6.2369 +saving checkpoint to out-openwebtext_warmup500_shared +iter 750: loss 6.2912, time 22208.68ms, mfu 0.05% +step 1000: train loss 6.0642, val loss 6.1191 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1000: loss 6.0028, time 22262.05ms, mfu 0.05% +step 1250: train loss 5.9682, val loss 5.9733 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1250: loss 5.6566, time 22190.52ms, mfu 0.05% +step 1500: train loss 5.9021, val loss 5.9020 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1500: loss 5.6416, time 22347.69ms, mfu 0.05% +step 1750: train loss 5.7712, val loss 5.7933 +saving checkpoint to out-openwebtext_warmup500_shared +iter 1750: loss 6.3198, time 22353.17ms, mfu 0.05% +step 2000: train loss 5.6861, val loss 5.7135 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2000: loss 5.3466, time 22356.71ms, mfu 0.05% +step 2250: train loss 5.6269, val loss 5.6395 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2250: loss 5.6114, time 22384.81ms, mfu 0.05% +step 2500: train loss 5.5497, val loss 5.5965 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2500: loss 5.8378, time 22192.37ms, mfu 0.05% +step 2750: train loss 5.4928, val loss 5.5081 +saving checkpoint to out-openwebtext_warmup500_shared +iter 2750: loss 5.3361, time 22302.20ms, mfu 0.05% +step 3000: train loss 5.4454, val loss 5.4433 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3000: loss 5.2295, time 22523.59ms, mfu 0.05% +step 3250: train loss 5.3764, val loss 5.4053 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3250: loss 5.1550, time 22464.70ms, mfu 0.05% +step 3500: train loss 5.3334, val loss 5.3176 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3500: loss 5.1760, time 22300.18ms, mfu 0.05% +step 3750: train loss 5.2885, val loss 5.2765 +saving checkpoint to out-openwebtext_warmup500_shared +iter 3750: loss 5.2987, time 22299.62ms, mfu 0.05% +step 4000: train loss 5.2539, val loss 5.2144 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4000: loss 5.2232, time 22279.12ms, mfu 0.05% +step 4250: train loss 5.2114, val loss 5.1961 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4250: loss 4.7525, time 22470.54ms, mfu 0.05% +step 4500: train loss 5.1463, val loss 5.1349 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4500: loss 4.6751, time 22224.37ms, mfu 0.05% +step 4750: train loss 5.1088, val loss 5.1225 +saving checkpoint to out-openwebtext_warmup500_shared +iter 4750: loss 5.2926, time 22109.91ms, mfu 0.05% +step 5000: train loss 5.0930, val loss 5.0993 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5000: loss 4.7746, time 22249.63ms, mfu 0.05% +step 5250: train loss 5.0170, val loss 5.0210 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5250: loss 5.0858, time 22155.55ms, mfu 0.05% +step 5500: train loss 5.0151, val loss 4.9867 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5500: loss 5.2769, time 22226.97ms, mfu 0.05% +step 5750: train loss 4.9699, val loss 4.9287 +saving checkpoint to out-openwebtext_warmup500_shared +iter 5750: loss 4.9531, time 22340.88ms, mfu 0.05% +step 6000: train loss 4.9179, val loss 4.9054 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6000: loss 4.9435, time 22356.66ms, mfu 0.05% +step 6250: train loss 4.8801, val loss 4.8617 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6250: loss 4.7389, time 22269.09ms, mfu 0.05% +step 6500: train loss 4.8313, val loss 4.8533 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6500: loss 5.2634, time 22365.64ms, mfu 0.05% +step 6750: train loss 4.7771, val loss 4.7845 +saving checkpoint to out-openwebtext_warmup500_shared +iter 6750: loss 4.6898, time 22297.17ms, mfu 0.05% +step 7000: train loss 4.7604, val loss 4.7589 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7000: loss 5.6962, time 22460.71ms, mfu 0.05% +step 7250: train loss 4.7005, val loss 4.7523 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7250: loss 4.8403, time 22360.80ms, mfu 0.05% +step 7500: train loss 4.7053, val loss 4.6937 +saving checkpoint to out-openwebtext_warmup500_shared +iter 7500: loss 4.4966, time 22127.53ms, mfu 0.05% +step 7750: train loss 4.6957, val loss 4.7125 +iter 7750: loss 4.6066, time 19287.97ms, mfu 0.05% +step 8000: train loss 4.6786, val loss 4.6868 +saving checkpoint to out-openwebtext_warmup500_shared +iter 8000: loss 4.6062, time 22331.68ms, mfu 0.05% +step 8250: train loss 4.6636, val loss 4.6560 +saving checkpoint to out-openwebtext_warmup500_shared +iter 8250: loss 4.6852, time 22428.49ms, mfu 0.05% +step 8500: train loss 4.6350, val loss 4.6426 +saving checkpoint to out-openwebtext_warmup500_shared +iter 8500: loss 4.5991, time 22327.54ms, mfu 0.05% +step 8750: train loss 4.6374, val loss 4.6367 +saving checkpoint to out-openwebtext_warmup500_shared +iter 8750: loss 4.3357, time 22218.30ms, mfu 0.05% +step 9000: train loss 4.6021, val loss 4.6164 +saving checkpoint to out-openwebtext_warmup500_shared +iter 9000: loss 4.8422, time 22309.99ms, mfu 0.05% +step 9250: train loss 4.6034, val loss 4.6106 +saving checkpoint to out-openwebtext_warmup500_shared +iter 9250: loss 4.5487, time 22395.89ms, mfu 0.05% +step 9500: train loss 4.5762, val loss 4.5931 +saving checkpoint to out-openwebtext_warmup500_shared +iter 9500: loss 4.5575, time 21979.87ms, mfu 0.05% +step 9750: train loss 4.5755, val loss 4.5950 +iter 9750: loss 4.7578, time 19697.62ms, mfu 0.05% +step 10000: train loss 4.5768, val loss 4.5733 +saving checkpoint to out-openwebtext_warmup500_shared +iter 10000: loss 5.2536, time 21354.03ms, mfu 0.05% diff --git a/results/logs/train_q_full_headspec_w2000_seed123.log b/results/logs/train_q_full_headspec_w2000_seed123.log new file mode 100644 index 0000000000000000000000000000000000000000..9772bd56c5fe07e5d4c8f0b61b9f2284d695744f --- /dev/null +++ b/results/logs/train_q_full_headspec_w2000_seed123.log @@ -0,0 +1,128 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 130.67M +C:\Users\brian\documents\nanogpt\train.py:203: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 131,039,232 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9920, val loss 10.9903 +iter 0: loss 10.9854, time 18879.20ms, mfu -100.00% +step 250: train loss 6.9343, val loss 6.9622 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 250: loss 7.3059, time 20676.12ms, mfu 0.05% +step 500: train loss 6.5068, val loss 6.5186 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 500: loss 6.3033, time 20840.21ms, mfu 0.05% +step 750: train loss 6.3518, val loss 6.3821 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 750: loss 6.1921, time 20514.34ms, mfu 0.05% +step 1000: train loss 6.1437, val loss 6.1739 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 1000: loss 6.3733, time 20277.49ms, mfu 0.05% +step 1250: train loss 6.0504, val loss 6.0486 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 1250: loss 6.4141, time 20284.28ms, mfu 0.05% +step 1500: train loss 5.9143, val loss 5.9290 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 1500: loss 5.9221, time 20447.25ms, mfu 0.05% +step 1750: train loss 5.8153, val loss 5.8236 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 1750: loss 5.5729, time 20355.26ms, mfu 0.05% +step 2000: train loss 5.7877, val loss 5.7594 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 2000: loss 6.0825, time 20576.93ms, mfu 0.05% +step 2250: train loss 5.5975, val loss 5.6382 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 2250: loss 5.5966, time 20268.06ms, mfu 0.05% +step 2500: train loss 5.5296, val loss 5.5245 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 2500: loss 5.6792, time 20329.69ms, mfu 0.05% +step 2750: train loss 5.4421, val loss 5.4424 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 2750: loss 5.8064, time 20613.88ms, mfu 0.05% +step 3000: train loss 5.3594, val loss 5.3704 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 3000: loss 5.0741, time 20461.44ms, mfu 0.05% +step 3250: train loss 5.2948, val loss 5.2877 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 3250: loss 5.6451, time 20641.47ms, mfu 0.05% +step 3500: train loss 5.2492, val loss 5.2157 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 3500: loss 5.8766, time 20608.27ms, mfu 0.05% +step 3750: train loss 5.2181, val loss 5.1971 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 3750: loss 5.0279, time 20419.21ms, mfu 0.05% +step 4000: train loss 5.1889, val loss 5.1183 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 4000: loss 4.8536, time 20346.42ms, mfu 0.05% +step 4250: train loss 5.0807, val loss 5.0988 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 4250: loss 4.6856, time 20320.82ms, mfu 0.05% +step 4500: train loss 5.0184, val loss 5.0358 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 4500: loss 5.2455, time 20482.20ms, mfu 0.05% +step 4750: train loss 4.9773, val loss 4.9931 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 4750: loss 5.0917, time 20692.41ms, mfu 0.05% +step 5000: train loss 4.9316, val loss 4.9164 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 5000: loss 5.0973, time 20270.29ms, mfu 0.05% +step 5250: train loss 4.9122, val loss 4.8787 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 5250: loss 4.8488, time 20552.13ms, mfu 0.05% +step 5500: train loss 4.8037, val loss 4.8120 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 5500: loss 4.5873, time 20579.60ms, mfu 0.05% +step 5750: train loss 4.7648, val loss 4.7702 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 5750: loss 4.6646, time 20455.06ms, mfu 0.05% +step 6000: train loss 4.7526, val loss 4.7439 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 6000: loss 4.4587, time 20541.19ms, mfu 0.05% +step 6250: train loss 4.6950, val loss 4.6829 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 6250: loss 4.6748, time 20407.03ms, mfu 0.05% +step 6500: train loss 4.6316, val loss 4.6653 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 6500: loss 4.7077, time 20734.76ms, mfu 0.05% +step 6750: train loss 4.6281, val loss 4.6468 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 6750: loss 4.6480, time 20417.29ms, mfu 0.05% +step 7000: train loss 4.5949, val loss 4.5956 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 7000: loss 4.5883, time 20630.81ms, mfu 0.05% +step 7250: train loss 4.5692, val loss 4.5856 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 7250: loss 4.4520, time 20651.71ms, mfu 0.05% +step 7500: train loss 4.5440, val loss 4.5449 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 7500: loss 4.2138, time 20312.40ms, mfu 0.05% +step 7750: train loss 4.5250, val loss 4.5374 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 7750: loss 4.1870, time 20504.78ms, mfu 0.05% +step 8000: train loss 4.5306, val loss 4.5044 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 8000: loss 4.4876, time 20773.98ms, mfu 0.05% +step 8250: train loss 4.5041, val loss 4.4934 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 8250: loss 4.8309, time 20357.49ms, mfu 0.05% +step 8500: train loss 4.4566, val loss 4.4713 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 8500: loss 4.8097, time 20496.59ms, mfu 0.05% +step 8750: train loss 4.4533, val loss 4.4395 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 8750: loss 4.3339, time 21192.37ms, mfu 0.05% +step 9000: train loss 4.4646, val loss 4.4370 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 9000: loss 4.5807, time 20805.66ms, mfu 0.05% +step 9250: train loss 4.4179, val loss 4.4285 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 9250: loss 4.7094, time 20308.76ms, mfu 0.05% +step 9500: train loss 4.4022, val loss 4.4432 +iter 9500: loss 4.1770, time 18694.97ms, mfu 0.05% +step 9750: train loss 4.4031, val loss 4.3964 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 9750: loss 4.5242, time 20327.11ms, mfu 0.05% +step 10000: train loss 4.3802, val loss 4.4366 +iter 10000: loss 4.2454, time 18748.70ms, mfu 0.05% diff --git a/results/logs/train_q_full_headspec_w2000_seed1337.log b/results/logs/train_q_full_headspec_w2000_seed1337.log new file mode 100644 index 0000000000000000000000000000000000000000..c6f51631979e5915714995eb132b24a2c4a3518d --- /dev/null +++ b/results/logs/train_q_full_headspec_w2000_seed1337.log @@ -0,0 +1,128 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 130.67M +C:\Users\brian\documents\nanogpt\train.py:203: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 131,039,232 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9522, val loss 10.9506 +iter 0: loss 10.9581, time 18984.75ms, mfu -100.00% +step 250: train loss 6.9799, val loss 6.9359 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 250: loss 6.7887, time 20281.75ms, mfu 0.05% +step 500: train loss 6.5055, val loss 6.4839 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 500: loss 6.3563, time 20876.19ms, mfu 0.05% +step 750: train loss 6.2921, val loss 6.3124 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 750: loss 6.7560, time 20372.56ms, mfu 0.05% +step 1000: train loss 6.1767, val loss 6.1654 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 1000: loss 5.6218, time 20448.19ms, mfu 0.05% +step 1250: train loss 6.0566, val loss 6.0267 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 1250: loss 5.9575, time 20463.86ms, mfu 0.05% +step 1500: train loss 5.9073, val loss 5.9259 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 1500: loss 6.3354, time 20488.81ms, mfu 0.05% +step 1750: train loss 5.8476, val loss 5.8525 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 1750: loss 5.4585, time 20223.42ms, mfu 0.05% +step 2000: train loss 5.7606, val loss 5.7438 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 2000: loss 5.8005, time 20415.29ms, mfu 0.05% +step 2250: train loss 5.6513, val loss 5.6801 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 2250: loss 5.7348, time 20505.82ms, mfu 0.05% +step 2500: train loss 5.5555, val loss 5.5412 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 2500: loss 5.0841, time 20569.66ms, mfu 0.05% +step 2750: train loss 5.4699, val loss 5.4892 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 2750: loss 4.9730, time 20476.81ms, mfu 0.05% +step 3000: train loss 5.3985, val loss 5.3795 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 3000: loss 5.5677, time 20704.37ms, mfu 0.05% +step 3250: train loss 5.3065, val loss 5.3656 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 3250: loss 5.4708, time 20632.24ms, mfu 0.05% +step 3500: train loss 5.2817, val loss 5.2598 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 3500: loss 5.6546, time 20306.75ms, mfu 0.05% +step 3750: train loss 5.2312, val loss 5.2176 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 3750: loss 5.2904, time 20414.02ms, mfu 0.05% +step 4000: train loss 5.1574, val loss 5.1349 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 4000: loss 5.3041, time 20525.56ms, mfu 0.05% +step 4250: train loss 5.0834, val loss 5.0951 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 4250: loss 4.9301, time 20307.40ms, mfu 0.05% +step 4500: train loss 5.0968, val loss 5.0433 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 4500: loss 5.2234, time 20358.34ms, mfu 0.05% +step 4750: train loss 5.0158, val loss 4.9967 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 4750: loss 4.5392, time 20553.28ms, mfu 0.05% +step 5000: train loss 4.9318, val loss 4.9431 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 5000: loss 5.1849, time 20676.17ms, mfu 0.05% +step 5250: train loss 4.8573, val loss 4.8549 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 5250: loss 4.6914, time 20822.02ms, mfu 0.05% +step 5500: train loss 4.8286, val loss 4.8254 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 5500: loss 4.9470, time 20309.95ms, mfu 0.05% +step 5750: train loss 4.7661, val loss 4.8056 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 5750: loss 4.3648, time 20353.68ms, mfu 0.05% +step 6000: train loss 4.7540, val loss 4.7427 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 6000: loss 4.9657, time 20409.21ms, mfu 0.05% +step 6250: train loss 4.7135, val loss 4.7102 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 6250: loss 4.7664, time 20393.17ms, mfu 0.05% +step 6500: train loss 4.6858, val loss 4.6721 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 6500: loss 4.8815, time 20441.55ms, mfu 0.05% +step 6750: train loss 4.6281, val loss 4.6291 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 6750: loss 4.8460, time 21262.58ms, mfu 0.05% +step 7000: train loss 4.6157, val loss 4.6164 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 7000: loss 4.3657, time 20419.23ms, mfu 0.05% +step 7250: train loss 4.5900, val loss 4.5529 +saving checkpoint to out-openwebtext_phase4_q_full_headspec 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+saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 9000: loss 4.7614, time 20414.33ms, mfu 0.05% +step 9250: train loss 4.4077, val loss 4.4366 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 9250: loss 4.6671, time 20298.16ms, mfu 0.05% +step 9500: train loss 4.4391, val loss 4.4341 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 9500: loss 4.8639, time 20359.13ms, mfu 0.05% +step 9750: train loss 4.4252, val loss 4.4058 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 9750: loss 4.4052, time 20380.31ms, mfu 0.05% +step 10000: train loss 4.3977, val loss 4.4101 +iter 10000: loss 4.2989, time 18759.61ms, mfu 0.05% diff --git a/results/logs/train_q_full_headspec_w2000_seed42.log b/results/logs/train_q_full_headspec_w2000_seed42.log new file mode 100644 index 0000000000000000000000000000000000000000..0dc0973f127100d3c103f401b82751f5021df158 --- /dev/null +++ b/results/logs/train_q_full_headspec_w2000_seed42.log @@ -0,0 +1,128 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 130.67M +C:\Users\brian\documents\nanogpt\train.py:203: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 131,039,232 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9673, val loss 10.9663 +iter 0: loss 10.9788, time 19335.07ms, mfu -100.00% +step 250: train loss 6.9164, val loss 6.9069 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 250: loss 7.5439, time 20295.48ms, mfu 0.05% +step 500: train loss 6.5075, val loss 6.5132 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 500: loss 6.4082, time 22435.94ms, mfu 0.05% +step 750: train loss 6.2973, val loss 6.3303 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 750: loss 5.9042, time 20857.04ms, mfu 0.05% +step 1000: train loss 6.1429, val loss 6.1679 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 1000: loss 5.7700, time 20636.66ms, mfu 0.05% +step 1250: train loss 6.0760, val loss 6.0682 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 1250: loss 6.4562, time 21115.91ms, mfu 0.05% +step 1500: train loss 5.9116, val loss 5.8725 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 1500: loss 5.6772, time 20429.67ms, mfu 0.05% +step 1750: train loss 5.8009, val loss 5.7876 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 1750: loss 5.5641, time 20741.72ms, mfu 0.05% +step 2000: train loss 5.7355, val loss 5.7238 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 2000: loss 5.6080, time 20691.91ms, mfu 0.05% +step 2250: train loss 5.6125, val loss 5.6112 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 2250: loss 5.5966, time 20709.25ms, mfu 0.05% +step 2500: train loss 5.5453, val loss 5.5025 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 2500: loss 5.4236, time 21371.50ms, mfu 0.05% +step 2750: train loss 5.4321, val loss 5.4427 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 2750: loss 5.6220, time 20805.25ms, mfu 0.05% +step 3000: train loss 5.3656, val loss 5.3829 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 3000: loss 5.7078, time 20819.96ms, mfu 0.05% +step 3250: train loss 5.3348, val loss 5.2797 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 3250: loss 5.5580, time 20461.16ms, mfu 0.05% +step 3500: train loss 5.2742, val loss 5.2601 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 3500: loss 5.1080, time 20833.98ms, mfu 0.05% +step 3750: train loss 5.1974, val loss 5.2100 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 3750: loss 5.4228, time 20363.47ms, mfu 0.05% +step 4000: train loss 5.0954, val loss 5.1323 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 4000: loss 5.3778, time 20384.55ms, mfu 0.05% +step 4250: train loss 5.0853, val loss 5.0812 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 4250: loss 5.3212, time 20300.66ms, mfu 0.05% +step 4500: train loss 5.0283, val loss 5.0098 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 4500: loss 4.9511, time 20406.43ms, mfu 0.05% +step 4750: train loss 4.9615, val loss 4.9967 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 4750: loss 4.9835, time 20457.12ms, mfu 0.05% +step 5000: train loss 4.9336, val loss 4.9354 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 5000: loss 4.6345, time 20413.45ms, mfu 0.05% +step 5250: train loss 4.8764, val loss 4.8922 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 5250: loss 4.4500, time 20435.45ms, mfu 0.05% +step 5500: train loss 4.8207, val loss 4.8247 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 5500: loss 4.4971, time 20184.74ms, mfu 0.05% +step 5750: train loss 4.7965, val loss 4.7987 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 5750: loss 4.9242, time 20334.03ms, mfu 0.05% +step 6000: train loss 4.7405, val loss 4.7324 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 6000: loss 4.8643, time 20364.46ms, mfu 0.05% +step 6250: train loss 4.6933, val loss 4.7006 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 6250: loss 4.4579, time 20364.41ms, mfu 0.05% +step 6500: train loss 4.6689, val loss 4.6757 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 6500: loss 4.6272, time 20693.04ms, mfu 0.05% +step 6750: train loss 4.6237, val loss 4.6416 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 6750: loss 4.7112, time 20519.75ms, mfu 0.05% +step 7000: train loss 4.6083, val loss 4.6268 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 7000: loss 4.2105, time 20580.63ms, mfu 0.05% +step 7250: train loss 4.5803, val loss 4.5828 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 7250: loss 4.4728, time 20480.21ms, mfu 0.05% +step 7500: train loss 4.5980, val loss 4.5495 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 7500: loss 4.5273, time 20628.07ms, mfu 0.05% +step 7750: train loss 4.5368, val loss 4.5429 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 7750: loss 4.6218, time 20466.19ms, mfu 0.05% +step 8000: train loss 4.4939, val loss 4.5387 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 8000: loss 4.6632, time 20409.18ms, mfu 0.05% +step 8250: train loss 4.4991, val loss 4.4930 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 8250: loss 4.7281, time 20429.49ms, mfu 0.05% +step 8500: train loss 4.5109, val loss 4.4635 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 8500: loss 4.5002, time 20354.90ms, mfu 0.05% +step 8750: train loss 4.4673, val loss 4.4768 +iter 8750: loss 3.8414, time 18764.84ms, mfu 0.05% +step 9000: train loss 4.4480, val loss 4.4457 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 9000: loss 4.0221, time 20524.67ms, mfu 0.05% +step 9250: train loss 4.4435, val loss 4.4333 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 9250: loss 4.5061, time 20572.15ms, mfu 0.05% +step 9500: train loss 4.4378, val loss 4.4428 +iter 9500: loss 4.4435, time 18794.05ms, mfu 0.05% +step 9750: train loss 4.4256, val loss 4.4331 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 9750: loss 4.1236, time 20713.39ms, mfu 0.05% +step 10000: train loss 4.3983, val loss 4.4151 +saving checkpoint to out-openwebtext_phase4_q_full_headspec +iter 10000: loss 4.7682, time 20931.01ms, mfu 0.05% diff --git a/results/logs/train_qa_headspec_w2000_seed123.log b/results/logs/train_qa_headspec_w2000_seed123.log new file mode 100644 index 0000000000000000000000000000000000000000..8e11c3e78156ded5675905a7e7a978caefb96316 --- /dev/null +++ b/results/logs/train_qa_headspec_w2000_seed123.log @@ -0,0 +1,125 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 124.77M +C:\Users\brian\documents\nanogpt\train.py:203: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 125,140,992 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9801, val loss 10.9814 +iter 0: loss 10.9926, time 18953.70ms, mfu -100.00% +step 250: train loss 6.9878, val loss 6.9393 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 250: loss 6.9111, time 20736.00ms, mfu 0.05% +step 500: train loss 6.5071, val loss 6.5386 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 500: loss 6.4847, time 21004.70ms, mfu 0.05% +step 750: train loss 6.3538, val loss 6.3615 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 750: loss 6.5779, time 20705.48ms, mfu 0.05% +step 1000: train loss 6.1562, val loss 6.1785 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 1000: loss 6.2913, time 20508.42ms, mfu 0.05% +step 1250: train loss 6.0628, val loss 6.0612 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 1250: loss 6.0236, time 20459.02ms, mfu 0.05% +step 1500: train loss 5.9751, val loss 6.0101 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 1500: loss 6.0539, time 20324.64ms, mfu 0.05% +step 1750: train loss 5.8548, val loss 5.8755 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 1750: loss 5.3054, time 20308.07ms, mfu 0.05% +step 2000: train loss 5.8100, val loss 5.8064 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 2000: loss 5.5360, time 20372.68ms, mfu 0.05% +step 2250: train loss 5.6805, val loss 5.6610 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 2250: loss 5.7859, time 20365.11ms, mfu 0.05% +step 2500: train loss 5.5409, val loss 5.5856 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 2500: loss 5.6041, time 20664.20ms, mfu 0.05% +step 2750: train loss 5.4799, val loss 5.5084 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 2750: loss 5.1791, time 20582.89ms, mfu 0.05% +step 3000: train loss 5.4476, val loss 5.4421 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 3000: loss 5.3637, time 20714.78ms, mfu 0.05% +step 3250: train loss 5.3332, val loss 5.3511 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 3250: loss 5.2031, time 20612.27ms, mfu 0.05% +step 3500: train loss 5.2566, val loss 5.2682 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 3500: loss 5.2368, time 20704.18ms, mfu 0.05% +step 3750: train loss 5.2162, val loss 5.1988 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 3750: loss 5.5619, time 20707.76ms, mfu 0.05% +step 4000: train loss 5.1521, val loss 5.1837 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 4000: loss 4.9783, time 21003.07ms, mfu 0.05% +step 4250: train loss 5.1304, val loss 5.1099 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 4250: loss 5.2401, time 20567.85ms, mfu 0.05% +step 4500: train loss 5.0723, val loss 5.0755 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 4500: loss 5.0900, time 20262.04ms, mfu 0.05% +step 4750: train loss 5.0106, val loss 5.0190 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 4750: loss 5.1177, time 20416.18ms, mfu 0.05% +step 5000: train loss 4.9560, val loss 4.9439 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 5000: loss 5.0267, time 20340.49ms, mfu 0.05% +step 5250: train loss 4.8961, val loss 4.8909 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 5250: loss 4.9689, time 20713.02ms, mfu 0.05% +step 5500: train loss 4.8395, val loss 4.8369 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 5500: loss 4.6503, time 20578.37ms, mfu 0.05% +step 5750: train loss 4.7904, val loss 4.8268 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 5750: loss 4.9539, time 20532.79ms, mfu 0.05% +step 6000: train loss 4.7382, val loss 4.7422 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 6000: loss 4.9344, time 20607.60ms, mfu 0.05% +step 6250: train loss 4.6949, val loss 4.7341 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 6250: loss 5.0541, time 20564.61ms, mfu 0.05% +step 6500: train loss 4.6949, val loss 4.6842 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 6500: loss 4.7767, time 21235.55ms, mfu 0.05% +step 6750: train loss 4.6292, val loss 4.6489 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 6750: loss 4.7734, time 20235.89ms, mfu 0.05% +step 7000: train loss 4.6123, val loss 4.6217 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 7000: loss 4.7336, time 20665.13ms, mfu 0.05% +step 7250: train loss 4.5864, val loss 4.5819 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 7250: loss 4.3220, time 20675.41ms, mfu 0.05% +step 7500: train loss 4.5519, val loss 4.5834 +iter 7500: loss 4.4425, time 19154.57ms, mfu 0.05% +step 7750: train loss 4.5355, val loss 4.5032 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 7750: loss 4.1923, time 20824.28ms, mfu 0.05% +step 8000: train loss 4.5120, val loss 4.4890 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 8000: loss 4.5828, time 20728.44ms, mfu 0.05% +step 8250: train loss 4.4875, val loss 4.5055 +iter 8250: loss 4.3561, time 19467.96ms, mfu 0.05% +step 8500: train loss 4.4771, val loss 4.4874 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 8500: loss 4.9381, time 20747.78ms, mfu 0.05% +step 8750: train loss 4.4663, val loss 4.4385 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 8750: loss 4.4546, time 21321.71ms, mfu 0.05% +step 9000: train loss 4.4415, val loss 4.4495 +iter 9000: loss 4.1722, time 19106.77ms, mfu 0.05% +step 9250: train loss 4.4456, val loss 4.4541 +iter 9250: loss 4.3666, time 19164.68ms, mfu 0.05% +step 9500: train loss 4.4292, val loss 4.4412 +iter 9500: loss 4.2498, time 19278.45ms, mfu 0.05% +step 9750: train loss 4.4541, val loss 4.4307 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 9750: loss 4.2472, time 21302.36ms, mfu 0.05% +step 10000: train loss 4.4061, val loss 4.3904 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 10000: loss 4.3000, time 20801.39ms, mfu 0.05% diff --git a/results/logs/train_qa_headspec_w2000_seed1337.log b/results/logs/train_qa_headspec_w2000_seed1337.log new file mode 100644 index 0000000000000000000000000000000000000000..a4e8890277913e82189b68b5c8e52e5e4f2c2028 --- /dev/null +++ b/results/logs/train_qa_headspec_w2000_seed1337.log @@ -0,0 +1,129 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 124.77M +C:\Users\brian\documents\nanogpt\train.py:203: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 125,140,992 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9907, val loss 10.9906 +iter 0: loss 11.0033, time 18950.47ms, mfu -100.00% +step 250: train loss 6.9325, val loss 6.9608 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 250: loss 6.8925, time 20367.40ms, mfu 0.05% +step 500: train loss 6.5034, val loss 6.5238 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 500: loss 6.6085, time 20523.52ms, mfu 0.05% +step 750: train loss 6.3022, val loss 6.3555 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 750: loss 6.5472, time 20314.77ms, mfu 0.05% +step 1000: train loss 6.2094, val loss 6.1648 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 1000: loss 6.0845, time 20259.15ms, mfu 0.05% +step 1250: train loss 6.0619, val loss 6.0894 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 1250: loss 6.5549, time 20857.53ms, mfu 0.05% +step 1500: train loss 5.9316, val loss 5.9347 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 1500: loss 6.4790, time 20392.25ms, mfu 0.05% +step 1750: train loss 5.8571, val loss 5.8905 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 1750: loss 5.8328, time 20761.73ms, mfu 0.05% +step 2000: train loss 5.7680, val loss 5.7334 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 2000: loss 5.5306, time 20241.67ms, mfu 0.05% +step 2250: train loss 5.6303, val loss 5.6934 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 2250: loss 5.3996, time 20596.13ms, mfu 0.05% +step 2500: train loss 5.5119, val loss 5.5412 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 2500: loss 5.4070, time 20798.71ms, mfu 0.05% +step 2750: train loss 5.4470, val loss 5.4628 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 2750: loss 5.6877, time 21050.32ms, mfu 0.05% +step 3000: train loss 5.4008, val loss 5.3847 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 3000: loss 5.5222, time 20663.64ms, mfu 0.05% +step 3250: train loss 5.3182, val loss 5.3181 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 3250: loss 5.2428, time 20518.04ms, mfu 0.05% +step 3500: train loss 5.2204, val loss 5.2730 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 3500: loss 5.7082, time 20449.54ms, mfu 0.05% +step 3750: train loss 5.1277, val loss 5.1704 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 3750: loss 5.0926, time 20342.65ms, mfu 0.05% +step 4000: train loss 5.1790, val loss 5.1499 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 4000: loss 5.0771, time 20310.44ms, mfu 0.05% +step 4250: train loss 5.0784, val loss 5.0636 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 4250: loss 5.1499, time 20253.29ms, mfu 0.05% +step 4500: train loss 5.0170, val loss 5.0519 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 4500: loss 5.0622, time 20354.51ms, mfu 0.05% +step 4750: train loss 4.9736, val loss 5.0022 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 4750: loss 5.1671, time 20415.76ms, mfu 0.05% +step 5000: train loss 4.8987, val loss 4.8988 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 5000: loss 5.2427, time 20784.10ms, mfu 0.05% +step 5250: train loss 4.8494, val loss 4.8658 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 5250: loss 4.6934, time 20706.07ms, mfu 0.05% +step 5500: train loss 4.8378, val loss 4.8182 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 5500: loss 5.1912, time 20414.76ms, mfu 0.05% +step 5750: train loss 4.7831, val loss 4.7633 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 5750: loss 4.7312, time 20456.81ms, mfu 0.05% +step 6000: train loss 4.7127, val loss 4.7373 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 6000: loss 4.8204, time 20373.77ms, mfu 0.05% +step 6250: train loss 4.6880, val loss 4.6910 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 6250: loss 4.5173, time 20381.35ms, mfu 0.05% +step 6500: train loss 4.6511, val loss 4.6844 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 6500: loss 4.6653, time 20317.48ms, mfu 0.05% +step 6750: train loss 4.6475, val loss 4.6266 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 6750: loss 4.2391, time 20388.46ms, mfu 0.05% +step 7000: train loss 4.5882, val loss 4.6025 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 7000: loss 4.6727, time 20409.11ms, mfu 0.05% +step 7250: train loss 4.5861, val loss 4.5766 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 7250: loss 4.2737, time 20755.14ms, mfu 0.05% +step 7500: train loss 4.5260, val loss 4.5494 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 7500: loss 3.9532, time 20820.97ms, mfu 0.05% +step 7750: train loss 4.5106, val loss 4.5286 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 7750: loss 4.3220, time 20434.19ms, mfu 0.05% +step 8000: train loss 4.4731, val loss 4.5230 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 8000: loss 4.4719, time 20347.63ms, mfu 0.05% +step 8250: train loss 4.4851, val loss 4.4917 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 8250: loss 4.4653, time 20500.43ms, mfu 0.05% +step 8500: train loss 4.4484, val loss 4.4888 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 8500: loss 4.8218, time 20311.62ms, mfu 0.05% +step 8750: train loss 4.4367, val loss 4.4591 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 8750: loss 4.6956, time 20322.49ms, mfu 0.05% +step 9000: train loss 4.4071, val loss 4.4581 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 9000: loss 4.8043, time 20753.70ms, mfu 0.05% +step 9250: train loss 4.4568, val loss 4.4259 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 9250: loss 4.2726, time 20841.20ms, mfu 0.05% +step 9500: train loss 4.4221, val loss 4.4169 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 9500: loss 4.5823, time 20515.89ms, mfu 0.05% +step 9750: train loss 4.3996, val loss 4.3945 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 9750: loss 4.3107, time 20777.86ms, mfu 0.05% +step 10000: train loss 4.4194, val loss 4.4295 +iter 10000: loss 4.1373, time 18831.41ms, mfu 0.05% diff --git a/results/logs/train_qa_headspec_w2000_seed42.log b/results/logs/train_qa_headspec_w2000_seed42.log new file mode 100644 index 0000000000000000000000000000000000000000..0508343a15de7cb0f157f58cb16f5377d475d778 --- /dev/null +++ b/results/logs/train_qa_headspec_w2000_seed42.log @@ -0,0 +1,128 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 124.77M +C:\Users\brian\documents\nanogpt\train.py:203: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 125,140,992 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9598, val loss 10.9611 +iter 0: loss 10.9579, time 19004.96ms, mfu -100.00% +step 250: train loss 6.9327, val loss 6.9332 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 250: loss 6.5166, time 20177.69ms, mfu 0.05% +step 500: train loss 6.5386, val loss 6.4968 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 500: loss 6.6497, time 20412.68ms, mfu 0.05% +step 750: train loss 6.3013, val loss 6.3286 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 750: loss 6.3072, time 20287.34ms, mfu 0.05% +step 1000: train loss 6.1250, val loss 6.1762 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 1000: loss 5.8551, time 20828.36ms, mfu 0.05% +step 1250: train loss 6.0471, val loss 6.0357 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 1250: loss 5.6450, time 20861.16ms, mfu 0.05% +step 1500: train loss 5.9710, val loss 5.9761 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 1500: loss 5.9467, time 21821.40ms, mfu 0.05% +step 1750: train loss 5.8578, val loss 5.8291 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 1750: loss 5.5882, time 20584.62ms, mfu 0.05% +step 2000: train loss 5.7453, val loss 5.7516 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 2000: loss 5.6083, time 20513.75ms, mfu 0.05% +step 2250: train loss 5.6416, val loss 5.6419 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 2250: loss 5.6385, time 20598.32ms, mfu 0.05% +step 2500: train loss 5.5380, val loss 5.5473 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 2500: loss 5.3579, time 20280.59ms, mfu 0.05% +step 2750: train loss 5.4653, val loss 5.4358 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 2750: loss 5.3950, time 20300.33ms, mfu 0.05% +step 3000: train loss 5.4144, val loss 5.4052 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 3000: loss 5.2847, time 20571.41ms, mfu 0.05% +step 3250: train loss 5.3480, val loss 5.3373 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 3250: loss 5.3535, time 20946.47ms, mfu 0.05% +step 3500: train loss 5.2326, val loss 5.2960 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 3500: loss 5.4146, time 20369.65ms, mfu 0.05% +step 3750: train loss 5.2372, val loss 5.2149 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 3750: loss 5.1598, time 20619.72ms, mfu 0.05% +step 4000: train loss 5.1300, val loss 5.1788 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 4000: loss 5.1419, time 20625.45ms, mfu 0.05% +step 4250: train loss 5.1188, val loss 5.1158 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 4250: loss 5.3151, time 20448.54ms, mfu 0.05% +step 4500: train loss 5.0449, val loss 5.0482 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 4500: loss 5.0694, time 20497.45ms, mfu 0.05% +step 4750: train loss 4.9606, val loss 5.0125 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 4750: loss 5.0434, time 20421.86ms, mfu 0.05% +step 5000: train loss 4.9887, val loss 4.9376 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 5000: loss 4.7296, time 20559.37ms, mfu 0.05% +step 5250: train loss 4.8894, val loss 4.9054 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 5250: loss 5.0293, time 20658.12ms, mfu 0.05% +step 5500: train loss 4.8079, val loss 4.8471 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 5500: loss 4.4874, time 20400.73ms, mfu 0.05% +step 5750: train loss 4.7981, val loss 4.7858 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 5750: loss 4.5802, time 20404.19ms, mfu 0.05% +step 6000: train loss 4.7263, val loss 4.7704 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 6000: loss 5.0580, time 20539.48ms, mfu 0.05% +step 6250: train loss 4.6922, val loss 4.7195 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 6250: loss 4.5884, time 20647.96ms, mfu 0.05% +step 6500: train loss 4.6707, val loss 4.6739 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 6500: loss 4.6810, time 20507.28ms, mfu 0.05% +step 6750: train loss 4.6387, val loss 4.6292 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 6750: loss 4.2705, time 20606.44ms, mfu 0.05% +step 7000: train loss 4.6065, val loss 4.6001 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 7000: loss 4.5458, time 20511.49ms, mfu 0.05% +step 7250: train loss 4.5922, val loss 4.5775 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 7250: loss 4.5294, time 20328.16ms, mfu 0.05% +step 7500: train loss 4.5505, val loss 4.5761 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 7500: loss 4.4436, time 20407.30ms, mfu 0.05% +step 7750: train loss 4.5603, val loss 4.5375 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 7750: loss 4.5215, time 20377.85ms, mfu 0.05% +step 8000: train loss 4.5196, val loss 4.5244 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 8000: loss 4.7308, time 20476.03ms, mfu 0.05% +step 8250: train loss 4.4735, val loss 4.5092 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 8250: loss 5.0785, time 20459.48ms, mfu 0.05% +step 8500: train loss 4.4745, val loss 4.4819 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 8500: loss 4.5368, time 20561.51ms, mfu 0.05% +step 8750: train loss 4.4570, val loss 4.4445 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 8750: loss 4.4526, time 20395.19ms, mfu 0.05% +step 9000: train loss 4.4328, val loss 4.4236 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 9000: loss 4.3536, time 20621.79ms, mfu 0.05% +step 9250: train loss 4.4325, val loss 4.4025 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 9250: loss 4.2916, time 20867.75ms, mfu 0.05% +step 9500: train loss 4.3986, val loss 4.4181 +iter 9500: loss 4.6344, time 18843.68ms, mfu 0.05% +step 9750: train loss 4.4144, val loss 4.4004 +saving checkpoint to out-openwebtext_phase4_qa_headspec +iter 9750: loss 4.5920, time 20605.15ms, mfu 0.05% +step 10000: train loss 4.4320, val loss 4.4005 +iter 10000: loss 4.3871, time 18910.73ms, mfu 0.05% diff --git a/results/logs/train_static_prehead_seed123.log b/results/logs/train_static_prehead_seed123.log new file mode 100644 index 0000000000000000000000000000000000000000..1debf1da76ff701005a0667a3d1e1c89c6fd4556 --- /dev/null +++ b/results/logs/train_static_prehead_seed123.log @@ -0,0 +1,126 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.60M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 123,970,560 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9594, val loss 10.9585 +iter 0: loss 10.9765, time 17940.17ms, mfu -100.00% +step 250: train loss 6.9611, val loss 6.9069 +saving checkpoint to out-openwebtext_rerun_shared +iter 250: loss 6.6577, time 19337.17ms, mfu 0.05% +step 500: train loss 6.4898, val loss 6.5664 +saving checkpoint to out-openwebtext_rerun_shared +iter 500: loss 6.5983, time 20267.61ms, mfu 0.05% +step 750: train loss 6.3091, val loss 6.3137 +saving checkpoint to out-openwebtext_rerun_shared +iter 750: loss 5.6393, time 20162.08ms, mfu 0.05% +step 1000: train loss 6.1905, val loss 6.1895 +saving checkpoint to out-openwebtext_rerun_shared +iter 1000: loss 6.4894, time 20204.55ms, mfu 0.05% +step 1250: train loss 6.0205, val loss 6.0481 +saving checkpoint to out-openwebtext_rerun_shared +iter 1250: loss 5.7661, time 19865.70ms, mfu 0.05% +step 1500: train loss 5.9799, val loss 5.9497 +saving checkpoint to out-openwebtext_rerun_shared +iter 1500: loss 5.7764, time 20167.88ms, mfu 0.05% +step 1750: train loss 5.8743, val loss 5.9126 +saving checkpoint to out-openwebtext_rerun_shared +iter 1750: loss 6.5066, time 20448.90ms, mfu 0.05% +step 2000: train loss 5.8144, val loss 5.8523 +saving checkpoint to out-openwebtext_rerun_shared +iter 2000: loss 5.7585, time 20182.09ms, mfu 0.05% +step 2250: train loss 5.6531, val loss 5.7110 +saving checkpoint to out-openwebtext_rerun_shared +iter 2250: loss 6.1416, time 20264.91ms, mfu 0.05% +step 2500: train loss 5.6265, val loss 5.6316 +saving checkpoint to out-openwebtext_rerun_shared +iter 2500: loss 5.8583, time 20175.78ms, mfu 0.05% +step 2750: train loss 5.5600, val loss 5.5333 +saving checkpoint to out-openwebtext_rerun_shared +iter 2750: loss 6.0901, time 19911.52ms, mfu 0.05% +step 3000: train loss 5.4712, val loss 5.4740 +saving checkpoint to out-openwebtext_rerun_shared +iter 3000: loss 6.2075, time 19864.62ms, mfu 0.05% +step 3250: train loss 5.3702, val loss 5.3662 +saving checkpoint to out-openwebtext_rerun_shared +iter 3250: loss 5.3279, time 19855.07ms, mfu 0.05% +step 3500: train loss 5.3037, val loss 5.3502 +saving checkpoint to out-openwebtext_rerun_shared +iter 3500: loss 5.3613, time 19841.98ms, mfu 0.05% +step 3750: train loss 5.2672, val loss 5.2519 +saving checkpoint to out-openwebtext_rerun_shared +iter 3750: loss 5.0321, time 19748.97ms, mfu 0.05% +step 4000: train loss 5.2178, val loss 5.2289 +saving checkpoint to out-openwebtext_rerun_shared +iter 4000: loss 5.1394, time 19428.41ms, mfu 0.05% +step 4250: train loss 5.1757, val loss 5.1863 +saving checkpoint to out-openwebtext_rerun_shared +iter 4250: loss 5.2375, time 20081.08ms, mfu 0.05% +step 4500: train loss 5.1027, val loss 5.1043 +saving checkpoint to out-openwebtext_rerun_shared +iter 4500: loss 5.1631, time 20247.52ms, mfu 0.05% +step 4750: train loss 5.0881, val loss 5.0843 +saving checkpoint to out-openwebtext_rerun_shared +iter 4750: loss 4.9750, time 20320.59ms, mfu 0.05% +step 5000: train loss 4.9971, val loss 4.9959 +saving checkpoint to out-openwebtext_rerun_shared +iter 5000: loss 5.1270, time 19703.84ms, mfu 0.05% +step 5250: train loss 4.9835, val loss 4.9708 +saving checkpoint to out-openwebtext_rerun_shared +iter 5250: loss 5.1620, time 19844.66ms, mfu 0.05% +step 5500: train loss 4.9022, val loss 4.9401 +saving checkpoint to out-openwebtext_rerun_shared +iter 5500: loss 4.7989, time 20296.94ms, mfu 0.05% +step 5750: train loss 4.9243, val loss 4.9046 +saving checkpoint to out-openwebtext_rerun_shared +iter 5750: loss 5.2501, time 20250.15ms, mfu 0.05% +step 6000: train loss 4.8699, val loss 4.8389 +saving checkpoint to out-openwebtext_rerun_shared +iter 6000: loss 4.8798, time 19764.29ms, mfu 0.05% +step 6250: train loss 4.7956, val loss 4.8488 +iter 6250: loss 5.0271, time 18187.94ms, mfu 0.05% +step 6500: train loss 4.7592, val loss 4.7660 +saving checkpoint to out-openwebtext_rerun_shared +iter 6500: loss 4.6352, time 20237.10ms, mfu 0.05% +step 6750: train loss 4.7142, val loss 4.7359 +saving checkpoint to out-openwebtext_rerun_shared +iter 6750: loss 4.5783, time 20434.93ms, mfu 0.05% +step 7000: train loss 4.6790, val loss 4.6877 +saving checkpoint to out-openwebtext_rerun_shared +iter 7000: loss 4.6195, time 20262.29ms, mfu 0.05% +step 7250: train loss 4.6506, val loss 4.6781 +saving checkpoint to out-openwebtext_rerun_shared +iter 7250: loss 4.2664, time 19936.76ms, mfu 0.05% +step 7500: train loss 4.6580, val loss 4.6395 +saving checkpoint to out-openwebtext_rerun_shared +iter 7500: loss 4.5987, time 19904.72ms, mfu 0.05% +step 7750: train loss 4.6078, val loss 4.6037 +saving checkpoint to out-openwebtext_rerun_shared +iter 7750: loss 4.8012, time 20139.25ms, mfu 0.05% +step 8000: train loss 4.5832, val loss 4.5790 +saving checkpoint to out-openwebtext_rerun_shared +iter 8000: loss 4.1934, time 20526.58ms, mfu 0.05% +step 8250: train loss 4.5383, val loss 4.5358 +saving checkpoint to out-openwebtext_rerun_shared +iter 8250: loss 4.3608, time 19760.66ms, mfu 0.05% +step 8500: train loss 4.5245, val loss 4.5444 +iter 8500: loss 4.3462, time 18174.37ms, mfu 0.05% +step 8750: train loss 4.5036, val loss 4.5170 +saving checkpoint to out-openwebtext_rerun_shared +iter 8750: loss 4.3588, time 19684.72ms, mfu 0.05% +step 9000: train loss 4.5158, val loss 4.4975 +saving checkpoint to out-openwebtext_rerun_shared +iter 9000: loss 4.2739, time 20074.10ms, mfu 0.05% +step 9250: train loss 4.4655, val loss 4.4908 +saving checkpoint to out-openwebtext_rerun_shared +iter 9250: loss 5.0094, time 19820.18ms, mfu 0.05% +step 9500: train loss 4.4609, val loss 4.4559 +saving checkpoint to out-openwebtext_rerun_shared +iter 9500: loss 4.9194, time 20064.32ms, mfu 0.05% +step 9750: train loss 4.4836, val loss 4.5074 +iter 9750: loss 4.4104, time 18149.13ms, mfu 0.05% +step 10000: train loss 4.4701, val loss 4.4777 +iter 10000: loss 4.6073, time 18234.34ms, mfu 0.05% diff --git a/results/logs/train_static_prehead_seed1337.log b/results/logs/train_static_prehead_seed1337.log new file mode 100644 index 0000000000000000000000000000000000000000..53e38b4017b7932221719f3dc22504cb2ac4c965 --- /dev/null +++ b/results/logs/train_static_prehead_seed1337.log @@ -0,0 +1,126 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.60M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 123,970,560 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9797, val loss 10.9790 +iter 0: loss 11.0013, time 17869.34ms, mfu -100.00% +step 250: train loss 6.9363, val loss 6.9696 +saving checkpoint to out-openwebtext_rerun_shared +iter 250: loss 6.7253, time 19307.26ms, mfu 0.05% +step 500: train loss 6.4777, val loss 6.5451 +saving checkpoint to out-openwebtext_rerun_shared +iter 500: loss 6.2608, time 19318.54ms, mfu 0.05% +step 750: train loss 6.3010, val loss 6.3186 +saving checkpoint to out-openwebtext_rerun_shared +iter 750: loss 6.0866, time 19174.70ms, mfu 0.05% +step 1000: train loss 6.1738, val loss 6.1470 +saving checkpoint to out-openwebtext_rerun_shared +iter 1000: loss 6.1500, time 19344.49ms, mfu 0.05% +step 1250: train loss 6.0577, val loss 6.0501 +saving checkpoint to out-openwebtext_rerun_shared +iter 1250: loss 6.0931, time 19234.99ms, mfu 0.05% +step 1500: train loss 5.9933, val loss 5.9171 +saving checkpoint to out-openwebtext_rerun_shared +iter 1500: loss 5.7086, time 19372.42ms, mfu 0.05% +step 1750: train loss 5.9092, val loss 5.8722 +saving checkpoint to out-openwebtext_rerun_shared +iter 1750: loss 6.0696, time 19896.84ms, mfu 0.05% +step 2000: train loss 5.8108, val loss 5.7812 +saving checkpoint to out-openwebtext_rerun_shared +iter 2000: loss 5.8443, time 20153.43ms, mfu 0.05% +step 2250: train loss 5.6885, val loss 5.6941 +saving checkpoint to out-openwebtext_rerun_shared +iter 2250: loss 5.8955, time 19784.42ms, mfu 0.05% +step 2500: train loss 5.5932, val loss 5.5722 +saving checkpoint to out-openwebtext_rerun_shared +iter 2500: loss 6.0556, time 19835.77ms, mfu 0.05% +step 2750: train loss 5.5103, val loss 5.5028 +saving checkpoint to out-openwebtext_rerun_shared +iter 2750: loss 5.5081, time 19849.48ms, mfu 0.05% +step 3000: train loss 5.4236, val loss 5.4415 +saving checkpoint to out-openwebtext_rerun_shared +iter 3000: loss 5.4906, time 20137.08ms, mfu 0.05% +step 3250: train loss 5.3281, val loss 5.3341 +saving checkpoint to out-openwebtext_rerun_shared +iter 3250: loss 5.2934, time 19932.10ms, mfu 0.05% +step 3500: train loss 5.3371, val loss 5.2998 +saving checkpoint to out-openwebtext_rerun_shared +iter 3500: loss 5.2238, time 19931.09ms, mfu 0.05% +step 3750: train loss 5.2446, val loss 5.2326 +saving checkpoint to out-openwebtext_rerun_shared +iter 3750: loss 5.5530, time 19788.00ms, mfu 0.05% +step 4000: train loss 5.1971, val loss 5.1996 +saving checkpoint to out-openwebtext_rerun_shared +iter 4000: loss 5.2560, time 19275.87ms, mfu 0.05% +step 4250: train loss 5.1441, val loss 5.1573 +saving checkpoint to out-openwebtext_rerun_shared +iter 4250: loss 4.7499, time 19790.81ms, mfu 0.05% +step 4500: train loss 5.0993, val loss 5.0914 +saving checkpoint to out-openwebtext_rerun_shared +iter 4500: loss 4.8418, time 20029.28ms, mfu 0.05% +step 4750: train loss 5.0386, val loss 5.0487 +saving checkpoint to out-openwebtext_rerun_shared +iter 4750: loss 5.1462, time 19721.30ms, mfu 0.05% +step 5000: train loss 4.9718, val loss 5.0090 +saving checkpoint to out-openwebtext_rerun_shared +iter 5000: loss 5.1591, time 20147.85ms, mfu 0.05% +step 5250: train loss 4.9672, val loss 4.9525 +saving checkpoint to out-openwebtext_rerun_shared +iter 5250: loss 4.6187, time 20372.44ms, mfu 0.05% +step 5500: train loss 4.9482, val loss 4.9098 +saving checkpoint to out-openwebtext_rerun_shared +iter 5500: loss 5.1646, time 19786.75ms, mfu 0.05% +step 5750: train loss 4.8786, val loss 4.8386 +saving checkpoint to out-openwebtext_rerun_shared +iter 5750: loss 5.1269, time 20056.09ms, mfu 0.05% +step 6000: train loss 4.7852, val loss 4.7896 +saving checkpoint to out-openwebtext_rerun_shared +iter 6000: loss 4.6789, time 20154.04ms, mfu 0.05% +step 6250: train loss 4.7645, val loss 4.7962 +iter 6250: loss 4.5706, time 18240.35ms, mfu 0.05% +step 6500: train loss 4.7267, val loss 4.7615 +saving checkpoint to out-openwebtext_rerun_shared +iter 6500: loss 4.9074, time 20207.04ms, mfu 0.05% +step 6750: train loss 4.6923, val loss 4.6991 +saving checkpoint to out-openwebtext_rerun_shared +iter 6750: loss 4.8792, time 20150.57ms, mfu 0.05% +step 7000: train loss 4.6574, val loss 4.6533 +saving checkpoint to out-openwebtext_rerun_shared +iter 7000: loss 4.5625, time 20298.82ms, mfu 0.05% +step 7250: train loss 4.6120, val loss 4.6422 +saving checkpoint to out-openwebtext_rerun_shared +iter 7250: loss 4.7262, time 20416.51ms, mfu 0.05% +step 7500: train loss 4.6216, val loss 4.5774 +saving checkpoint to out-openwebtext_rerun_shared +iter 7500: loss 4.4983, time 20477.05ms, mfu 0.05% +step 7750: train loss 4.5701, val loss 4.5632 +saving checkpoint to out-openwebtext_rerun_shared +iter 7750: loss 4.7535, time 20334.46ms, mfu 0.05% +step 8000: train loss 4.5329, val loss 4.5565 +saving checkpoint to out-openwebtext_rerun_shared +iter 8000: loss 4.5902, time 19831.93ms, mfu 0.05% +step 8250: train loss 4.5099, val loss 4.5400 +saving checkpoint to out-openwebtext_rerun_shared +iter 8250: loss 4.2967, time 20145.05ms, mfu 0.05% +step 8500: train loss 4.5072, val loss 4.5162 +saving checkpoint to out-openwebtext_rerun_shared +iter 8500: loss 4.2451, time 19866.00ms, mfu 0.05% +step 8750: train loss 4.4704, val loss 4.4724 +saving checkpoint to out-openwebtext_rerun_shared +iter 8750: loss 4.4906, time 20100.74ms, mfu 0.05% +step 9000: train loss 4.4839, val loss 4.4884 +iter 9000: loss 4.5710, time 18196.53ms, mfu 0.05% +step 9250: train loss 4.4455, val loss 4.4766 +iter 9250: loss 4.5147, time 18197.45ms, mfu 0.05% +step 9500: train loss 4.4517, val loss 4.4631 +saving checkpoint to out-openwebtext_rerun_shared +iter 9500: loss 4.4707, time 20164.18ms, mfu 0.05% +step 9750: train loss 4.4562, val loss 4.4340 +saving checkpoint to out-openwebtext_rerun_shared +iter 9750: loss 4.3577, time 20186.83ms, mfu 0.05% +step 10000: train loss 4.4490, val loss 4.4742 +iter 10000: loss 4.4786, time 18117.43ms, mfu 0.05% diff --git a/results/logs/train_static_prehead_seed42.log b/results/logs/train_static_prehead_seed42.log new file mode 100644 index 0000000000000000000000000000000000000000..1631918767b5be355b6e48c5152df4287c231fcc --- /dev/null +++ b/results/logs/train_static_prehead_seed42.log @@ -0,0 +1,125 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.60M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 123,970,560 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9682, val loss 10.9677 +iter 0: loss 10.9624, time 18127.70ms, mfu -100.00% +step 250: train loss 6.9873, val loss 6.9835 +saving checkpoint to out-openwebtext_rerun_shared +iter 250: loss 7.0970, time 19627.78ms, mfu 0.05% +step 500: train loss 6.5065, val loss 6.5115 +saving checkpoint to out-openwebtext_rerun_shared +iter 500: loss 6.1548, time 19340.59ms, mfu 0.05% +step 750: train loss 6.2889, val loss 6.3204 +saving checkpoint to out-openwebtext_rerun_shared +iter 750: loss 6.6195, time 19183.41ms, mfu 0.05% +step 1000: train loss 6.1638, val loss 6.1607 +saving checkpoint to out-openwebtext_rerun_shared +iter 1000: loss 5.8180, time 19250.81ms, mfu 0.05% +step 1250: train loss 6.0059, val loss 6.0236 +saving checkpoint to out-openwebtext_rerun_shared +iter 1250: loss 6.5209, time 19189.72ms, mfu 0.05% +step 1500: train loss 5.9646, val loss 5.9579 +saving checkpoint to out-openwebtext_rerun_shared +iter 1500: loss 5.4895, time 19146.74ms, mfu 0.05% +step 1750: train loss 5.8735, val loss 5.8854 +saving checkpoint to out-openwebtext_rerun_shared +iter 1750: loss 6.1767, time 19312.43ms, mfu 0.05% +step 2000: train loss 5.8002, val loss 5.8123 +saving checkpoint to out-openwebtext_rerun_shared +iter 2000: loss 5.6055, time 19301.09ms, mfu 0.05% +step 2250: train loss 5.6611, val loss 5.6741 +saving checkpoint to out-openwebtext_rerun_shared +iter 2250: loss 5.7555, time 19495.18ms, mfu 0.05% +step 2500: train loss 5.5981, val loss 5.5940 +saving checkpoint to out-openwebtext_rerun_shared +iter 2500: loss 4.9232, time 20318.00ms, mfu 0.05% +step 2750: train loss 5.5083, val loss 5.5042 +saving checkpoint to out-openwebtext_rerun_shared +iter 2750: loss 5.5406, time 20180.09ms, mfu 0.05% +step 3000: train loss 5.4337, val loss 5.4194 +saving checkpoint to out-openwebtext_rerun_shared +iter 3000: loss 5.4812, time 19611.98ms, mfu 0.05% +step 3250: train loss 5.3883, val loss 5.3449 +saving checkpoint to out-openwebtext_rerun_shared +iter 3250: loss 5.5516, time 19349.88ms, mfu 0.05% +step 3500: train loss 5.2938, val loss 5.3456 +iter 3500: loss 5.4241, time 18145.04ms, mfu 0.05% +step 3750: train loss 5.2583, val loss 5.2814 +saving checkpoint to out-openwebtext_rerun_shared +iter 3750: loss 5.3021, time 19785.70ms, mfu 0.05% +step 4000: train loss 5.2098, val loss 5.2147 +saving checkpoint to out-openwebtext_rerun_shared +iter 4000: loss 5.8809, time 20262.53ms, mfu 0.05% +step 4250: train loss 5.1693, val loss 5.1547 +saving checkpoint to out-openwebtext_rerun_shared +iter 4250: loss 4.8967, time 19834.49ms, mfu 0.05% +step 4500: train loss 5.0857, val loss 5.1120 +saving checkpoint to out-openwebtext_rerun_shared +iter 4500: loss 5.1966, time 20197.86ms, mfu 0.05% +step 4750: train loss 5.0681, val loss 5.0741 +saving checkpoint to out-openwebtext_rerun_shared +iter 4750: loss 5.1653, time 19294.15ms, mfu 0.05% +step 5000: train loss 5.0148, val loss 4.9889 +saving checkpoint to out-openwebtext_rerun_shared +iter 5000: loss 5.4418, time 19250.52ms, mfu 0.05% +step 5250: train loss 4.9552, val loss 4.9577 +saving checkpoint to out-openwebtext_rerun_shared +iter 5250: loss 5.0776, time 19798.29ms, mfu 0.05% +step 5500: train loss 4.9456, val loss 4.9336 +saving checkpoint to out-openwebtext_rerun_shared +iter 5500: loss 4.6919, time 19748.73ms, mfu 0.05% +step 5750: train loss 4.8688, val loss 4.8734 +saving checkpoint to out-openwebtext_rerun_shared +iter 5750: loss 5.0975, time 19967.70ms, mfu 0.05% +step 6000: train loss 4.8107, val loss 4.8159 +saving checkpoint to out-openwebtext_rerun_shared +iter 6000: loss 4.7419, time 19405.03ms, mfu 0.05% +step 6250: train loss 4.7656, val loss 4.7774 +saving checkpoint to out-openwebtext_rerun_shared +iter 6250: loss 4.4581, time 19245.91ms, mfu 0.05% +step 6500: train loss 4.7652, val loss 4.7450 +saving checkpoint to out-openwebtext_rerun_shared +iter 6500: loss 4.6060, time 19315.13ms, mfu 0.05% +step 6750: train loss 4.6993, val loss 4.6855 +saving checkpoint to out-openwebtext_rerun_shared +iter 6750: loss 4.6537, time 19233.51ms, mfu 0.05% +step 7000: train loss 4.6526, val loss 4.6764 +saving checkpoint to out-openwebtext_rerun_shared +iter 7000: loss 4.6395, time 19865.11ms, mfu 0.05% +step 7250: train loss 4.6321, val loss 4.6303 +saving checkpoint to out-openwebtext_rerun_shared +iter 7250: loss 4.8298, time 19205.05ms, mfu 0.05% +step 7500: train loss 4.6301, val loss 4.6111 +saving checkpoint to out-openwebtext_rerun_shared +iter 7500: loss 4.7316, time 19187.12ms, mfu 0.05% +step 7750: train loss 4.6051, val loss 4.5827 +saving checkpoint to out-openwebtext_rerun_shared +iter 7750: loss 4.6957, time 19184.52ms, mfu 0.05% +step 8000: train loss 4.5623, val loss 4.5637 +saving checkpoint to out-openwebtext_rerun_shared +iter 8000: loss 4.5431, time 19143.82ms, mfu 0.05% +step 8250: train loss 4.5209, val loss 4.5406 +saving checkpoint to out-openwebtext_rerun_shared +iter 8250: loss 4.3536, time 19245.11ms, mfu 0.05% +step 8500: train loss 4.5286, val loss 4.5583 +iter 8500: loss 4.3886, time 17719.72ms, mfu 0.05% +step 8750: train loss 4.4823, val loss 4.5045 +saving checkpoint to out-openwebtext_rerun_shared +iter 8750: loss 4.6599, time 19254.91ms, mfu 0.05% +step 9000: train loss 4.4965, val loss 4.5172 +iter 9000: loss 4.4744, time 17618.39ms, mfu 0.06% +step 9250: train loss 4.4745, val loss 4.4649 +saving checkpoint to out-openwebtext_rerun_shared +iter 9250: loss 4.5835, time 19800.23ms, mfu 0.05% +step 9500: train loss 4.4473, val loss 4.4651 +iter 9500: loss 4.1806, time 17631.67ms, mfu 0.06% +step 9750: train loss 4.4425, val loss 4.4550 +saving checkpoint to out-openwebtext_rerun_shared +iter 9750: loss 4.7760, time 19352.06ms, mfu 0.06% +step 10000: train loss 4.4411, val loss 4.4699 +iter 10000: loss 4.4480, time 17651.47ms, mfu 0.06% diff --git a/results/logs/train_x_full_headspec_w2000_seed123.log b/results/logs/train_x_full_headspec_w2000_seed123.log new file mode 100644 index 0000000000000000000000000000000000000000..a966feb858b443f984d44d8fb8b98cff841648ca --- /dev/null +++ b/results/logs/train_x_full_headspec_w2000_seed123.log @@ -0,0 +1,128 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 130.67M +C:\Users\brian\documents\nanogpt\train.py:203: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 131,039,232 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9910, val loss 10.9893 +iter 0: loss 10.9860, time 18596.60ms, mfu -100.00% +step 250: train loss 6.9332, val loss 6.9612 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 250: loss 7.3043, time 20133.56ms, mfu 0.05% +step 500: train loss 6.5075, val loss 6.5197 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 500: loss 6.3071, time 20044.90ms, mfu 0.05% +step 750: train loss 6.3529, val loss 6.3825 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 750: loss 6.1965, time 19954.54ms, mfu 0.05% +step 1000: train loss 6.1366, val loss 6.1671 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 1000: loss 6.3665, time 19850.45ms, mfu 0.05% +step 1250: train loss 6.0423, val loss 6.0399 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 1250: loss 6.4023, time 19923.09ms, mfu 0.05% +step 1500: train loss 5.9332, val loss 5.9510 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 1500: loss 5.9334, time 20197.75ms, mfu 0.05% +step 1750: train loss 5.8187, val loss 5.8263 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 1750: loss 5.5783, time 20095.98ms, mfu 0.05% +step 2000: train loss 5.7877, val loss 5.7579 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 2000: loss 6.0573, time 19927.30ms, mfu 0.06% +step 2250: train loss 5.5998, val loss 5.6358 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 2250: loss 5.5991, time 19878.83ms, mfu 0.06% +step 2500: train loss 5.5329, val loss 5.5306 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 2500: loss 5.6551, time 19918.90ms, mfu 0.06% +step 2750: train loss 5.4423, val loss 5.4422 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 2750: loss 5.8096, time 19916.81ms, mfu 0.06% +step 3000: train loss 5.3589, val loss 5.3766 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 3000: loss 5.0763, time 20361.36ms, mfu 0.06% +step 3250: train loss 5.2938, val loss 5.2856 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 3250: loss 5.6414, time 20625.76ms, mfu 0.05% +step 3500: train loss 5.2475, val loss 5.2094 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 3500: loss 5.8965, time 20096.18ms, mfu 0.05% +step 3750: train loss 5.2026, val loss 5.1840 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 3750: loss 5.0214, time 19942.20ms, mfu 0.05% +step 4000: train loss 5.1792, val loss 5.1151 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 4000: loss 4.8754, time 19950.39ms, mfu 0.05% +step 4250: train loss 5.0756, val loss 5.0931 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 4250: loss 4.7157, time 20193.00ms, mfu 0.05% +step 4500: train loss 5.0089, val loss 5.0291 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 4500: loss 5.2480, time 19981.12ms, mfu 0.05% +step 4750: train loss 4.9651, val loss 4.9817 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 4750: loss 5.0998, time 19923.48ms, mfu 0.06% +step 5000: train loss 4.9176, val loss 4.9016 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 5000: loss 5.0708, time 19883.51ms, mfu 0.06% +step 5250: train loss 4.9083, val loss 4.8731 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 5250: loss 4.8879, time 19900.77ms, mfu 0.06% +step 5500: train loss 4.8022, val loss 4.8138 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 5500: loss 4.5656, time 20164.64ms, mfu 0.06% +step 5750: train loss 4.7591, val loss 4.7644 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 5750: loss 4.6502, time 19837.17ms, mfu 0.06% +step 6000: train loss 4.7500, val loss 4.7420 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 6000: loss 4.4519, time 20227.35ms, mfu 0.06% +step 6250: train loss 4.6850, val loss 4.6745 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 6250: loss 4.6789, time 20038.08ms, mfu 0.06% +step 6500: train loss 4.6226, val loss 4.6562 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 6500: loss 4.6986, time 19933.49ms, mfu 0.06% +step 6750: train loss 4.6199, val loss 4.6353 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 6750: loss 4.6439, time 19963.53ms, mfu 0.06% +step 7000: train loss 4.5867, val loss 4.5862 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 7000: loss 4.5977, time 19911.75ms, mfu 0.06% +step 7250: train loss 4.5622, val loss 4.5778 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 7250: loss 4.4473, time 19879.03ms, mfu 0.06% +step 7500: train loss 4.5327, val loss 4.5358 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 7500: loss 4.2148, time 20115.08ms, mfu 0.06% +step 7750: train loss 4.5145, val loss 4.5254 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 7750: loss 4.1869, time 20025.94ms, mfu 0.06% +step 8000: train loss 4.5217, val loss 4.4950 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 8000: loss 4.4561, time 19861.65ms, mfu 0.06% +step 8250: train loss 4.4954, val loss 4.4821 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 8250: loss 4.8224, time 20000.92ms, mfu 0.06% +step 8500: train loss 4.4483, val loss 4.4612 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 8500: loss 4.8030, time 20006.41ms, mfu 0.06% +step 8750: train loss 4.4448, val loss 4.4289 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 8750: loss 4.3102, time 19821.02ms, mfu 0.06% +step 9000: train loss 4.4559, val loss 4.4271 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 9000: loss 4.5740, time 20173.08ms, mfu 0.06% +step 9250: train loss 4.4093, val loss 4.4203 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 9250: loss 4.6887, time 19951.76ms, mfu 0.06% +step 9500: train loss 4.3919, val loss 4.4335 +iter 9500: loss 4.1700, time 18285.65ms, mfu 0.06% +step 9750: train loss 4.3950, val loss 4.3867 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 9750: loss 4.4964, time 19750.42ms, mfu 0.06% +step 10000: train loss 4.3692, val loss 4.4239 +iter 10000: loss 4.2471, time 18319.85ms, mfu 0.06% diff --git a/results/logs/train_x_full_headspec_w2000_seed1337.log b/results/logs/train_x_full_headspec_w2000_seed1337.log new file mode 100644 index 0000000000000000000000000000000000000000..82a7954dbe31b98d9171b1c5888dedd2e10139f8 --- /dev/null +++ b/results/logs/train_x_full_headspec_w2000_seed1337.log @@ -0,0 +1,128 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 130.67M +C:\Users\brian\documents\nanogpt\train.py:203: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 131,039,232 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9528, val loss 10.9515 +iter 0: loss 10.9586, time 18662.09ms, mfu -100.00% +step 250: train loss 6.9799, val loss 6.9358 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 250: loss 6.7884, time 19954.02ms, mfu 0.06% +step 500: train loss 6.5036, val loss 6.4820 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 500: loss 6.3523, time 20255.01ms, mfu 0.06% +step 750: train loss 6.2883, val loss 6.3085 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 750: loss 6.7465, time 20132.43ms, mfu 0.06% +step 1000: train loss 6.1800, val loss 6.1701 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 1000: loss 5.6163, time 20007.53ms, mfu 0.06% +step 1250: train loss 6.0586, val loss 6.0321 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 1250: loss 5.9724, time 19971.88ms, mfu 0.06% +step 1500: train loss 5.9192, val loss 5.9396 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 1500: loss 6.3574, time 19992.69ms, mfu 0.06% +step 1750: train loss 5.8569, val loss 5.8616 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 1750: loss 5.4773, time 19902.92ms, mfu 0.06% +step 2000: train loss 5.7638, val loss 5.7501 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 2000: loss 5.7976, time 20015.15ms, mfu 0.06% +step 2250: train loss 5.6557, val loss 5.6832 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 2250: loss 5.7293, time 20243.63ms, mfu 0.06% +step 2500: train loss 5.5570, val loss 5.5400 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 2500: loss 5.0920, time 19990.04ms, mfu 0.06% +step 2750: train loss 5.4680, val loss 5.4854 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 2750: loss 4.9632, time 20098.42ms, mfu 0.06% +step 3000: train loss 5.3925, val loss 5.3719 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 3000: loss 5.5654, time 19995.77ms, mfu 0.06% +step 3250: train loss 5.2975, val loss 5.3559 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 3250: loss 5.4716, time 20369.25ms, mfu 0.06% +step 3500: train loss 5.2767, val loss 5.2548 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 3500: loss 5.6111, time 19997.15ms, mfu 0.06% +step 3750: train loss 5.2246, val loss 5.2105 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 3750: loss 5.3125, time 20065.50ms, mfu 0.06% +step 4000: train loss 5.1503, val loss 5.1277 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 4000: loss 5.3060, time 19984.21ms, mfu 0.06% +step 4250: train loss 5.0799, val loss 5.0883 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 4250: loss 4.9323, time 19985.32ms, mfu 0.06% +step 4500: train loss 5.0962, val loss 5.0412 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 4500: loss 5.1972, time 20292.96ms, mfu 0.06% +step 4750: train loss 5.0166, val loss 4.9964 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 4750: loss 4.5318, time 20217.67ms, mfu 0.05% +step 5000: train loss 4.9342, val loss 4.9440 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 5000: loss 5.1747, time 19984.77ms, mfu 0.05% +step 5250: train loss 4.8561, val loss 4.8510 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 5250: loss 4.6674, time 20011.23ms, mfu 0.06% +step 5500: train loss 4.8243, val loss 4.8225 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 5500: loss 4.9419, time 19908.31ms, mfu 0.06% +step 5750: train loss 4.7640, val loss 4.8001 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 5750: loss 4.3768, time 20028.27ms, mfu 0.06% +step 6000: train loss 4.7523, val loss 4.7386 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 6000: loss 4.9444, time 19962.97ms, mfu 0.06% +step 6250: train loss 4.7100, val loss 4.7067 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 6250: loss 4.7686, time 20414.12ms, mfu 0.05% +step 6500: train loss 4.6780, val loss 4.6657 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 6500: loss 4.8617, time 20113.37ms, mfu 0.05% +step 6750: train loss 4.6246, val loss 4.6237 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 6750: loss 4.8303, time 19873.64ms, mfu 0.06% +step 7000: train loss 4.6142, val loss 4.6173 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 7000: loss 4.3361, time 19988.27ms, mfu 0.06% +step 7250: train loss 4.5834, val loss 4.5491 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 7250: loss 4.9713, time 19959.29ms, mfu 0.06% +step 7500: train loss 4.5605, val loss 4.5193 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 7500: loss 4.4240, time 20172.53ms, mfu 0.06% +step 7750: train loss 4.4971, val loss 4.5116 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 7750: loss 4.4543, time 20032.47ms, mfu 0.06% +step 8000: train loss 4.5051, val loss 4.4866 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 8000: loss 4.5383, time 20250.95ms, mfu 0.05% +step 8250: train loss 4.5112, val loss 4.5081 +iter 8250: loss 4.6944, time 18352.73ms, mfu 0.06% +step 8500: train loss 4.4525, val loss 4.4760 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 8500: loss 4.4313, time 19886.64ms, mfu 0.06% +step 8750: train loss 4.4520, val loss 4.4490 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 8750: loss 4.3864, time 19919.81ms, mfu 0.06% +step 9000: train loss 4.4255, val loss 4.4366 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 9000: loss 4.7503, time 19913.57ms, mfu 0.06% +step 9250: train loss 4.4056, val loss 4.4356 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 9250: loss 4.6650, time 20038.06ms, mfu 0.06% +step 9500: train loss 4.4373, val loss 4.4324 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 9500: loss 4.8623, time 20017.53ms, mfu 0.06% +step 9750: train loss 4.4227, val loss 4.4011 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 9750: loss 4.4104, time 19871.77ms, mfu 0.06% +step 10000: train loss 4.3955, val loss 4.4048 +iter 10000: loss 4.2757, time 18371.43ms, mfu 0.06% diff --git a/results/logs/train_x_full_headspec_w2000_seed42.log b/results/logs/train_x_full_headspec_w2000_seed42.log new file mode 100644 index 0000000000000000000000000000000000000000..ab049e69f01328e87a4a0d4b9347fb0c946009dd --- /dev/null +++ b/results/logs/train_x_full_headspec_w2000_seed42.log @@ -0,0 +1,128 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 130.67M +C:\Users\brian\documents\nanogpt\train.py:203: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 131,039,232 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9651, val loss 10.9643 +iter 0: loss 10.9796, time 18587.40ms, mfu -100.00% +step 250: train loss 6.9155, val loss 6.9059 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 250: loss 7.5391, time 19789.15ms, mfu 0.06% +step 500: train loss 6.5074, val loss 6.5132 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 500: loss 6.4112, time 20268.35ms, mfu 0.06% +step 750: train loss 6.2999, val loss 6.3325 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 750: loss 5.9121, time 19860.67ms, mfu 0.06% +step 1000: train loss 6.1522, val loss 6.1752 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 1000: loss 5.7863, time 19916.07ms, mfu 0.06% +step 1250: train loss 6.0648, val loss 6.0561 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 1250: loss 6.4271, time 19982.08ms, mfu 0.06% +step 1500: train loss 5.9200, val loss 5.8795 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 1500: loss 5.6875, time 19905.64ms, mfu 0.06% +step 1750: train loss 5.8301, val loss 5.8152 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 1750: loss 5.6014, time 19970.56ms, mfu 0.06% +step 2000: train loss 5.7607, val loss 5.7459 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 2000: loss 5.6117, time 19937.67ms, mfu 0.06% +step 2250: train loss 5.6295, val loss 5.6291 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 2250: loss 5.5987, time 20024.77ms, mfu 0.06% +step 2500: train loss 5.5548, val loss 5.5114 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 2500: loss 5.4427, time 19955.30ms, mfu 0.06% +step 2750: train loss 5.4341, val loss 5.4494 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 2750: loss 5.6308, time 20215.41ms, mfu 0.06% +step 3000: train loss 5.3669, val loss 5.3859 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 3000: loss 5.7115, time 20008.95ms, mfu 0.06% +step 3250: train loss 5.3269, val loss 5.2722 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 3250: loss 5.5334, time 20018.36ms, mfu 0.06% +step 3500: train loss 5.2614, val loss 5.2476 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 3500: loss 5.0990, time 20035.68ms, mfu 0.06% +step 3750: train loss 5.1959, val loss 5.2070 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 3750: loss 5.4390, time 19907.36ms, mfu 0.06% +step 4000: train loss 5.0921, val loss 5.1305 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 4000: loss 5.3530, time 20276.20ms, mfu 0.06% +step 4250: train loss 5.0775, val loss 5.0774 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 4250: loss 5.3352, time 19877.71ms, mfu 0.06% +step 4500: train loss 5.0195, val loss 5.0042 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 4500: loss 4.9421, time 19941.86ms, mfu 0.06% +step 4750: train loss 4.9503, val loss 4.9835 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 4750: loss 5.0069, time 19925.38ms, mfu 0.06% +step 5000: train loss 4.9274, val loss 4.9295 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 5000: loss 4.6355, time 19903.59ms, mfu 0.06% +step 5250: train loss 4.8568, val loss 4.8726 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 5250: loss 4.4370, time 20344.55ms, mfu 0.06% +step 5500: train loss 4.8099, val loss 4.8131 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 5500: loss 4.4640, time 19942.54ms, mfu 0.06% +step 5750: train loss 4.7812, val loss 4.7833 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 5750: loss 4.9025, time 19902.22ms, mfu 0.06% +step 6000: train loss 4.7308, val loss 4.7225 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 6000: loss 4.8630, time 20000.36ms, mfu 0.06% +step 6250: train loss 4.6828, val loss 4.6914 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 6250: loss 4.4703, time 20280.42ms, mfu 0.06% +step 6500: train loss 4.6552, val loss 4.6621 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 6500: loss 4.6118, time 19930.37ms, mfu 0.06% +step 6750: train loss 4.6161, val loss 4.6319 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 6750: loss 4.6974, time 19826.96ms, mfu 0.06% +step 7000: train loss 4.5969, val loss 4.6157 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 7000: loss 4.1744, time 20161.33ms, mfu 0.06% +step 7250: train loss 4.5688, val loss 4.5705 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 7250: loss 4.4520, time 19926.89ms, mfu 0.06% +step 7500: train loss 4.5876, val loss 4.5346 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 7500: loss 4.5169, time 19910.58ms, mfu 0.06% +step 7750: train loss 4.5252, val loss 4.5294 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 7750: loss 4.6006, time 19947.56ms, mfu 0.06% +step 8000: train loss 4.4801, val loss 4.5224 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 8000: loss 4.6496, time 20329.08ms, mfu 0.06% +step 8250: train loss 4.4884, val loss 4.4774 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 8250: loss 4.7022, time 20255.29ms, mfu 0.06% +step 8500: train loss 4.4997, val loss 4.4509 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 8500: loss 4.5022, time 20170.41ms, mfu 0.06% +step 8750: train loss 4.4542, val loss 4.4620 +iter 8750: loss 3.8438, time 18332.86ms, mfu 0.06% +step 9000: train loss 4.4367, val loss 4.4326 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 9000: loss 3.9998, time 20304.98ms, mfu 0.06% +step 9250: train loss 4.4304, val loss 4.4192 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 9250: loss 4.4905, time 19971.39ms, mfu 0.06% +step 9500: train loss 4.4239, val loss 4.4271 +iter 9500: loss 4.4001, time 18370.55ms, mfu 0.06% +step 9750: train loss 4.4123, val loss 4.4177 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 9750: loss 4.1070, time 19930.87ms, mfu 0.06% +step 10000: train loss 4.3828, val loss 4.4013 +saving checkpoint to out-openwebtext_phase4_x_full_headspec +iter 10000: loss 4.7327, time 19939.45ms, mfu 0.06% diff --git a/results/logs/train_x_headshared_seed123.log b/results/logs/train_x_headshared_seed123.log new file mode 100644 index 0000000000000000000000000000000000000000..a123952cae6e2ccb63142a6160c0bcdb9bc4de08 --- /dev/null +++ b/results/logs/train_x_headshared_seed123.log @@ -0,0 +1,126 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.64M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 124,010,496 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9091, val loss 10.9097 +iter 0: loss 10.9225, time 18555.82ms, mfu -100.00% +step 250: train loss 6.9509, val loss 6.9769 +saving checkpoint to out-openwebtext_rerun_shared +iter 250: loss 7.0434, time 19910.07ms, mfu 0.05% +step 500: train loss 6.5218, val loss 6.5130 +saving checkpoint to out-openwebtext_rerun_shared +iter 500: loss 6.2355, time 19926.91ms, mfu 0.05% +step 750: train loss 6.3291, val loss 6.3567 +saving checkpoint to out-openwebtext_rerun_shared +iter 750: loss 5.8425, time 19862.82ms, mfu 0.05% +step 1000: train loss 6.1730, val loss 6.1793 +saving checkpoint to out-openwebtext_rerun_shared +iter 1000: loss 6.1374, time 19892.18ms, mfu 0.05% +step 1250: train loss 6.0575, val loss 6.0566 +saving checkpoint to out-openwebtext_rerun_shared +iter 1250: loss 6.1772, time 19871.34ms, mfu 0.05% +step 1500: train loss 5.9688, val loss 5.9663 +saving checkpoint to out-openwebtext_rerun_shared +iter 1500: loss 6.2928, time 19885.17ms, mfu 0.05% +step 1750: train loss 5.8836, val loss 5.8921 +saving checkpoint to out-openwebtext_rerun_shared +iter 1750: loss 5.6337, time 19862.16ms, mfu 0.05% +step 2000: train loss 5.7926, val loss 5.7870 +saving checkpoint to out-openwebtext_rerun_shared +iter 2000: loss 5.8497, time 19937.52ms, mfu 0.05% +step 2250: train loss 5.6862, val loss 5.6980 +saving checkpoint to out-openwebtext_rerun_shared +iter 2250: loss 5.8601, time 20193.99ms, mfu 0.05% +step 2500: train loss 5.6427, val loss 5.6243 +saving checkpoint to out-openwebtext_rerun_shared +iter 2500: loss 5.5160, time 19853.37ms, mfu 0.05% +step 2750: train loss 5.4920, val loss 5.5270 +saving checkpoint to out-openwebtext_rerun_shared +iter 2750: loss 5.1896, time 19868.91ms, mfu 0.05% +step 3000: train loss 5.4307, val loss 5.4181 +saving checkpoint to out-openwebtext_rerun_shared +iter 3000: loss 5.5906, time 19748.70ms, mfu 0.05% +step 3250: train loss 5.3533, val loss 5.3793 +saving checkpoint to out-openwebtext_rerun_shared +iter 3250: loss 5.3563, time 19873.90ms, mfu 0.05% +step 3500: train loss 5.3421, val loss 5.3323 +saving checkpoint to out-openwebtext_rerun_shared +iter 3500: loss 4.9575, time 19829.25ms, mfu 0.05% +step 3750: train loss 5.2816, val loss 5.2898 +saving checkpoint to out-openwebtext_rerun_shared +iter 3750: loss 5.5662, time 19850.87ms, mfu 0.05% +step 4000: train loss 5.1933, val loss 5.2153 +saving checkpoint to out-openwebtext_rerun_shared +iter 4000: loss 5.0788, time 19873.95ms, mfu 0.05% +step 4250: train loss 5.1517, val loss 5.1472 +saving checkpoint to out-openwebtext_rerun_shared +iter 4250: loss 5.1802, time 19799.85ms, mfu 0.05% +step 4500: train loss 5.0782, val loss 5.0826 +saving checkpoint to out-openwebtext_rerun_shared +iter 4500: loss 4.5145, time 20087.26ms, mfu 0.05% +step 4750: train loss 5.0454, val loss 5.0600 +saving checkpoint to out-openwebtext_rerun_shared +iter 4750: loss 4.8549, time 19941.46ms, mfu 0.05% +step 5000: train loss 5.0201, val loss 5.0041 +saving checkpoint to out-openwebtext_rerun_shared +iter 5000: loss 5.3351, time 19833.24ms, mfu 0.05% +step 5250: train loss 4.9594, val loss 4.9545 +saving checkpoint to out-openwebtext_rerun_shared +iter 5250: loss 4.9022, time 19818.52ms, mfu 0.05% +step 5500: train loss 4.9164, val loss 4.9279 +saving checkpoint to out-openwebtext_rerun_shared +iter 5500: loss 4.7828, time 19793.33ms, mfu 0.05% +step 5750: train loss 4.8561, val loss 4.8595 +saving checkpoint to out-openwebtext_rerun_shared +iter 5750: loss 5.6779, time 19840.76ms, mfu 0.05% +step 6000: train loss 4.8045, val loss 4.8156 +saving checkpoint to out-openwebtext_rerun_shared +iter 6000: loss 4.6865, time 19904.89ms, mfu 0.05% +step 6250: train loss 4.7694, val loss 4.7462 +saving checkpoint to out-openwebtext_rerun_shared +iter 6250: loss 5.0063, time 19943.38ms, mfu 0.05% +step 6500: train loss 4.7497, val loss 4.7520 +iter 6500: loss 4.6715, time 18380.00ms, mfu 0.05% +step 6750: train loss 4.6444, val loss 4.7219 +saving checkpoint to out-openwebtext_rerun_shared +iter 6750: loss 4.5487, time 21009.01ms, mfu 0.05% +step 7000: train loss 4.6407, val loss 4.6843 +saving checkpoint to out-openwebtext_rerun_shared +iter 7000: loss 5.0948, time 22166.25ms, mfu 0.05% +step 7250: train loss 4.6538, val loss 4.6577 +saving checkpoint to out-openwebtext_rerun_shared +iter 7250: loss 4.6525, time 20579.36ms, mfu 0.05% +step 7500: train loss 4.5842, val loss 4.5959 +saving checkpoint to out-openwebtext_rerun_shared +iter 7500: loss 5.0131, time 19803.40ms, mfu 0.05% +step 7750: train loss 4.5739, val loss 4.5874 +saving checkpoint to out-openwebtext_rerun_shared +iter 7750: loss 4.7534, time 19826.64ms, mfu 0.05% +step 8000: train loss 4.5363, val loss 4.5499 +saving checkpoint to out-openwebtext_rerun_shared +iter 8000: loss 4.3646, time 19813.23ms, mfu 0.05% +step 8250: train loss 4.5441, val loss 4.5359 +saving checkpoint to out-openwebtext_rerun_shared +iter 8250: loss 4.5213, time 19830.48ms, mfu 0.05% +step 8500: train loss 4.4933, val loss 4.4898 +saving checkpoint to out-openwebtext_rerun_shared +iter 8500: loss 4.5042, time 19837.09ms, mfu 0.05% +step 8750: train loss 4.4904, val loss 4.5055 +iter 8750: loss 4.2771, time 18337.57ms, mfu 0.05% +step 9000: train loss 4.4868, val loss 4.4916 +iter 9000: loss 4.5701, time 18329.49ms, mfu 0.05% +step 9250: train loss 4.4956, val loss 4.4543 +saving checkpoint to out-openwebtext_rerun_shared +iter 9250: loss 4.6956, time 19815.09ms, mfu 0.05% +step 9500: train loss 4.4703, val loss 4.4482 +saving checkpoint to out-openwebtext_rerun_shared +iter 9500: loss 4.5620, time 19919.09ms, mfu 0.05% +step 9750: train loss 4.4460, val loss 4.4679 +iter 9750: loss 4.1919, time 18374.70ms, mfu 0.05% +step 10000: train loss 4.4387, val loss 4.4367 +saving checkpoint to out-openwebtext_rerun_shared +iter 10000: loss 4.1024, time 19858.77ms, mfu 0.05% diff --git a/results/logs/train_x_headshared_seed1337.log b/results/logs/train_x_headshared_seed1337.log new file mode 100644 index 0000000000000000000000000000000000000000..334b90db7f872b98929579e414e6498f675fac52 --- /dev/null +++ b/results/logs/train_x_headshared_seed1337.log @@ -0,0 +1,124 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.64M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 124,010,496 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9310, val loss 10.9306 +iter 0: loss 10.9358, time 18576.77ms, mfu -100.00% +step 250: train loss 6.9042, val loss 6.9312 +saving checkpoint to out-openwebtext_rerun_shared +iter 250: loss 6.7944, time 19870.73ms, mfu 0.05% +step 500: train loss 6.4894, val loss 6.4932 +saving checkpoint to out-openwebtext_rerun_shared +iter 500: loss 6.6432, time 19802.99ms, mfu 0.05% +step 750: train loss 6.3007, val loss 6.3279 +saving checkpoint to out-openwebtext_rerun_shared +iter 750: loss 6.0934, time 19875.67ms, mfu 0.05% +step 1000: train loss 6.1596, val loss 6.1465 +saving checkpoint to out-openwebtext_rerun_shared +iter 1000: loss 6.0731, time 19921.11ms, mfu 0.05% +step 1250: train loss 6.0660, val loss 6.0354 +saving checkpoint to out-openwebtext_rerun_shared +iter 1250: loss 5.6900, time 19883.14ms, mfu 0.05% +step 1500: train loss 5.9485, val loss 5.9744 +saving checkpoint to out-openwebtext_rerun_shared +iter 1500: loss 6.2860, time 19652.32ms, mfu 0.05% +step 1750: train loss 5.8465, val loss 5.8606 +saving checkpoint to out-openwebtext_rerun_shared +iter 1750: loss 5.9694, time 19963.84ms, mfu 0.05% +step 2000: train loss 5.8157, val loss 5.7944 +saving checkpoint to out-openwebtext_rerun_shared +iter 2000: loss 5.8263, time 19983.62ms, mfu 0.05% +step 2250: train loss 5.6981, val loss 5.6443 +saving checkpoint to out-openwebtext_rerun_shared +iter 2250: loss 6.0843, time 19959.93ms, mfu 0.05% +step 2500: train loss 5.6294, val loss 5.5959 +saving checkpoint to out-openwebtext_rerun_shared +iter 2500: loss 5.1890, time 19902.76ms, mfu 0.05% +step 2750: train loss 5.4973, val loss 5.5124 +saving checkpoint to out-openwebtext_rerun_shared +iter 2750: loss 5.8531, time 20019.16ms, mfu 0.05% +step 3000: train loss 5.4136, val loss 5.4306 +saving checkpoint to out-openwebtext_rerun_shared +iter 3000: loss 5.1328, time 19885.08ms, mfu 0.05% +step 3250: train loss 5.3882, val loss 5.3949 +saving checkpoint to out-openwebtext_rerun_shared +iter 3250: loss 5.0801, time 19943.02ms, mfu 0.05% +step 3500: train loss 5.3263, val loss 5.3078 +saving checkpoint to out-openwebtext_rerun_shared +iter 3500: loss 5.4370, time 19941.91ms, mfu 0.05% +step 3750: train loss 5.2633, val loss 5.2769 +saving checkpoint to out-openwebtext_rerun_shared +iter 3750: loss 5.3708, time 19860.30ms, mfu 0.05% +step 4000: train loss 5.1917, val loss 5.2173 +saving checkpoint to out-openwebtext_rerun_shared +iter 4000: loss 5.0674, time 19910.44ms, mfu 0.05% +step 4250: train loss 5.1803, val loss 5.1808 +saving checkpoint to out-openwebtext_rerun_shared +iter 4250: loss 4.9804, time 20044.11ms, mfu 0.05% +step 4500: train loss 5.1002, val loss 5.1336 +saving checkpoint to out-openwebtext_rerun_shared +iter 4500: loss 5.6366, time 20555.90ms, mfu 0.05% +step 4750: train loss 5.0319, val loss 5.0696 +saving checkpoint to out-openwebtext_rerun_shared +iter 4750: loss 5.4312, time 20774.37ms, mfu 0.05% +step 5000: train loss 5.0069, val loss 5.0190 +saving checkpoint to out-openwebtext_rerun_shared +iter 5000: loss 4.9321, time 19711.72ms, mfu 0.05% +step 5250: train loss 4.9627, val loss 4.9532 +saving checkpoint to out-openwebtext_rerun_shared +iter 5250: loss 4.6505, time 19652.65ms, mfu 0.05% +step 5500: train loss 4.9114, val loss 4.9115 +saving checkpoint to out-openwebtext_rerun_shared +iter 5500: loss 4.8360, time 19878.56ms, mfu 0.05% +step 5750: train loss 4.8803, val loss 4.8925 +saving checkpoint to out-openwebtext_rerun_shared +iter 5750: loss 4.7528, time 19889.64ms, mfu 0.05% +step 6000: train loss 4.8027, val loss 4.8199 +saving checkpoint to out-openwebtext_rerun_shared +iter 6000: loss 4.9540, time 19761.38ms, mfu 0.05% +step 6250: train loss 4.7850, val loss 4.7989 +saving checkpoint to out-openwebtext_rerun_shared +iter 6250: loss 4.6306, time 19862.99ms, mfu 0.05% +step 6500: train loss 4.7253, val loss 4.7613 +saving checkpoint to out-openwebtext_rerun_shared +iter 6500: loss 4.6999, time 19930.18ms, mfu 0.05% +step 6750: train loss 4.6828, val loss 4.7092 +saving checkpoint to out-openwebtext_rerun_shared +iter 6750: loss 4.8369, time 19863.93ms, mfu 0.05% +step 7000: train loss 4.6674, val loss 4.6173 +saving checkpoint to out-openwebtext_rerun_shared +iter 7000: loss 4.1729, time 19807.55ms, mfu 0.05% +step 7250: train loss 4.5927, val loss 4.6594 +iter 7250: loss 4.3893, time 18431.31ms, mfu 0.05% +step 7500: train loss 4.5908, val loss 4.6060 +saving checkpoint to out-openwebtext_rerun_shared +iter 7500: loss 4.9469, time 19825.58ms, mfu 0.05% +step 7750: train loss 4.5817, val loss 4.5554 +saving checkpoint to out-openwebtext_rerun_shared +iter 7750: loss 4.9583, time 19683.09ms, mfu 0.05% +step 8000: train loss 4.5782, val loss 4.5606 +iter 8000: loss 4.4582, time 18164.08ms, mfu 0.05% +step 8250: train loss 4.5344, val loss 4.5147 +saving checkpoint to out-openwebtext_rerun_shared +iter 8250: loss 4.6526, time 19868.51ms, mfu 0.05% +step 8500: train loss 4.4960, val loss 4.4887 +saving checkpoint to out-openwebtext_rerun_shared +iter 8500: loss 4.2315, time 19913.07ms, mfu 0.05% +step 8750: train loss 4.5196, val loss 4.5344 +iter 8750: loss 4.4427, time 18414.06ms, mfu 0.05% +step 9000: train loss 4.4762, val loss 4.5232 +iter 9000: loss 4.7582, time 18149.48ms, mfu 0.05% +step 9250: train loss 4.4730, val loss 4.4475 +saving checkpoint to out-openwebtext_rerun_shared +iter 9250: loss 4.4652, time 19859.93ms, mfu 0.05% +step 9500: train loss 4.4701, val loss 4.4623 +iter 9500: loss 4.5340, time 18416.39ms, mfu 0.05% +step 9750: train loss 4.4377, val loss 4.4605 +iter 9750: loss 4.5111, time 18451.27ms, mfu 0.05% +step 10000: train loss 4.4256, val loss 4.4468 +saving checkpoint to out-openwebtext_rerun_shared +iter 10000: loss 4.6838, time 20161.55ms, mfu 0.05% diff --git a/results/logs/train_x_headshared_seed42.log b/results/logs/train_x_headshared_seed42.log new file mode 100644 index 0000000000000000000000000000000000000000..81582e9f17927b7f6f694dc8f926a6047ef48a48 --- /dev/null +++ b/results/logs/train_x_headshared_seed42.log @@ -0,0 +1,126 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.64M +C:\Users\brian\documents\nanogpt\train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 124,010,496 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9641, val loss 10.9611 +iter 0: loss 10.9442, time 18495.27ms, mfu -100.00% +step 250: train loss 6.9237, val loss 6.9409 +saving checkpoint to out-openwebtext_rerun_shared +iter 250: loss 6.8589, time 19934.44ms, mfu 0.05% +step 500: train loss 6.4777, val loss 6.5230 +saving checkpoint to out-openwebtext_rerun_shared +iter 500: loss 6.2176, time 19883.74ms, mfu 0.05% +step 750: train loss 6.3202, val loss 6.3306 +saving checkpoint to out-openwebtext_rerun_shared +iter 750: loss 6.2960, time 19902.99ms, mfu 0.05% +step 1000: train loss 6.1727, val loss 6.1959 +saving checkpoint to out-openwebtext_rerun_shared +iter 1000: loss 6.6711, time 19912.21ms, mfu 0.05% +step 1250: train loss 6.0502, val loss 6.0823 +saving checkpoint to out-openwebtext_rerun_shared +iter 1250: loss 5.4967, time 20353.14ms, mfu 0.05% +step 1500: train loss 5.9288, val loss 5.9755 +saving checkpoint to out-openwebtext_rerun_shared +iter 1500: loss 6.2423, time 20462.83ms, mfu 0.05% +step 1750: train loss 5.9001, val loss 5.9072 +saving checkpoint to out-openwebtext_rerun_shared +iter 1750: loss 5.2559, time 20107.79ms, mfu 0.05% +step 2000: train loss 5.7827, val loss 5.7875 +saving checkpoint to out-openwebtext_rerun_shared +iter 2000: loss 5.9367, time 19881.26ms, mfu 0.05% +step 2250: train loss 5.6644, val loss 5.6749 +saving checkpoint to out-openwebtext_rerun_shared +iter 2250: loss 5.5695, time 19842.86ms, mfu 0.05% +step 2500: train loss 5.5532, val loss 5.5670 +saving checkpoint to out-openwebtext_rerun_shared +iter 2500: loss 5.8322, time 19869.30ms, mfu 0.05% +step 2750: train loss 5.5186, val loss 5.5020 +saving checkpoint to out-openwebtext_rerun_shared +iter 2750: loss 5.0819, time 19895.14ms, mfu 0.05% +step 3000: train loss 5.4030, val loss 5.4490 +saving checkpoint to out-openwebtext_rerun_shared +iter 3000: loss 5.5632, time 19904.32ms, mfu 0.05% +step 3250: train loss 5.3927, val loss 5.4123 +saving checkpoint to out-openwebtext_rerun_shared +iter 3250: loss 5.1623, time 19876.60ms, mfu 0.05% +step 3500: train loss 5.3078, val loss 5.3068 +saving checkpoint to out-openwebtext_rerun_shared +iter 3500: loss 5.4916, time 19833.63ms, mfu 0.05% +step 3750: train loss 5.2631, val loss 5.2906 +saving checkpoint to out-openwebtext_rerun_shared +iter 3750: loss 5.0661, time 19848.73ms, mfu 0.05% +step 4000: train loss 5.1906, val loss 5.1600 +saving checkpoint to out-openwebtext_rerun_shared +iter 4000: loss 5.1939, time 19866.61ms, mfu 0.05% +step 4250: train loss 5.1404, val loss 5.1263 +saving checkpoint to out-openwebtext_rerun_shared +iter 4250: loss 5.3693, time 20274.61ms, mfu 0.05% +step 4500: train loss 5.0661, val loss 5.0742 +saving checkpoint to out-openwebtext_rerun_shared +iter 4500: loss 5.0780, time 19849.42ms, mfu 0.05% +step 4750: train loss 5.0389, val loss 5.0468 +saving checkpoint to out-openwebtext_rerun_shared +iter 4750: loss 5.3133, time 20030.60ms, mfu 0.05% +step 5000: train loss 5.0141, val loss 4.9942 +saving checkpoint to out-openwebtext_rerun_shared +iter 5000: loss 5.2235, time 19803.22ms, mfu 0.05% +step 5250: train loss 4.9319, val loss 4.9673 +saving checkpoint to out-openwebtext_rerun_shared +iter 5250: loss 5.0381, time 19834.39ms, mfu 0.05% +step 5500: train loss 4.8865, val loss 4.8746 +saving checkpoint to out-openwebtext_rerun_shared +iter 5500: loss 4.7281, time 19848.01ms, mfu 0.05% +step 5750: train loss 4.8231, val loss 4.8450 +saving checkpoint to out-openwebtext_rerun_shared +iter 5750: loss 4.8421, time 19793.79ms, mfu 0.05% +step 6000: train loss 4.8229, val loss 4.8227 +saving checkpoint to out-openwebtext_rerun_shared +iter 6000: loss 4.4900, time 19799.87ms, mfu 0.05% +step 6250: train loss 4.7699, val loss 4.7595 +saving checkpoint to out-openwebtext_rerun_shared +iter 6250: loss 4.7564, time 19758.58ms, mfu 0.05% +step 6500: train loss 4.7190, val loss 4.7399 +saving checkpoint to out-openwebtext_rerun_shared +iter 6500: loss 4.8135, time 19867.88ms, mfu 0.05% +step 6750: train loss 4.6664, val loss 4.7110 +saving checkpoint to out-openwebtext_rerun_shared +iter 6750: loss 4.9939, time 19912.84ms, mfu 0.05% +step 7000: train loss 4.6534, val loss 4.6714 +saving checkpoint to out-openwebtext_rerun_shared +iter 7000: loss 4.3706, time 19815.23ms, mfu 0.05% +step 7250: train loss 4.5822, val loss 4.6491 +saving checkpoint to out-openwebtext_rerun_shared +iter 7250: loss 4.1715, time 19862.83ms, mfu 0.05% +step 7500: train loss 4.5747, val loss 4.5730 +saving checkpoint to out-openwebtext_rerun_shared +iter 7500: loss 4.4988, time 19853.09ms, mfu 0.05% +step 7750: train loss 4.5482, val loss 4.5878 +iter 7750: loss 4.8376, time 18377.82ms, mfu 0.05% +step 8000: train loss 4.5411, val loss 4.5516 +saving checkpoint to out-openwebtext_rerun_shared +iter 8000: loss 4.2756, time 19808.60ms, mfu 0.05% +step 8250: train loss 4.4973, val loss 4.5216 +saving checkpoint to out-openwebtext_rerun_shared +iter 8250: loss 4.7416, time 19900.62ms, mfu 0.05% +step 8500: train loss 4.5016, val loss 4.5138 +saving checkpoint to out-openwebtext_rerun_shared +iter 8500: loss 4.5660, time 20017.05ms, mfu 0.05% +step 8750: train loss 4.4868, val loss 4.5198 +iter 8750: loss 4.5555, time 18422.92ms, mfu 0.05% +step 9000: train loss 4.4641, val loss 4.4735 +saving checkpoint to out-openwebtext_rerun_shared +iter 9000: loss 4.4388, time 20050.18ms, mfu 0.05% +step 9250: train loss 4.4498, val loss 4.4738 +iter 9250: loss 4.5534, time 18439.13ms, mfu 0.05% +step 9500: train loss 4.4749, val loss 4.4447 +saving checkpoint to out-openwebtext_rerun_shared +iter 9500: loss 4.9183, time 19857.28ms, mfu 0.05% +step 9750: train loss 4.4216, val loss 4.4614 +iter 9750: loss 4.2104, time 18412.46ms, mfu 0.05% +step 10000: train loss 4.4299, val loss 4.4204 +saving checkpoint to out-openwebtext_rerun_shared +iter 10000: loss 4.5177, time 19926.80ms, mfu 0.05% diff --git a/results/logs/train_x_headspec_elem_w2000_seed123.log b/results/logs/train_x_headspec_elem_w2000_seed123.log new file mode 100644 index 0000000000000000000000000000000000000000..1ef016fa48e9afb44bbb5c9a49d2457bbdaec432 --- /dev/null +++ b/results/logs/train_x_headspec_elem_w2000_seed123.log @@ -0,0 +1,126 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 124.18M +C:\Users\brian\documents\nanogpt\train.py:200: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 124,551,168 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9635, val loss 10.9645 +iter 0: loss 10.8981, time 18139.56ms, mfu -100.00% +step 250: train loss 6.8985, val loss 6.9504 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 250: loss 6.6903, time 19555.87ms, mfu 0.05% +step 500: train loss 6.5425, val loss 6.5243 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 500: loss 6.4882, time 19415.71ms, mfu 0.05% +step 750: train loss 6.3191, val loss 6.3219 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 750: loss 6.3474, time 19576.77ms, mfu 0.05% +step 1000: train loss 6.1572, val loss 6.1778 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 1000: loss 6.1503, time 19665.62ms, mfu 0.05% +step 1250: train loss 6.0984, val loss 6.0939 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 1250: loss 6.3041, time 19441.96ms, mfu 0.05% +step 1500: train loss 5.9525, val loss 5.9117 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 1500: loss 6.0366, time 19546.70ms, mfu 0.05% +step 1750: train loss 5.8818, val loss 5.8898 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 1750: loss 5.6439, time 19889.41ms, mfu 0.05% +step 2000: train loss 5.7342, val loss 5.7798 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 2000: loss 5.6726, time 20198.22ms, mfu 0.05% +step 2250: train loss 5.6946, val loss 5.6899 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 2250: loss 5.2766, time 19539.20ms, mfu 0.05% +step 2500: train loss 5.6078, val loss 5.5754 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 2500: loss 5.2971, time 20029.09ms, mfu 0.05% +step 2750: train loss 5.4923, val loss 5.5017 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 2750: loss 5.5842, time 20004.32ms, mfu 0.05% +step 3000: train loss 5.4020, val loss 5.3945 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 3000: loss 5.3148, time 19872.24ms, mfu 0.05% +step 3250: train loss 5.3649, val loss 5.3651 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 3250: loss 6.0551, time 20154.18ms, mfu 0.05% +step 3500: train loss 5.2968, val loss 5.2868 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 3500: loss 5.8340, time 20092.87ms, mfu 0.05% +step 3750: train loss 5.2306, val loss 5.2242 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 3750: loss 5.4074, time 20003.54ms, mfu 0.05% +step 4000: train loss 5.1934, val loss 5.1896 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 4000: loss 5.2474, time 20147.93ms, mfu 0.05% +step 4250: train loss 5.1047, val loss 5.1183 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 4250: loss 5.1797, time 19775.04ms, mfu 0.05% +step 4500: train loss 5.0811, val loss 5.0760 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 4500: loss 5.1935, time 19818.64ms, mfu 0.05% +step 4750: train loss 4.9987, val loss 5.0277 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 4750: loss 5.3691, time 20941.11ms, mfu 0.05% +step 5000: train loss 4.9728, val loss 4.9969 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 5000: loss 5.6330, time 19977.42ms, mfu 0.05% +step 5250: train loss 4.9156, val loss 4.9438 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 5250: loss 5.0572, time 20255.80ms, mfu 0.05% +step 5500: train loss 4.9055, val loss 4.9067 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 5500: loss 4.8275, time 20234.63ms, mfu 0.05% +step 5750: train loss 4.8429, val loss 4.8665 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 5750: loss 4.7157, time 20526.36ms, mfu 0.05% +step 6000: train loss 4.7687, val loss 4.8063 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 6000: loss 4.6596, time 20134.03ms, mfu 0.05% +step 6250: train loss 4.7471, val loss 4.7556 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 6250: loss 4.9597, time 20717.38ms, mfu 0.05% +step 6500: train loss 4.7506, val loss 4.7178 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 6500: loss 4.3025, time 20152.57ms, mfu 0.05% +step 6750: train loss 4.6741, val loss 4.6665 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 6750: loss 5.0157, time 20651.38ms, mfu 0.05% +step 7000: train loss 4.6452, val loss 4.6410 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 7000: loss 4.4338, time 20233.54ms, mfu 0.05% +step 7250: train loss 4.6122, val loss 4.6296 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 7250: loss 4.3433, time 20218.97ms, mfu 0.05% +step 7500: train loss 4.6133, val loss 4.6067 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 7500: loss 4.7148, time 20109.54ms, mfu 0.05% +step 7750: train loss 4.5442, val loss 4.5782 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 7750: loss 4.3742, time 20098.83ms, mfu 0.05% +step 8000: train loss 4.5330, val loss 4.5424 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 8000: loss 4.1625, time 20089.41ms, mfu 0.05% +step 8250: train loss 4.5288, val loss 4.5490 +iter 8250: loss 3.6592, time 18428.84ms, mfu 0.05% +step 8500: train loss 4.4872, val loss 4.5011 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 8500: loss 4.8309, time 20336.74ms, mfu 0.05% +step 8750: train loss 4.4424, val loss 4.4981 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 8750: loss 4.5448, time 20356.71ms, mfu 0.05% +step 9000: train loss 4.4771, val loss 4.4591 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 9000: loss 4.4653, time 20235.27ms, mfu 0.05% +step 9250: train loss 4.4492, val loss 4.4655 +iter 9250: loss 4.4904, time 18449.18ms, mfu 0.05% +step 9500: train loss 4.4569, val loss 4.4739 +iter 9500: loss 4.3747, time 18426.77ms, mfu 0.05% +step 9750: train loss 4.4346, val loss 4.4247 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 9750: loss 4.5284, time 20732.69ms, mfu 0.05% +step 10000: train loss 4.4523, val loss 4.4393 +iter 10000: loss 4.4684, time 18451.20ms, mfu 0.05% diff --git a/results/logs/train_x_headspec_elem_w2000_seed1337.log b/results/logs/train_x_headspec_elem_w2000_seed1337.log new file mode 100644 index 0000000000000000000000000000000000000000..503d0ca04ecd38fc631fc96a10f7f93642943c46 --- /dev/null +++ b/results/logs/train_x_headspec_elem_w2000_seed1337.log @@ -0,0 +1,125 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 124.18M +C:\Users\brian\documents\nanogpt\train.py:200: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 124,551,168 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9651, val loss 10.9664 +iter 0: loss 10.9776, time 18155.23ms, mfu -100.00% +step 250: train loss 6.9305, val loss 6.9655 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 250: loss 6.7807, time 19544.61ms, mfu 0.05% +step 500: train loss 6.4932, val loss 6.5116 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 500: loss 6.3697, time 19371.94ms, mfu 0.05% +step 750: train loss 6.3030, val loss 6.3442 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 750: loss 7.1483, time 19609.88ms, mfu 0.05% +step 1000: train loss 6.1317, val loss 6.1881 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 1000: loss 6.1860, time 19874.20ms, mfu 0.05% +step 1250: train loss 6.0614, val loss 6.0253 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 1250: loss 6.2967, time 19285.34ms, mfu 0.05% +step 1500: train loss 5.9534, val loss 6.0032 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 1500: loss 6.2054, time 19369.31ms, mfu 0.05% +step 1750: train loss 5.8545, val loss 5.8582 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 1750: loss 5.7722, time 19487.00ms, mfu 0.05% +step 2000: train loss 5.7933, val loss 5.8122 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 2000: loss 5.6635, time 19333.55ms, mfu 0.05% +step 2250: train loss 5.7214, val loss 5.7097 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 2250: loss 5.7975, time 20081.38ms, mfu 0.05% +step 2500: train loss 5.6137, val loss 5.5794 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 2500: loss 5.6280, time 19928.56ms, mfu 0.05% +step 2750: train loss 5.5282, val loss 5.5260 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 2750: loss 5.2268, time 19966.24ms, mfu 0.05% +step 3000: train loss 5.4613, val loss 5.4348 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 3000: loss 5.0773, time 20161.12ms, mfu 0.05% +step 3250: train loss 5.3621, val loss 5.3533 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 3250: loss 5.4111, time 20163.88ms, mfu 0.05% +step 3500: train loss 5.2892, val loss 5.3172 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 3500: loss 5.6999, time 20168.71ms, mfu 0.05% +step 3750: train loss 5.2366, val loss 5.2322 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 3750: loss 5.7741, time 20054.13ms, mfu 0.05% +step 4000: train loss 5.2129, val loss 5.1859 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 4000: loss 5.1721, time 20176.79ms, mfu 0.05% +step 4250: train loss 5.1525, val loss 5.1672 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 4250: loss 5.1009, time 20062.51ms, mfu 0.05% +step 4500: train loss 5.0767, val loss 5.1119 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 4500: loss 4.6173, time 19982.05ms, mfu 0.05% +step 4750: train loss 5.0735, val loss 5.0492 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 4750: loss 4.1417, time 19899.54ms, mfu 0.05% +step 5000: train loss 5.0004, val loss 5.0126 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 5000: loss 5.1606, time 20186.43ms, mfu 0.05% +step 5250: train loss 4.9656, val loss 4.9788 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 5250: loss 5.1236, time 20171.41ms, mfu 0.05% +step 5500: train loss 4.9181, val loss 4.9436 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 5500: loss 4.9206, time 20178.07ms, mfu 0.05% +step 5750: train loss 4.8731, val loss 4.8356 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 5750: loss 4.6730, time 20164.03ms, mfu 0.05% +step 6000: train loss 4.8315, val loss 4.8389 +iter 6000: loss 4.9211, time 18254.16ms, mfu 0.05% +step 6250: train loss 4.7560, val loss 4.7972 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 6250: loss 5.0586, time 20083.72ms, mfu 0.05% +step 6500: train loss 4.7147, val loss 4.7358 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 6500: loss 4.7781, time 20234.79ms, mfu 0.05% +step 6750: train loss 4.6845, val loss 4.6707 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 6750: loss 4.9570, time 19739.45ms, mfu 0.05% +step 7000: train loss 4.6554, val loss 4.6714 +iter 7000: loss 4.3185, time 18355.28ms, mfu 0.05% +step 7250: train loss 4.6537, val loss 4.6239 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 7250: loss 4.9054, time 19925.77ms, mfu 0.05% +step 7500: train loss 4.6237, val loss 4.6126 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 7500: loss 4.2564, time 19730.85ms, mfu 0.05% +step 7750: train loss 4.5891, val loss 4.5869 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 7750: loss 4.4109, time 19929.93ms, mfu 0.05% +step 8000: train loss 4.5360, val loss 4.5471 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 8000: loss 4.7314, time 19949.17ms, mfu 0.05% +step 8250: train loss 4.5356, val loss 4.5193 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 8250: loss 4.3509, time 19910.10ms, mfu 0.05% +step 8500: train loss 4.5175, val loss 4.5244 +iter 8500: loss 4.4901, time 18269.59ms, mfu 0.05% +step 8750: train loss 4.4787, val loss 4.4710 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 8750: loss 4.2362, time 20058.91ms, mfu 0.05% +step 9000: train loss 4.4600, val loss 4.4930 +iter 9000: loss 4.6775, time 18298.13ms, mfu 0.05% +step 9250: train loss 4.4798, val loss 4.4669 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 9250: loss 4.3429, time 19918.76ms, mfu 0.05% +step 9500: train loss 4.4623, val loss 4.4530 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 9500: loss 4.3910, time 19993.06ms, mfu 0.05% +step 9750: train loss 4.4524, val loss 4.4466 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 9750: loss 4.2157, time 20178.58ms, mfu 0.05% +step 10000: train loss 4.4479, val loss 4.4627 +iter 10000: loss 4.6868, time 18346.66ms, mfu 0.05% diff --git a/results/logs/train_x_headspec_elem_w2000_seed42.log b/results/logs/train_x_headspec_elem_w2000_seed42.log new file mode 100644 index 0000000000000000000000000000000000000000..9acd7749c0f62cac5ce2fc2a936455ade2d60e5b --- /dev/null +++ b/results/logs/train_x_headspec_elem_w2000_seed42.log @@ -0,0 +1,125 @@ +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 124.18M +C:\Users\brian\documents\nanogpt\train.py:200: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +num decayed parameter tensors: 62, with 124,551,168 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9565, val loss 10.9575 +iter 0: loss 10.9440, time 18001.62ms, mfu -100.00% +step 250: train loss 6.9284, val loss 6.9348 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 250: loss 7.0057, time 19208.81ms, mfu 0.05% +step 500: train loss 6.5206, val loss 6.5272 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 500: loss 6.7435, time 19599.55ms, mfu 0.05% +step 750: train loss 6.2842, val loss 6.3435 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 750: loss 6.3515, time 19755.37ms, mfu 0.05% +step 1000: train loss 6.1797, val loss 6.1982 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 1000: loss 6.0482, time 19334.95ms, mfu 0.05% +step 1250: train loss 6.0673, val loss 6.0533 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 1250: loss 6.6231, time 19555.40ms, mfu 0.05% +step 1500: train loss 5.9599, val loss 5.9924 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 1500: loss 6.3900, time 19432.25ms, mfu 0.05% +step 1750: train loss 5.8455, val loss 5.8679 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 1750: loss 5.8004, time 19658.64ms, mfu 0.05% +step 2000: train loss 5.7978, val loss 5.8005 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 2000: loss 5.1285, time 19646.19ms, mfu 0.05% +step 2250: train loss 5.7018, val loss 5.6794 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 2250: loss 5.6005, time 19494.49ms, mfu 0.05% +step 2500: train loss 5.5768, val loss 5.6020 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 2500: loss 5.9159, time 20247.73ms, mfu 0.05% +step 2750: train loss 5.5338, val loss 5.5242 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 2750: loss 5.2799, time 20142.66ms, mfu 0.05% +step 3000: train loss 5.4255, val loss 5.4291 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 3000: loss 6.4023, time 20222.94ms, mfu 0.05% +step 3250: train loss 5.3753, val loss 5.3506 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 3250: loss 5.1939, time 20189.01ms, mfu 0.05% +step 3500: train loss 5.3354, val loss 5.3086 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 3500: loss 5.3575, time 20144.61ms, mfu 0.05% +step 3750: train loss 5.2716, val loss 5.2445 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 3750: loss 5.0631, time 19992.00ms, mfu 0.05% +step 4000: train loss 5.1866, val loss 5.2149 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 4000: loss 4.7877, time 20210.48ms, mfu 0.05% +step 4250: train loss 5.1353, val loss 5.1448 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 4250: loss 4.9458, time 19850.06ms, mfu 0.05% +step 4500: train loss 5.0583, val loss 5.0823 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 4500: loss 5.3840, time 19821.69ms, mfu 0.05% +step 4750: train loss 5.0901, val loss 5.0557 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 4750: loss 5.4637, time 19881.05ms, mfu 0.05% +step 5000: train loss 4.9804, val loss 5.0300 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 5000: loss 5.1675, time 19952.92ms, mfu 0.05% +step 5250: train loss 4.9592, val loss 4.9600 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 5250: loss 4.7392, time 19882.90ms, mfu 0.05% +step 5500: train loss 4.9248, val loss 4.9059 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 5500: loss 5.2358, time 19983.31ms, mfu 0.05% +step 5750: train loss 4.8569, val loss 4.8771 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 5750: loss 4.7113, time 20074.71ms, mfu 0.05% +step 6000: train loss 4.8025, val loss 4.7929 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 6000: loss 4.7688, time 19970.26ms, mfu 0.05% +step 6250: train loss 4.7291, val loss 4.7500 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 6250: loss 5.0114, time 20080.63ms, mfu 0.05% +step 6500: train loss 4.6975, val loss 4.7275 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 6500: loss 4.7808, time 20155.00ms, mfu 0.05% +step 6750: train loss 4.7085, val loss 4.6896 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 6750: loss 4.6750, time 20252.02ms, mfu 0.05% +step 7000: train loss 4.6574, val loss 4.6414 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 7000: loss 4.7124, time 19903.50ms, mfu 0.05% +step 7250: train loss 4.6613, val loss 4.5747 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 7250: loss 4.7251, time 19853.30ms, mfu 0.05% +step 7500: train loss 4.6044, val loss 4.5897 +iter 7500: loss 4.5548, time 18292.53ms, mfu 0.05% +step 7750: train loss 4.5762, val loss 4.6042 +iter 7750: loss 4.8146, time 18299.80ms, mfu 0.05% +step 8000: train loss 4.5766, val loss 4.5327 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 8000: loss 4.6859, time 19943.83ms, mfu 0.05% +step 8250: train loss 4.5409, val loss 4.5506 +iter 8250: loss 4.2359, time 18335.02ms, mfu 0.05% +step 8500: train loss 4.4896, val loss 4.5104 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 8500: loss 4.3863, time 20198.98ms, mfu 0.05% +step 8750: train loss 4.4922, val loss 4.4803 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 8750: loss 4.3668, time 19723.54ms, mfu 0.05% +step 9000: train loss 4.5028, val loss 4.4897 +iter 9000: loss 4.4055, time 18258.64ms, mfu 0.05% +step 9250: train loss 4.4607, val loss 4.4681 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 9250: loss 4.8943, time 19937.92ms, mfu 0.05% +step 9500: train loss 4.4341, val loss 4.4865 +iter 9500: loss 4.5344, time 18340.67ms, mfu 0.05% +step 9750: train loss 4.4748, val loss 4.4681 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 9750: loss 4.6409, time 20163.71ms, mfu 0.05% +step 10000: train loss 4.4464, val loss 4.4638 +saving checkpoint to out-openwebtext_phase2_x_headspec_elem +iter 10000: loss 4.5720, time 20508.95ms, mfu 0.05% diff --git a/results/phase10_wslbridge_w2000_runs_20260329_1020.csv b/results/phase10_wslbridge_w2000_runs_20260329_1020.csv new file mode 100644 index 0000000000000000000000000000000000000000..c2179764ca4236a5c1d62f3100e2f5d3d1898ad0 --- /dev/null +++ b/results/phase10_wslbridge_w2000_runs_20260329_1020.csv @@ -0,0 +1,5 @@ +variant,short_name,warmup,seed,status,runtime_sec,params_m,best_val,best_step,final_val,stable,out_dir,error +none,baseline,2000,42,ok,3600.69,123.59,4.4766,9750,4.4912,False,out-openwebtext_phase10_wslbridge_w2000_baseline, +dynamic_qa_conditioned,cheap_qa,2000,42,ok,3862.21,123.69,4.3665,10000,4.3665,True,out-openwebtext_phase10_wslbridge_w2000_cheap_qa, +dynamic_xconditioned_fullwidth_headspecific,x_full_headspec,2000,42,ok,3895.17,130.67,4.4062,10000,4.4062,True,out-openwebtext_phase10_wslbridge_w2000_x_full_headspec, +dynamic_qa_conditioned_normed,qa_normed,2000,42,ok,4375.64,123.69,4.3724,10000,4.3724,True,out-openwebtext_phase10_wslbridge_w2000_qa_normed, diff --git a/results/phase10_wslbridge_w2000_summary_20260329_1020.txt b/results/phase10_wslbridge_w2000_summary_20260329_1020.txt new file mode 100644 index 0000000000000000000000000000000000000000..d248acf6a39abf0d2a4cdac8c5fc09eabd73d23b --- /dev/null +++ b/results/phase10_wslbridge_w2000_summary_20260329_1020.txt @@ -0,0 +1,61 @@ +PHASE 10 — WSL LINUX BRIDGE SUMMARY +==================================================================================================== + +WARNING: + DO NOT MIX WITH CANONICAL LEADERBOARD. DO NOT MIX WITH A10 FULL-BOARD RESULTS. + This is a bridge/diagnostic pack only. + +Bundle purpose: + Re-run key variants on the same 3060 laptop under Linux/WSL + torch 2.7.0/cu128 to probe + whether the A10 rerank shifts look more like a software-stack effect. + +Results CSV: /home/brian/nanogpt_wsl_bridge/results/phase10_wslbridge_w2000_runs_20260329_1020.csv + +Recipe: + batch_size=4 gradient_accumulation_steps=2 block_size=512 tokens/iter=4,096 + compile=False warmup=2000 max_iters=10000 seeds=[42] + +Reference 2000-warmup canon anchors (Windows / torch 2.5.1): + baseline 4.4708 + cheap_qa 4.3897 + x_full_headspec 4.3964 + qa_normed 4.3926 + +Bridge ranking (completed runs only): + 1. cheap_qa mean=4.3665 std=0.0000 done=1/1 vs canon=-0.0232 + 2. qa_normed mean=4.3724 std=0.0000 done=1/1 vs canon=-0.0202 + 3. x_full_headspec mean=4.4062 std=0.0000 done=1/1 vs canon=+0.0098 + 4. baseline mean=4.4766 std=0.0000 done=1/1 vs canon=+0.0058 + +---------------------------------------------------------------------------------------------------- +baseline (none) + No writeback gate + out_dir: out-openwebtext_phase10_wslbridge_w2000_baseline + completed seeds: 1/1 + partial mean/std: 4.4766 ± 0.0000 + vs own 2000-warmup canon: +0.0058 + seed= 42 best=4.4766 final=4.4912 best@9750 stable=False + +cheap_qa (dynamic_qa_conditioned) + Shared Q+A linear gate: concat [q,y] -> 128->64 + out_dir: out-openwebtext_phase10_wslbridge_w2000_cheap_qa + completed seeds: 1/1 + partial mean/std: 4.3665 ± 0.0000 + vs own 2000-warmup canon: -0.0232 + seed= 42 best=4.3665 final=4.3665 best@10000 stable=True + +x_full_headspec (dynamic_xconditioned_fullwidth_headspecific) + Head-specific full-width X gate: separate 768->64 per head + out_dir: out-openwebtext_phase10_wslbridge_w2000_x_full_headspec + completed seeds: 1/1 + partial mean/std: 4.4062 ± 0.0000 + vs own 2000-warmup canon: +0.0098 + seed= 42 best=4.4062 final=4.4062 best@10000 stable=True + +qa_normed (dynamic_qa_conditioned_normed) + RMSNorm q and y separately, then shared 128->64 gate + out_dir: out-openwebtext_phase10_wslbridge_w2000_qa_normed + completed seeds: 1/1 + partial mean/std: 4.3724 ± 0.0000 + vs own 2000-warmup canon: -0.0202 + seed= 42 best=4.3724 final=4.3724 best@10000 stable=True diff --git a/results/phase11_wslbridge_w2000_seed1337_runs_20260329_1623.csv b/results/phase11_wslbridge_w2000_seed1337_runs_20260329_1623.csv new file mode 100644 index 0000000000000000000000000000000000000000..10bc115322dfe05bc5cf374630610b2e44bb117a --- /dev/null +++ b/results/phase11_wslbridge_w2000_seed1337_runs_20260329_1623.csv @@ -0,0 +1,5 @@ +variant,short_name,warmup,seed,status,runtime_sec,params_m,best_val,best_step,final_val,stable,out_dir,error +none,baseline,2000,1337,ok,3520.01,123.59,4.4697,9750,4.5115,False,out-openwebtext_phase11_wslbridge_w2000_seed1337_baseline, +dynamic_qa_conditioned,cheap_qa,2000,1337,ok,3758.52,123.69,4.3934,9750,4.443,False,out-openwebtext_phase11_wslbridge_w2000_seed1337_cheap_qa, +dynamic_xconditioned_fullwidth_headspecific,x_full_headspec,2000,1337,ok,3864.29,130.67,4.4084,9750,4.4099,False,out-openwebtext_phase11_wslbridge_w2000_seed1337_x_full_headspec, +dynamic_qa_conditioned_normed,qa_normed,2000,1337,ok,4355.17,123.69,4.3729,9750,4.4203,False,out-openwebtext_phase11_wslbridge_w2000_seed1337_qa_normed, diff --git a/results/phase11_wslbridge_w2000_seed1337_summary_20260329_1623.txt b/results/phase11_wslbridge_w2000_seed1337_summary_20260329_1623.txt new file mode 100644 index 0000000000000000000000000000000000000000..307c1d5804649af25692b62cdb36a1bad2b3183b --- /dev/null +++ b/results/phase11_wslbridge_w2000_seed1337_summary_20260329_1623.txt @@ -0,0 +1,61 @@ +PHASE 11 — WSL LINUX BRIDGE SUMMARY +==================================================================================================== + +WARNING: + DO NOT MIX WITH CANONICAL LEADERBOARD. DO NOT MIX WITH A10 FULL-BOARD RESULTS. + This is a bridge/diagnostic pack only. + +Bundle purpose: + Re-run key variants on the same 3060 laptop under Linux/WSL + torch 2.7.0/cu128 to probe + whether the A10 rerank shifts look more like a software-stack effect. + +Results CSV: /home/brian/nanogpt_wsl_bridge/results/phase11_wslbridge_w2000_seed1337_runs_20260329_1623.csv + +Recipe: + batch_size=4 gradient_accumulation_steps=2 block_size=512 tokens/iter=4,096 + compile=False warmup=2000 max_iters=10000 seeds=[1337] + +Reference 2000-warmup canon anchors (Windows / torch 2.5.1): + baseline 4.4708 + cheap_qa 4.3897 + x_full_headspec 4.3964 + qa_normed 4.3926 + +Bridge ranking (completed runs only): + 1. qa_normed mean=4.3729 std=0.0000 done=1/1 vs canon=-0.0197 + 2. cheap_qa mean=4.3934 std=0.0000 done=1/1 vs canon=+0.0037 + 3. x_full_headspec mean=4.4084 std=0.0000 done=1/1 vs canon=+0.0120 + 4. baseline mean=4.4697 std=0.0000 done=1/1 vs canon=-0.0011 + +---------------------------------------------------------------------------------------------------- +baseline (none) + No writeback gate + out_dir: out-openwebtext_phase11_wslbridge_w2000_seed1337_baseline + completed seeds: 1/1 + partial mean/std: 4.4697 ± 0.0000 + vs own 2000-warmup canon: -0.0011 + seed=1337 best=4.4697 final=4.5115 best@9750 stable=False + +cheap_qa (dynamic_qa_conditioned) + Shared Q+A linear gate: concat [q,y] -> 128->64 + out_dir: out-openwebtext_phase11_wslbridge_w2000_seed1337_cheap_qa + completed seeds: 1/1 + partial mean/std: 4.3934 ± 0.0000 + vs own 2000-warmup canon: +0.0037 + seed=1337 best=4.3934 final=4.4430 best@9750 stable=False + +x_full_headspec (dynamic_xconditioned_fullwidth_headspecific) + Head-specific full-width X gate: separate 768->64 per head + out_dir: out-openwebtext_phase11_wslbridge_w2000_seed1337_x_full_headspec + completed seeds: 1/1 + partial mean/std: 4.4084 ± 0.0000 + vs own 2000-warmup canon: +0.0120 + seed=1337 best=4.4084 final=4.4099 best@9750 stable=False + +qa_normed (dynamic_qa_conditioned_normed) + RMSNorm q and y separately, then shared 128->64 gate + out_dir: out-openwebtext_phase11_wslbridge_w2000_seed1337_qa_normed + completed seeds: 1/1 + partial mean/std: 4.3729 ± 0.0000 + vs own 2000-warmup canon: -0.0197 + seed=1337 best=4.3729 final=4.4203 best@9750 stable=False diff --git a/results/phase12_wslbridge_w2000_seed123_runs_20260329_2119.csv b/results/phase12_wslbridge_w2000_seed123_runs_20260329_2119.csv new file mode 100644 index 0000000000000000000000000000000000000000..39a9aa485890ff722030e8e5de6147529a8b4712 --- /dev/null +++ b/results/phase12_wslbridge_w2000_seed123_runs_20260329_2119.csv @@ -0,0 +1,5 @@ +variant,short_name,warmup,seed,status,runtime_sec,params_m,best_val,best_step,final_val,stable,out_dir,error +none,baseline,2000,123,ok,3569.73,123.59,4.4803,9500,4.5009,False,out-openwebtext_phase12_wslbridge_w2000_seed123_baseline, +dynamic_qa_conditioned,cheap_qa,2000,123,ok,3788.76,123.69,4.4124,9750,4.4228,False,out-openwebtext_phase12_wslbridge_w2000_seed123_cheap_qa, +dynamic_xconditioned_fullwidth_headspecific,x_full_headspec,2000,123,ok,3857.08,130.67,4.3946,9750,4.4305,False,out-openwebtext_phase12_wslbridge_w2000_seed123_x_full_headspec, +dynamic_qa_conditioned_normed,qa_normed,2000,123,ok,4415.03,123.69,4.4147,9750,4.4256,False,out-openwebtext_phase12_wslbridge_w2000_seed123_qa_normed, diff --git a/results/phase12_wslbridge_w2000_seed123_runs_20260330_1914.csv b/results/phase12_wslbridge_w2000_seed123_runs_20260330_1914.csv new file mode 100644 index 0000000000000000000000000000000000000000..50beff951d967c08793c0374d2d8c81a076a1f20 --- /dev/null +++ b/results/phase12_wslbridge_w2000_seed123_runs_20260330_1914.csv @@ -0,0 +1,5 @@ +variant,short_name,warmup,seed,status,runtime_sec,params_m,best_val,best_step,final_val,stable,out_dir,error +none,baseline,2000,123,ok,3570.63,123.59,4.4814,9500,4.5017,False,out-openwebtext_phase12_wslbridge_w2000_seed123_baseline, +dynamic_qa_conditioned,cheap_qa,2000,123,ok,3724.87,123.69,4.4107,9750,4.4256,False,out-openwebtext_phase12_wslbridge_w2000_seed123_cheap_qa, +dynamic_xconditioned_fullwidth_headspecific,x_full_headspec,2000,123,ok,3801.81,130.67,4.3898,9750,4.4277,False,out-openwebtext_phase12_wslbridge_w2000_seed123_x_full_headspec, +dynamic_qa_conditioned_normed,qa_normed,2000,123,ok,4400.69,123.69,4.4237,9750,4.4334,False,out-openwebtext_phase12_wslbridge_w2000_seed123_qa_normed, diff --git a/results/phase12_wslbridge_w2000_seed123_summary_20260329_2119-first.txt b/results/phase12_wslbridge_w2000_seed123_summary_20260329_2119-first.txt new file mode 100644 index 0000000000000000000000000000000000000000..d4a05534480369c99b752ecc4942d7c690f88fe8 --- /dev/null +++ b/results/phase12_wslbridge_w2000_seed123_summary_20260329_2119-first.txt @@ -0,0 +1,61 @@ +PHASE 12 — WSL LINUX BRIDGE SUMMARY +==================================================================================================== + +WARNING: + DO NOT MIX WITH CANONICAL LEADERBOARD. DO NOT MIX WITH A10 FULL-BOARD RESULTS. + This is a bridge/diagnostic pack only. + +Bundle purpose: + Re-run key variants on the same 3060 laptop under Linux/WSL + torch 2.7.0/cu128 to probe + whether the A10 rerank shifts look more like a software-stack effect. + +Results CSV: /home/brian/nanogpt_wsl_bridge/results/phase12_wslbridge_w2000_seed123_runs_20260329_2119.csv + +Recipe: + batch_size=4 gradient_accumulation_steps=2 block_size=512 tokens/iter=4,096 + compile=False warmup=2000 max_iters=10000 seeds=[123] + +Reference 2000-warmup canon anchors (Windows / torch 2.5.1): + baseline 4.4708 + cheap_qa 4.3897 + x_full_headspec 4.3964 + qa_normed 4.3926 + +Bridge ranking (completed runs only): + 1. x_full_headspec mean=4.3946 std=0.0000 done=1/1 vs canon=-0.0018 + 2. cheap_qa mean=4.4124 std=0.0000 done=1/1 vs canon=+0.0227 + 3. qa_normed mean=4.4147 std=0.0000 done=1/1 vs canon=+0.0221 + 4. baseline mean=4.4803 std=0.0000 done=1/1 vs canon=+0.0095 + +---------------------------------------------------------------------------------------------------- +baseline (none) + No writeback gate + out_dir: out-openwebtext_phase12_wslbridge_w2000_seed123_baseline + completed seeds: 1/1 + partial mean/std: 4.4803 ± 0.0000 + vs own 2000-warmup canon: +0.0095 + seed= 123 best=4.4803 final=4.5009 best@9500 stable=False + +cheap_qa (dynamic_qa_conditioned) + Shared Q+A linear gate: concat [q,y] -> 128->64 + out_dir: out-openwebtext_phase12_wslbridge_w2000_seed123_cheap_qa + completed seeds: 1/1 + partial mean/std: 4.4124 ± 0.0000 + vs own 2000-warmup canon: +0.0227 + seed= 123 best=4.4124 final=4.4228 best@9750 stable=False + +x_full_headspec (dynamic_xconditioned_fullwidth_headspecific) + Head-specific full-width X gate: separate 768->64 per head + out_dir: out-openwebtext_phase12_wslbridge_w2000_seed123_x_full_headspec + completed seeds: 1/1 + partial mean/std: 4.3946 ± 0.0000 + vs own 2000-warmup canon: -0.0018 + seed= 123 best=4.3946 final=4.4305 best@9750 stable=False + +qa_normed (dynamic_qa_conditioned_normed) + RMSNorm q and y separately, then shared 128->64 gate + out_dir: out-openwebtext_phase12_wslbridge_w2000_seed123_qa_normed + completed seeds: 1/1 + partial mean/std: 4.4147 ± 0.0000 + vs own 2000-warmup canon: +0.0221 + seed= 123 best=4.4147 final=4.4256 best@9750 stable=False diff --git a/results/phase12_wslbridge_w2000_seed123_summary_20260330_1914-second.txt b/results/phase12_wslbridge_w2000_seed123_summary_20260330_1914-second.txt new file mode 100644 index 0000000000000000000000000000000000000000..be1691afcb5dfaf9aea9d9a088aa31f9e6efbedf --- /dev/null +++ b/results/phase12_wslbridge_w2000_seed123_summary_20260330_1914-second.txt @@ -0,0 +1,61 @@ +PHASE 12 — WSL LINUX BRIDGE SUMMARY +==================================================================================================== + +WARNING: + DO NOT MIX WITH CANONICAL LEADERBOARD. DO NOT MIX WITH A10 FULL-BOARD RESULTS. + This is a bridge/diagnostic pack only. + +Bundle purpose: + Re-run key variants on the same 3060 laptop under Linux/WSL + torch 2.7.0/cu128 to probe + whether the A10 rerank shifts look more like a software-stack effect. + +Results CSV: /home/brian/nanogpt_wsl_bridge/results/phase12_wslbridge_w2000_seed123_runs_20260330_1914.csv + +Recipe: + batch_size=4 gradient_accumulation_steps=2 block_size=512 tokens/iter=4,096 + compile=False warmup=2000 max_iters=10000 seeds=[123] + +Reference 2000-warmup canon anchors (Windows / torch 2.5.1): + baseline 4.4708 + cheap_qa 4.3897 + x_full_headspec 4.3964 + qa_normed 4.3926 + +Bridge ranking (completed runs only): + 1. x_full_headspec mean=4.3898 std=0.0000 done=1/1 vs canon=-0.0066 + 2. cheap_qa mean=4.4107 std=0.0000 done=1/1 vs canon=+0.0210 + 3. qa_normed mean=4.4237 std=0.0000 done=1/1 vs canon=+0.0311 + 4. baseline mean=4.4814 std=0.0000 done=1/1 vs canon=+0.0106 + +---------------------------------------------------------------------------------------------------- +baseline (none) + No writeback gate + out_dir: out-openwebtext_phase12_wslbridge_w2000_seed123_baseline + completed seeds: 1/1 + partial mean/std: 4.4814 ± 0.0000 + vs own 2000-warmup canon: +0.0106 + seed= 123 best=4.4814 final=4.5017 best@9500 stable=False + +cheap_qa (dynamic_qa_conditioned) + Shared Q+A linear gate: concat [q,y] -> 128->64 + out_dir: out-openwebtext_phase12_wslbridge_w2000_seed123_cheap_qa + completed seeds: 1/1 + partial mean/std: 4.4107 ± 0.0000 + vs own 2000-warmup canon: +0.0210 + seed= 123 best=4.4107 final=4.4256 best@9750 stable=False + +x_full_headspec (dynamic_xconditioned_fullwidth_headspecific) + Head-specific full-width X gate: separate 768->64 per head + out_dir: out-openwebtext_phase12_wslbridge_w2000_seed123_x_full_headspec + completed seeds: 1/1 + partial mean/std: 4.3898 ± 0.0000 + vs own 2000-warmup canon: -0.0066 + seed= 123 best=4.3898 final=4.4277 best@9750 stable=False + +qa_normed (dynamic_qa_conditioned_normed) + RMSNorm q and y separately, then shared 128->64 gate + out_dir: out-openwebtext_phase12_wslbridge_w2000_seed123_qa_normed + completed seeds: 1/1 + partial mean/std: 4.4237 ± 0.0000 + vs own 2000-warmup canon: +0.0311 + seed= 123 best=4.4237 final=4.4334 best@9750 stable=False diff --git a/results/phase13_qa_asymnorm_w2000_runs_20260330_2338 - windows w pytorch 2.70+cu128.csv b/results/phase13_qa_asymnorm_w2000_runs_20260330_2338 - windows w pytorch 2.70+cu128.csv new file mode 100644 index 0000000000000000000000000000000000000000..3dcb5cb397c69591ac507e56fc284188d48b02f3 --- /dev/null +++ b/results/phase13_qa_asymnorm_w2000_runs_20260330_2338 - windows w pytorch 2.70+cu128.csv @@ -0,0 +1,7 @@ +variant,short_name,warmup,seed,status,runtime_sec,params_m,best_val,best_step,final_val,stable,out_dir,error +dynamic_qa_conditioned_normed_yonly,qa_normed_yonly,2000,42,ok,4225.51,123.69,4.3871,10000,4.3871,True,out-openwebtext_phase13_w2000_qa_normed_yonly, +dynamic_qa_conditioned_normed_yonly,qa_normed_yonly,2000,1337,ok,4181.55,123.69,4.3881,9750,4.4346,False,out-openwebtext_phase13_w2000_qa_normed_yonly, +dynamic_qa_conditioned_normed_yonly,qa_normed_yonly,2000,123,ok,4189.59,123.69,4.411,9750,4.4218,False,out-openwebtext_phase13_w2000_qa_normed_yonly, +dynamic_qa_conditioned_normed_qonly,qa_normed_qonly,2000,42,ok,4177.96,123.69,4.3773,10000,4.3773,True,out-openwebtext_phase13_w2000_qa_normed_qonly, +dynamic_qa_conditioned_normed_qonly,qa_normed_qonly,2000,1337,ok,4177.6,123.69,4.3825,9750,4.4294,False,out-openwebtext_phase13_w2000_qa_normed_qonly, +dynamic_qa_conditioned_normed_qonly,qa_normed_qonly,2000,123,ok,4181.32,123.69,4.4021,9750,4.4125,False,out-openwebtext_phase13_w2000_qa_normed_qonly, diff --git a/results/phase13_qa_asymnorm_w2000_runs_20260330_2338 - windows w pytorch 2.70+cu128.txt b/results/phase13_qa_asymnorm_w2000_runs_20260330_2338 - windows w pytorch 2.70+cu128.txt new file mode 100644 index 0000000000000000000000000000000000000000..262ea6fe72ece62db62b64b508c6617992179a1f --- /dev/null +++ b/results/phase13_qa_asymnorm_w2000_runs_20260330_2338 - windows w pytorch 2.70+cu128.txt @@ -0,0 +1,44 @@ +PHASE 13 — QA ASYMMETRIC NORMALIZATION ABLATION +==================================================================================================== + +Purpose: + Isolate which signal's magnitude is responsible for qa_normed's late-training instability + by testing the two asymmetric Q+A normalization combinations. + +Scientific prediction: + qa_normed_yonly (raw q + normed y) -> predicted STABLE, performance near cheap_qa + qa_normed_qonly (normed q + raw y) -> predicted UNSTABLE, performance near qa_normed + +Results CSV: C:\Users\brian\Documents\nanoGPT\results\phase13_qa_asymnorm_w2000_runs_20260330_2338.csv + +Recipe: + batch_size=4 gradient_accumulation_steps=2 block_size=512 tokens/iter=4,096 + compile=False warmup=2000 max_iters=10000 seeds=[42, 1337, 123] + +Canon anchors to keep in mind: + cheap_qa 4.3897 (raw q + raw y, stable) + qa_normed 4.3926 (normed q + normed y, unstable) + a_only 4.4298 (raw y only) + +Ablation ranking (completed runs only): + 1. qa_normed_qonly mean=4.3873 std=0.0107 done=3/3 + 2. qa_normed_yonly mean=4.3954 std=0.0110 done=3/3 + +---------------------------------------------------------------------------------------------------- +qa_normed_yonly (dynamic_qa_conditioned_normed_yonly) + Raw q + RMSNorm(y), then shared 128->64 gate + out_dir: out-openwebtext_phase13_w2000_qa_normed_yonly + completed seeds: 3/3 + partial mean/std: 4.3954 ± 0.0110 + seed= 42 best=4.3871 final=4.3871 best@10000 stable=True + seed= 123 best=4.4110 final=4.4218 best@9750 stable=False + seed=1337 best=4.3881 final=4.4346 best@9750 stable=False + +qa_normed_qonly (dynamic_qa_conditioned_normed_qonly) + RMSNorm(q) + raw y, then shared 128->64 gate + out_dir: out-openwebtext_phase13_w2000_qa_normed_qonly + completed seeds: 3/3 + partial mean/std: 4.3873 ± 0.0107 + seed= 42 best=4.3773 final=4.3773 best@10000 stable=True + seed= 123 best=4.4021 final=4.4125 best@9750 stable=False + seed=1337 best=4.3825 final=4.4294 best@9750 stable=False diff --git a/results/phase14_qa_softq_w2000_runs_20260331_1838.csv b/results/phase14_qa_softq_w2000_runs_20260331_1838.csv new file mode 100644 index 0000000000000000000000000000000000000000..3fd5ba2a2bf256307e87a0107954c73572ee0434 --- /dev/null +++ b/results/phase14_qa_softq_w2000_runs_20260331_1838.csv @@ -0,0 +1,4 @@ +variant,short_name,warmup,seed,status,runtime_sec,params_m,best_val,best_step,final_val,stable,alpha_layer0,out_dir,error +dynamic_qa_conditioned_softq,qa_softq,2000,42,ok,4385.9086236953735,123.69,4.3764,10000,4.3764,True,0.7439010143280029,out-openwebtext_phase14_w2000_qa_softq, +dynamic_qa_conditioned_softq,qa_softq,2000,1337,ok,4368.7725481987,123.69,4.3894,9750,4.4344,False,0.7442627549171448,out-openwebtext_phase14_w2000_qa_softq, +dynamic_qa_conditioned_softq,qa_softq,2000,123,ok,4367.432096719742,123.69,4.3937,9750,4.4037,False,0.7390603423118591,out-openwebtext_phase14_w2000_qa_softq, diff --git a/results/phase14_qa_softq_w2000_summary_20260331_1838.txt b/results/phase14_qa_softq_w2000_summary_20260331_1838.txt new file mode 100644 index 0000000000000000000000000000000000000000..7324457f185cbb183e65d15cf0d873671b452df9 --- /dev/null +++ b/results/phase14_qa_softq_w2000_summary_20260331_1838.txt @@ -0,0 +1,39 @@ +PHASE 14 — QA SOFT-Q NORMALIZATION (LEARNED BLEND) +==================================================================================================== + +Scientific motivation: + Phase 13 suggested that q-only normalization may help performance, but with + higher variance and late-training instability. This phase tests whether a + learned blend between raw q and RMSNorm(q) can find a better stability/ + performance tradeoff automatically. + +Scientific predictions: + Performance: between qa_normed_qonly (4.3873) and cheap_qa (4.3897), possibly better than both + Stability: better than qa_normed_qonly because some q magnitude is preserved + Learned alpha: likely to settle somewhere in 0.3-0.7 range + +Results CSV: C:\Users\brian\Documents\nanoGPT\results\phase14_qa_softq_w2000_runs_20260331_1838.csv + +Recipe: + batch_size=4 gradient_accumulation_steps=2 block_size=512 tokens/iter=4,096 + compile=False warmup=2000 max_iters=10000 seeds=[42, 1337, 123] + +Canon anchors to keep in mind: + cheap_qa 4.3897 (raw q + raw y, stable, champion) + qa_normed_qonly 4.3873 (normed q + raw y, potentially better but unstable) + qa_normed 4.3926 (normed q + normed y, unstable) + qa_normed_yonly 4.3954 (raw q + normed y, unstable) + +Completed runs: + 1. qa_softq mean=4.3865 std=0.0074 done=3/3 + +---------------------------------------------------------------------------------------------------- +qa_softq (dynamic_qa_conditioned_softq) + Learned blend between RMSNorm(q) and raw q, with raw y, then shared 128->64 gate + out_dir: out-openwebtext_phase14_w2000_qa_softq + completed seeds: 3/3 + partial mean/std: 4.3865 ± 0.0074 + learned alpha (layer 0) mean across completed seeds: 0.7424 + seed= 42 best=4.3764 final=4.3764 best@10000 stable=True alpha_l0=0.7439 + seed= 123 best=4.3937 final=4.4037 best@9750 stable=False alpha_l0=0.7391 + seed=1337 best=4.3894 final=4.4344 best@9750 stable=False alpha_l0=0.7443 diff --git a/results/phase15_qa_asymnorm_w2000_runs_20260331_2223.csv b/results/phase15_qa_asymnorm_w2000_runs_20260331_2223.csv new file mode 100644 index 0000000000000000000000000000000000000000..c068189f7e58cc5ecd5f2f61029a6bb16785af5e --- /dev/null +++ b/results/phase15_qa_asymnorm_w2000_runs_20260331_2223.csv @@ -0,0 +1,7 @@ +variant,short_name,warmup,seed,status,runtime_sec,params_m,best_val,best_step,final_val,stable,out_dir,error +dynamic_qa_conditioned_normed_yonly,qa_normed_yonly,2000,42,ok,4163.87,123.69,4.3878,10000,4.3878,True,out-openwebtext_phase15_w2000_qa_normed_yonly, +dynamic_qa_conditioned_normed_yonly,qa_normed_yonly,2000,1337,ok,4158.5,123.69,4.3825,9750,4.4271,False,out-openwebtext_phase15_w2000_qa_normed_yonly, +dynamic_qa_conditioned_normed_yonly,qa_normed_yonly,2000,123,ok,4170.91,123.69,4.3966,9750,4.4073,False,out-openwebtext_phase15_w2000_qa_normed_yonly, +dynamic_qa_conditioned_normed_qonly,qa_normed_qonly,2000,42,ok,4179.72,123.69,4.3798,10000,4.3798,True,out-openwebtext_phase15_w2000_qa_normed_qonly, +dynamic_qa_conditioned_normed_qonly,qa_normed_qonly,2000,1337,ok,4157.51,123.69,4.3892,9750,4.4321,False,out-openwebtext_phase15_w2000_qa_normed_qonly, +dynamic_qa_conditioned_normed_qonly,qa_normed_qonly,2000,123,ok,4156.84,123.69,4.3952,9750,4.406,False,out-openwebtext_phase15_w2000_qa_normed_qonly, diff --git a/results/phase15_qa_asymnorm_w2000_summary_20260331_2223.txt b/results/phase15_qa_asymnorm_w2000_summary_20260331_2223.txt new file mode 100644 index 0000000000000000000000000000000000000000..ff9a262f20899834118107200a92a9fc3f59ae0e --- /dev/null +++ b/results/phase15_qa_asymnorm_w2000_summary_20260331_2223.txt @@ -0,0 +1,44 @@ +PHASE 13 — QA ASYMMETRIC NORMALIZATION ABLATION +==================================================================================================== + +Purpose: + Isolate which signal's magnitude is responsible for qa_normed's late-training instability + by testing the two asymmetric Q+A normalization combinations. + +Scientific prediction: + qa_normed_yonly (raw q + normed y) -> predicted STABLE, performance near cheap_qa + qa_normed_qonly (normed q + raw y) -> predicted UNSTABLE, performance near qa_normed + +Results CSV: C:\Users\brian\Documents\nanoGPT\results\phase15_qa_asymnorm_w2000_runs_20260331_2223.csv + +Recipe: + batch_size=4 gradient_accumulation_steps=2 block_size=512 tokens/iter=4,096 + compile=False warmup=2000 max_iters=10000 seeds=[42, 1337, 123] + +Canon anchors to keep in mind: + cheap_qa 4.3897 (raw q + raw y, stable) + qa_normed 4.3926 (normed q + normed y, unstable) + a_only 4.4298 (raw y only) + +Ablation ranking (completed runs only): + 1. qa_normed_qonly mean=4.3881 std=0.0063 done=3/3 + 2. qa_normed_yonly mean=4.3890 std=0.0058 done=3/3 + +---------------------------------------------------------------------------------------------------- +qa_normed_yonly (dynamic_qa_conditioned_normed_yonly) + Raw q + RMSNorm(y), then shared 128->64 gate + out_dir: out-openwebtext_phase15_w2000_qa_normed_yonly + completed seeds: 3/3 + partial mean/std: 4.3890 ± 0.0058 + seed= 42 best=4.3878 final=4.3878 best@10000 stable=True + seed= 123 best=4.3966 final=4.4073 best@9750 stable=False + seed=1337 best=4.3825 final=4.4271 best@9750 stable=False + +qa_normed_qonly (dynamic_qa_conditioned_normed_qonly) + RMSNorm(q) + raw y, then shared 128->64 gate + out_dir: out-openwebtext_phase15_w2000_qa_normed_qonly + completed seeds: 3/3 + partial mean/std: 4.3881 ± 0.0063 + seed= 42 best=4.3798 final=4.3798 best@10000 stable=True + seed= 123 best=4.3952 final=4.4060 best@9750 stable=False + seed=1337 best=4.3892 final=4.4321 best@9750 stable=False diff --git a/results/phase16_qa_softqa_w2000_runs_20260401_1720.csv b/results/phase16_qa_softqa_w2000_runs_20260401_1720.csv new file mode 100644 index 0000000000000000000000000000000000000000..05f83f4b29b12f139f4b3fb38263357e1e0eb0aa --- /dev/null +++ b/results/phase16_qa_softqa_w2000_runs_20260401_1720.csv @@ -0,0 +1,4 @@ +variant,short_name,warmup,seed,status,runtime_sec,params_m,best_val,best_step,final_val,stable,alpha_q_l0,alpha_y_l0,out_dir,error +dynamic_qa_conditioned_softqa,qa_softqa,2000,42,ok,5043.299421548843,123.69,4.3822,10000,4.3822,True,0.6707101464271545,0.7450965642929077,out-openwebtext_phase16_w2000_qa_softqa, +dynamic_qa_conditioned_softqa,qa_softqa,2000,1337,ok,5004.127624988556,123.69,4.3828,9750,4.4281,False,0.6415046453475952,0.7223806381225586,out-openwebtext_phase16_w2000_qa_softqa, +dynamic_qa_conditioned_softqa,qa_softqa,2000,123,ok,4843.485694408417,123.69,4.4016,9750,4.4112,False,0.676720142364502,0.7526704668998718,out-openwebtext_phase16_w2000_qa_softqa, diff --git a/results/phase16_qa_softqa_w2000_summary_20260401_1720.txt b/results/phase16_qa_softqa_w2000_summary_20260401_1720.txt new file mode 100644 index 0000000000000000000000000000000000000000..1ac03d424ee4f477b541caa132c5706f04ade01e --- /dev/null +++ b/results/phase16_qa_softqa_w2000_summary_20260401_1720.txt @@ -0,0 +1,40 @@ +PHASE 16 — QA SOFT-QA NORMALIZATION (INDEPENDENT DUAL SOFT BLENDS) +==================================================================================================== + +Scientific motivation: + Phase 14 showed soft-q works well, and Phase 15 weakly favored q-only over + y-only normalization. This phase asks whether independent soft blends for + both q and y can improve the stability/performance tradeoff further. + +Scientific predictions: + alpha_q: likely near 0.742 (Phase 14 prior) + alpha_y: likely near 0.0-0.3 if raw y is preferred + Performance: potentially competitive with cheap_qa (4.3897) + Stability: unknown + +Results CSV: C:\Users\brian\Documents\nanoGPT\results\phase16_qa_softqa_w2000_runs_20260401_1720.csv + +Recipe: + batch_size=4 gradient_accumulation_steps=2 block_size=512 tokens/iter=4,096 + compile=False warmup=2000 max_iters=10000 seeds=[42, 1337, 123] + +Canon anchors to keep in mind: + cheap_qa 4.3897 (stable champion) + qa_softq 4.3865 (1/3 stable, alpha_q=0.742) + qa_normed_qonly 4.3881 (1/3 stable) + qa_normed_yonly 4.3890 (1/3 stable) + +Completed runs: + 1. qa_softqa mean=4.3889 std=0.0090 done=3/3 + +---------------------------------------------------------------------------------------------------- +qa_softqa (dynamic_qa_conditioned_softqa) + Independent learned blends for BOTH q and y before shared 128->64 gate + out_dir: out-openwebtext_phase16_w2000_qa_softqa + completed seeds: 3/3 + partial mean/std: 4.3889 ± 0.0090 + learned alpha_q (layer 0) mean across completed seeds: 0.6630 + learned alpha_y (layer 0) mean across completed seeds: 0.7400 + seed= 42 best=4.3822 final=4.3822 best@10000 stable=True alpha_q_l0=0.6707 alpha_y_l0=0.7451 + seed= 123 best=4.4016 final=4.4112 best@9750 stable=False alpha_q_l0=0.6767 alpha_y_l0=0.7527 + seed=1337 best=4.3828 final=4.4281 best@9750 stable=False alpha_q_l0=0.6415 alpha_y_l0=0.7224 diff --git a/results/phase17_qa_softqa_pl_w2000_runs_20260401_2159.csv b/results/phase17_qa_softqa_pl_w2000_runs_20260401_2159.csv new file mode 100644 index 0000000000000000000000000000000000000000..5adedc67a1fcedbc07b128ecda96175383cbbab6 --- /dev/null +++ b/results/phase17_qa_softqa_pl_w2000_runs_20260401_2159.csv @@ -0,0 +1,4 @@ +variant,short_name,warmup,seed,status,runtime_sec,params_m,best_val,best_step,final_val,stable,alpha_q_layers,alpha_y_layers,out_dir,error +dynamic_qa_conditioned_softqa_perlayer,qa_softqa_pl,2000,42,ok,4853.519824028015,123.69,4.379,10000,4.379,True,"[0.5447, 0.4967, 0.4356, 0.4284, 0.4538, 0.4574, 0.4168, 0.3864, 0.4088, 0.424, 0.4536, 0.4554]","[0.6529, 0.5365, 0.4965, 0.4645, 0.4982, 0.5216, 0.5202, 0.4789, 0.4439, 0.5389, 0.5356, 0.514]",out-openwebtext_phase17_w2000_qa_softqa_pl, +dynamic_qa_conditioned_softqa_perlayer,qa_softqa_pl,2000,1337,ok,4855.814431667328,123.69,4.3846,9750,4.4294,False,"[0.5245, 0.4923, 0.4361, 0.4252, 0.4561, 0.4562, 0.411, 0.3803, 0.4069, 0.4272, 0.4699, 0.4598]","[0.6312, 0.5555, 0.4909, 0.4681, 0.4789, 0.5714, 0.4717, 0.4126, 0.4306, 0.4855, 0.5356, 0.5344]",out-openwebtext_phase17_w2000_qa_softqa_pl, +dynamic_qa_conditioned_softqa_perlayer,qa_softqa_pl,2000,123,ok,4853.314908027649,123.69,4.4033,9750,4.4127,False,"[0.5551, 0.5034, 0.44, 0.4057, 0.4711, 0.4345, 0.3973, 0.417, 0.4026, 0.4576, 0.4836, 0.455]","[0.6509, 0.5626, 0.4893, 0.4804, 0.4674, 0.5744, 0.4989, 0.4944, 0.4341, 0.5252, 0.5484, 0.5256]",out-openwebtext_phase17_w2000_qa_softqa_pl, diff --git a/results/phase17_qa_softqa_pl_w2000_summary_20260401_2159.txt b/results/phase17_qa_softqa_pl_w2000_summary_20260401_2159.txt new file mode 100644 index 0000000000000000000000000000000000000000..1caba024c84972cbb10bef101df4faaa9bdfcb26 --- /dev/null +++ b/results/phase17_qa_softqa_pl_w2000_summary_20260401_2159.txt @@ -0,0 +1,50 @@ +PHASE 17 — QA SOFT-QA PER-LAYER NORMALIZATION (INDEPENDENT PER-LAYER SOFT BLENDS) +==================================================================================================== + +Scientific motivation: + Phase 16 showed global soft blends for q and y were competitive but still + showed late-training instability. Phase 17 tests whether per-layer freedom + can resolve that instability by removing cross-layer optimization conflict. + +Scientific predictions: + alpha_q per layer: unknown profile — may show monotonic increase, U-curve, or flat + alpha_y per layer: unknown profile — may differ significantly from alpha_q pattern + Mean alpha_q expected near 0.663 (Phase 16 global mean) + Mean alpha_y expected near 0.740 (Phase 16 global mean) + Performance: potentially competitive with or better than Phase 16 qa_softqa + Stability hypothesis: per-layer freedom may RESOLVE late-training instability + +Results CSV: C:\Users\brian\Documents\nanoGPT\results\phase17_qa_softqa_pl_w2000_runs_20260401_2159.csv + +Recipe: + batch_size=4 gradient_accumulation_steps=2 block_size=512 tokens/iter=4,096 + compile=False warmup=2000 max_iters=10000 seeds=[42, 1337, 123] + +Canon anchors to keep in mind: + cheap_qa 4.3897 (3/3 stable champion) + qa_softqa 4.3889 (1/3 stable, global alphas) + qa_softq 4.3865 (1/3 stable) + qa_normed_qonly 4.3881 (1/3 stable) + +Completed runs: + 1. qa_softqa_pl mean=4.3890 std=0.0104 done=3/3 + +---------------------------------------------------------------------------------------------------- +qa_softqa_pl (dynamic_qa_conditioned_softqa_perlayer) + Independent per-layer learned blends for BOTH q and y before shared 128->64 gate + out_dir: out-openwebtext_phase17_w2000_qa_softqa_pl + completed seeds: 3/3 + partial mean/std: 4.3890 ± 0.0104 + learned alpha_q layerwise mean across completed seeds: [0.5414, 0.4975, 0.4372, 0.4198, 0.4603, 0.4494, 0.4084, 0.3946, 0.4061, 0.4363, 0.469, 0.4567] + learned alpha_q global mean across completed seeds: 0.4481 + learned alpha_y layerwise mean across completed seeds: [0.645, 0.5515, 0.4922, 0.471, 0.4815, 0.5558, 0.4969, 0.462, 0.4362, 0.5165, 0.5399, 0.5247] + learned alpha_y global mean across completed seeds: 0.5144 + seed= 42 best=4.3790 final=4.3790 best@10000 stable=True + alpha_q=[0.5447, 0.4967, 0.4356, 0.4284, 0.4538, 0.4574, 0.4168, 0.3864, 0.4088, 0.424, 0.4536, 0.4554] + alpha_y=[0.6529, 0.5365, 0.4965, 0.4645, 0.4982, 0.5216, 0.5202, 0.4789, 0.4439, 0.5389, 0.5356, 0.514] + seed= 123 best=4.4033 final=4.4127 best@9750 stable=False + alpha_q=[0.5551, 0.5034, 0.44, 0.4057, 0.4711, 0.4345, 0.3973, 0.417, 0.4026, 0.4576, 0.4836, 0.455] + alpha_y=[0.6509, 0.5626, 0.4893, 0.4804, 0.4674, 0.5744, 0.4989, 0.4944, 0.4341, 0.5252, 0.5484, 0.5256] + seed=1337 best=4.3846 final=4.4294 best@9750 stable=False + alpha_q=[0.5245, 0.4923, 0.4361, 0.4252, 0.4561, 0.4562, 0.411, 0.3803, 0.4069, 0.4272, 0.4699, 0.4598] + alpha_y=[0.6312, 0.5555, 0.4909, 0.4681, 0.4789, 0.5714, 0.4717, 0.4126, 0.4306, 0.4855, 0.5356, 0.5344] diff --git a/results/phase17_true_blend_ratios.txt b/results/phase17_true_blend_ratios.txt new file mode 100644 index 0000000000000000000000000000000000000000..2b924b965100290aee96ac031f5ab2455c575806 --- /dev/null +++ b/results/phase17_true_blend_ratios.txt @@ -0,0 +1,123 @@ +====================================================================== +PHASE 17 - TRUE BLEND RATIOS (POST-SIGMOID) +Corrects the raw-param logging bug from original Phase 17 summary +====================================================================== + +Loading: C:\Users\brian\Documents\nanoGPT\out-openwebtext_phase17_w2000_qa_softqa_pl\ckpt.pt +best_val_loss = 4.403288841247559 +iter_num = 9750 + +All alpha/scale keys found (60): + transformer.h.0.attn.qa_q_blend_alpha shape=torch.Size([]) raw=0.22134970128536224 + transformer.h.0.attn.qa_y_blend_alpha shape=torch.Size([]) raw=0.6229410767555237 + transformer.h.0.attn.qa_q_norm_softqa_pl.weight shape=torch.Size([64]) raw=[1.0821291208267212, 0.9229230880737305, 1.0287425518035889, 0.970302164554596, 0.926013708114624, 0.9951927065849304, 0.9508225917816162, 0.9500793218612671, 0.9047906994819641, 0.9762359261512756, 1.0490493774414062, 1.2681221961975098, 0.9607866406440735, 1.0198962688446045, 0.9765430688858032, 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0.00831206887960434, 0.056048113852739334, 0.03429925814270973, -0.06474906951189041]] + +====================================================================== +SCALE_Q - TRUE BLEND RATIOS (sigmoid applied) +0.0 = fully raw q | 1.0 = fully normalized q +====================================================================== + Layer 0 raw=[1.0821291208267212, 0.9229230880737305, 1.0287425518035889, 0.970302164554596, 0.926013708114624, 0.9951927065849304, 0.9508225917816162, 0.9500793218612671, 0.9047906994819641, 0.9762359261512756, 1.0490493774414062, 1.2681221961975098, 0.9607866406440735, 1.0198962688446045, 0.9765430688858032, 1.148079514503479, 0.9260222315788269, 0.9349114894866943, 1.0775954723358154, 1.264096975326538, 1.1377546787261963, 1.085544228553772, 1.0868479013442993, 1.0257149934768677, 0.9414620399475098, 1.1040282249450684, 1.1186190843582153, 0.9496709704399109, 0.993484616279602, 1.0508922338485718, 1.0400117635726929, 0.9107621908187866, 1.0672035217285156, 1.1725153923034668, 0.9717702865600586, 1.0685884952545166, 0.9906771779060364, 0.8423517942428589, 0.9679808020591736, 0.9667661786079407, 1.022877812385559, 0.9952566027641296, 1.0789889097213745, 0.958061158657074, 1.0748014450073242, 1.0072740316390991, 1.202741265296936, 0.9783819317817688, 1.0093022584915161, 0.961561918258667, 1.1005079746246338, 0.8494730591773987, 0.9694604873657227, 0.9692680835723877, 1.0350418090820312, 0.9917517304420471, 1.1555204391479492, 0.9463096261024475, 0.9967873692512512, 0.9533196687698364, 1.1262743473052979, 0.8423093557357788, 0.9750909805297852, 0.9400725364685059] -> sigmoid=[0.7469, 0.7156, 0.7367, 0.7252, 0.7163, 0.7301, 0.7213, 0.7211, 0.7119, 0.7264, 0.7406, 0.7804, 0.7233, 0.735, 0.7264, 0.7592, 0.7163, 0.7181, 0.746, 0.7797, 0.7573, 0.7475, 0.7478, 0.7361, 0.7194, 0.751, 0.7537, 0.721, 0.7298, 0.7409, 0.7389, 0.7132, 0.7441, 0.7636, 0.7255, 0.7443, 0.7292, 0.699, 0.7247, 0.7245, 0.7355, 0.7301, 0.7463, 0.7227, 0.7455, 0.7325, 0.769, 0.7268, 0.7329, 0.7234, 0.7504, 0.7005, 0.725, 0.725, 0.7379, 0.7294, 0.7605, 0.7204, 0.7304, 0.7218, 0.7552, 0.699, 0.7261, 0.7191] + Layer 1 raw=[1.0888714790344238, 0.9683815240859985, 1.0308575630187988, 1.036203384399414, 0.8769784569740295, 0.9439508318901062, 0.9878274202346802, 0.8998557925224304, 1.1207212209701538, 0.8669422268867493, 1.0930489301681519, 1.0876957178115845, 0.8598116636276245, 0.8730360865592957, 0.854408860206604, 1.0438449382781982, 0.8021169304847717, 1.0591315031051636, 1.0361675024032593, 1.062071681022644, 1.0186675786972046, 1.0404473543167114, 1.062187910079956, 0.9835883975028992, 0.869575023651123, 1.043290376663208, 0.9877099990844727, 1.0486388206481934, 1.0143513679504395, 1.097993016242981, 1.0986965894699097, 0.9622485637664795, 0.990914523601532, 1.0257532596588135, 0.8450654149055481, 1.0889806747436523, 0.9723054766654968, 0.8048465251922607, 0.9324176907539368, 0.8885783553123474, 1.0355088710784912, 0.8475661277770996, 0.866688072681427, 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0.9746896028518677, 0.9405969381332397, 0.8827962279319763, 0.8104851841926575, 0.8867903351783752, 0.9225919842720032, 0.9714711904525757, 0.8606827855110168, 1.0048398971557617, 0.8344006538391113, 0.8099696636199951, 1.0355385541915894, 0.8993537425994873, 0.8849891424179077, 0.9244243502616882, 0.8325510025024414, 0.8688273429870605, 1.088134527206421, 1.192196249961853, 0.8132022619247437, 0.9692491888999939, 0.9234764575958252, 1.0957564115524292, 0.8455065488815308, 0.897262454032898, 0.8572636842727661, 0.9621909260749817, 1.0363540649414062, 0.9360591769218445, 1.052431583404541, 1.0336601734161377, 1.0012716054916382, 0.8702583909034729, 0.9110122323036194, 0.9192014932632446, 0.9829489588737488, 0.8913535475730896, 0.9488621354103088, 0.9214871525764465, 0.8805440664291382, 0.891421914100647, 0.8608114123344421, 0.8943984508514404, 0.8306775689125061, 0.8976739645004272, 0.920957088470459, 0.9434120655059814, 0.9252135157585144, 0.8682395815849304, 0.8936537504196167, 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1.0130900144577026, 1.086514949798584, 1.091429591178894, 1.1016879081726074, 1.0134526491165161, 1.1050622463226318, 1.017113208770752, 1.0383607149124146, 1.0863604545593262, 1.1513501405715942, 1.1813623905181885, 1.1414302587509155, 1.0269678831100464, 1.171947956085205, 1.0701149702072144, 1.1395326852798462, 1.0817588567733765, 1.0957286357879639, 1.0939733982086182, 1.1020877361297607, 1.140384554862976, 1.0164802074432373, 1.1402013301849365, 1.0981801748275757, 1.0816022157669067, 1.002514362335205] -> sigmoid=[0.7738, 0.7282, 0.7192, 0.7377, 0.7473, 0.748, 0.7362, 0.7431, 0.7421, 0.7552, 0.7587, 0.7673, 0.7276, 0.7348, 0.7524, 0.7607, 0.7507, 0.7371, 0.7574, 0.748, 0.7505, 0.733, 0.7336, 0.7545, 0.7452, 0.7501, 0.7378, 0.7493, 0.7428, 0.7379, 0.7553, 0.7513, 0.7391, 0.7563, 0.7384, 0.7555, 0.753, 0.7475, 0.7336, 0.7477, 0.7487, 0.7506, 0.7337, 0.7512, 0.7344, 0.7385, 0.7477, 0.7598, 0.7652, 0.7579, 0.7363, 0.7635, 0.7446, 0.7576, 0.7468, 0.7495, 0.7491, 0.7507, 0.7578, 0.7343, 0.7577, 0.7499, 0.7468, 0.7316] + + Summary scale_q: + mean=0.7437 std=0.0200 min=0.6853 max=0.8109 + Layer sigmoid means: [0.7469, 0.7156, 0.7367, 0.7252, 0.7163, 0.7301, 0.7213, 0.7211, 0.7119, 0.7264, 0.7406, 0.7804, 0.7233, 0.735, 0.7264, 0.7592, 0.7163, 0.7181, 0.746, 0.7797, 0.7573, 0.7475, 0.7478, 0.7361, 0.7194, 0.751, 0.7537, 0.721, 0.7298, 0.7409, 0.7389, 0.7132, 0.7441, 0.7636, 0.7255, 0.7443, 0.7292, 0.699, 0.7247, 0.7245, 0.7355, 0.7301, 0.7463, 0.7227, 0.7455, 0.7325, 0.769, 0.7268, 0.7329, 0.7234, 0.7504, 0.7005, 0.725, 0.725, 0.7379, 0.7294, 0.7605, 0.7204, 0.7304, 0.7218, 0.7552, 0.699, 0.7261, 0.7191, 0.7482, 0.7248, 0.7371, 0.7381, 0.7062, 0.7199, 0.7287, 0.7109, 0.7541, 0.7041, 0.749, 0.7479, 0.7026, 0.7054, 0.7015, 0.7396, 0.6904, 0.7425, 0.7381, 0.7431, 0.7347, 0.7389, 0.7431, 0.7278, 0.7047, 0.7395, 0.7286, 0.7405, 0.7339, 0.7499, 0.75, 0.7236, 0.7293, 0.7361, 0.6995, 0.7482, 0.7256, 0.691, 0.7176, 0.7086, 0.738, 0.7001, 0.7041, 0.6955, 0.7522, 0.7445, 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0.7336, 0.7477, 0.7487, 0.7506, 0.7337, 0.7512, 0.7344, 0.7385, 0.7477, 0.7598, 0.7652, 0.7579, 0.7363, 0.7635, 0.7446, 0.7576, 0.7468, 0.7495, 0.7491, 0.7507, 0.7578, 0.7343, 0.7577, 0.7499, 0.7468, 0.7316] + +====================================================================== +SCALE_Y - TRUE BLEND RATIOS (sigmoid applied) +0.0 = fully raw y | 1.0 = fully normalized y +====================================================================== + Layer 0 raw=[1.1819450855255127, 1.0849997997283936, 1.0062412023544312, 0.936033308506012, 1.371364712715149, 1.308315396308899, 1.2978459596633911, 1.0923306941986084, 1.040870189666748, 1.0141125917434692, 1.0304840803146362, 1.115607500076294, 1.0096174478530884, 1.1382591724395752, 1.267049789428711, 1.3509308099746704, 0.9869304895401001, 1.250954508781433, 1.0147618055343628, 1.0241997241973877, 1.2716597318649292, 1.3232743740081787, 1.0957579612731934, 1.098982572555542, 0.9084482789039612, 1.4096579551696777, 1.249984860420227, 1.1930099725723267, 1.2232370376586914, 0.884830117225647, 1.3688397407531738, 0.9225565791130066, 1.2211637496948242, 1.322664737701416, 1.3503265380859375, 1.055283546447754, 0.8699869513511658, 1.2087986469268799, 1.0652620792388916, 1.2998201847076416, 1.0692222118377686, 0.9052255153656006, 1.1466871500015259, 1.2987098693847656, 1.0510327816009521, 1.1084469556808472, 1.073096513748169, 1.067969560623169, 1.382784128189087, 1.0526341199874878, 1.2217146158218384, 1.2765976190567017, 0.9858461022377014, 1.0806890726089478, 1.3361881971359253, 1.131898045539856, 1.1609166860580444, 1.3739122152328491, 1.2837767601013184, 0.9841175079345703, 1.031618356704712, 1.0992965698242188, 1.3130322694778442, 0.9501575231552124] -> sigmoid=[0.7653, 0.7474, 0.7323, 0.7183, 0.7976, 0.7872, 0.7855, 0.7488, 0.739, 0.7338, 0.737, 0.7532, 0.7329, 0.7574, 0.7802, 0.7943, 0.7285, 0.7775, 0.734, 0.7358, 0.781, 0.7897, 0.7495, 0.7501, 0.7127, 0.8037, 0.7773, 0.7673, 0.7726, 0.7078, 0.7972, 0.7156, 0.7723, 0.7896, 0.7942, 0.7418, 0.7047, 0.7701, 0.7437, 0.7858, 0.7444, 0.712, 0.7589, 0.7856, 0.741, 0.7518, 0.7452, 0.7442, 0.7994, 0.7413, 0.7724, 0.7819, 0.7283, 0.7466, 0.7919, 0.7562, 0.7615, 0.798, 0.7831, 0.7279, 0.7372, 0.7501, 0.788, 0.7211] + Layer 1 raw=[0.9440274238586426, 0.7957388758659363, 0.881196141242981, 0.874348521232605, 0.9004610180854797, 0.8327299952507019, 0.9180752038955688, 0.9006534814834595, 0.8932895660400391, 0.8147370219230652, 0.9121181964874268, 0.955083429813385, 0.9040775299072266, 0.9943276643753052, 0.9246459603309631, 0.9252259135246277, 0.9035545587539673, 0.8453344106674194, 0.904547929763794, 0.9050214290618896, 0.8999736309051514, 0.8652458190917969, 0.9461787343025208, 0.8463034629821777, 0.9108654856681824, 0.934725821018219, 0.9038253426551819, 0.9046840667724609, 0.8540454506874084, 0.9388730525970459, 0.9180430173873901, 0.9336933493614197, 0.921846330165863, 0.9248925447463989, 0.8981353044509888, 0.9162894487380981, 0.9120555520057678, 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sigmoid=[0.7504, 0.7664, 0.7628, 0.7538, 0.7626, 0.7528, 0.7749, 0.7562, 0.7605, 0.7705, 0.7618, 0.7566, 0.7746, 0.7545, 0.7533, 0.7617, 0.7551, 0.7526, 0.7679, 0.7567, 0.765, 0.7603, 0.7448, 0.7553, 0.7519, 0.78, 0.767, 0.7623, 0.7494, 0.7621, 0.7557, 0.7557, 0.764, 0.7694, 0.7786, 0.7531, 0.7696, 0.7612, 0.7424, 0.7693, 0.7323, 0.7528, 0.7507, 0.7619, 0.7683, 0.7561, 0.7558, 0.7678, 0.7693, 0.7692, 0.7653, 0.7583, 0.7742, 0.7743, 0.7648, 0.749, 0.7487, 0.7528, 0.7611, 0.7503, 0.7554, 0.7777, 0.7649, 0.7747] + Layer 5 raw=[1.2109885215759277, 1.2185466289520264, 1.186621904373169, 1.2975825071334839, 1.2706433534622192, 1.186183214187622, 1.1959669589996338, 1.3186455965042114, 1.1772786378860474, 1.2577235698699951, 1.2738014459609985, 1.2786622047424316, 1.2651474475860596, 1.241012692451477, 1.2207258939743042, 1.2022483348846436, 1.339530348777771, 1.2456098794937134, 1.2167820930480957, 1.2295324802398682, 1.305363416671753, 1.2915197610855103, 1.293481707572937, 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1.1837300062179565, 1.1588208675384521, 1.2546101808547974, 1.0943584442138672, 1.2082176208496094, 1.164724349975586, 1.1557800769805908, 1.1197264194488525, 1.0716722011566162, 1.1930515766143799, 1.0594514608383179, 1.194108486175537, 1.0958386659622192, 1.1993908882141113] -> sigmoid=[0.7461, 0.7557, 0.7807, 0.755, 0.7397, 0.7772, 0.782, 0.7825, 0.7724, 0.7388, 0.763, 0.7619, 0.7555, 0.7641, 0.7537, 0.7561, 0.7755, 0.753, 0.7581, 0.7466, 0.7611, 0.7697, 0.7541, 0.7673, 0.7835, 0.7287, 0.7466, 0.746, 0.7708, 0.7533, 0.7667, 0.7777, 0.7468, 0.7812, 0.7565, 0.7768, 0.7696, 0.7559, 0.7725, 0.7581, 0.7749, 0.765, 0.7677, 0.7615, 0.7586, 0.7547, 0.7341, 0.7484, 0.7496, 0.7549, 0.7656, 0.7611, 0.7781, 0.7492, 0.77, 0.7622, 0.7606, 0.7539, 0.7449, 0.7673, 0.7426, 0.7675, 0.7495, 0.7684] + Layer 9 raw=[1.0742980241775513, 1.0729812383651733, 1.0559521913528442, 1.0530411005020142, 1.054856777191162, 1.0794094800949097, 1.1349021196365356, 0.9953089356422424, 0.932023823261261, 1.0710290670394897, 1.0425336360931396, 0.9995422959327698, 1.0350638628005981, 1.074638843536377, 1.1116214990615845, 1.0238860845565796, 0.9750809669494629, 1.1148414611816406, 1.0882405042648315, 1.0319464206695557, 1.0971288681030273, 1.061696171760559, 1.0601686239242554, 1.0397868156433105, 1.1910358667373657, 0.9826194047927856, 1.0609734058380127, 1.0393184423446655, 1.0463573932647705, 0.9915756583213806, 1.1046572923660278, 1.0666522979736328, 1.0556364059448242, 1.0716092586517334, 1.0956523418426514, 0.9741443395614624, 0.9879555702209473, 1.080291748046875, 1.1140656471252441, 1.0233900547027588, 1.0819660425186157, 1.0520310401916504, 1.0939006805419922, 1.0909459590911865, 1.0734561681747437, 1.0371873378753662, 1.0241360664367676, 1.087043285369873, 1.0781917572021484, 1.037690281867981, 1.0006396770477295, 1.0820947885513306, 0.9677733778953552, 1.0904996395111084, 1.0715208053588867, 1.0584653615951538, 0.9611716866493225, 1.062748670578003, 1.0790876150131226, 1.0081804990768433, 1.0044591426849365, 1.0455348491668701, 1.0808721780776978, 1.0700100660324097] -> sigmoid=[0.7454, 0.7452, 0.7419, 0.7414, 0.7417, 0.7464, 0.7567, 0.7301, 0.7175, 0.7448, 0.7393, 0.731, 0.7379, 0.7455, 0.7524, 0.7357, 0.7261, 0.753, 0.7481, 0.7373, 0.7497, 0.743, 0.7427, 0.7388, 0.7669, 0.7276, 0.7429, 0.7387, 0.7401, 0.7294, 0.7511, 0.744, 0.7419, 0.7449, 0.7494, 0.7259, 0.7287, 0.7465, 0.7529, 0.7356, 0.7469, 0.7412, 0.7491, 0.7486, 0.7453, 0.7383, 0.7358, 0.7478, 0.7462, 0.7384, 0.7312, 0.7469, 0.7247, 0.7485, 0.7449, 0.7424, 0.7234, 0.7432, 0.7463, 0.7327, 0.7319, 0.7399, 0.7467, 0.7446] + Layer 10 raw=[1.0002530813217163, 1.0578525066375732, 1.0711861848831177, 1.0870498418807983, 1.0656447410583496, 1.1015673875808716, 1.0218509435653687, 1.0189119577407837, 1.0869144201278687, 1.1060103178024292, 1.0235817432403564, 1.0844649076461792, 0.9998311400413513, 1.1170963048934937, 1.0842807292938232, 1.1591618061065674, 1.1032097339630127, 0.9681366682052612, 1.0481582880020142, 1.047249674797058, 1.0236490964889526, 1.0429219007492065, 1.0343331098556519, 1.0720218420028687, 1.0474685430526733, 1.0445339679718018, 1.0610435009002686, 1.0995664596557617, 1.0549840927124023, 1.0359082221984863, 1.0648667812347412, 1.0695730447769165, 1.0496762990951538, 1.0772396326065063, 1.0979533195495605, 1.0599724054336548, 1.1186282634735107, 1.0235283374786377, 1.0281685590744019, 1.0224134922027588, 1.0155140161514282, 1.069002389907837, 1.0622011423110962, 1.0629714727401733, 0.9943235516548157, 1.0745383501052856, 1.0376925468444824, 1.1039292812347412, 1.0694921016693115, 1.0692657232284546, 1.0612280368804932, 1.1209933757781982, 0.9818269610404968, 1.1425213813781738, 1.06732177734375, 1.0262147188186646, 1.0222904682159424, 0.9985707998275757, 1.0594979524612427, 1.0699933767318726, 1.0916184186935425, 1.057707667350769, 1.026161789894104, 1.0341166257858276] -> sigmoid=[0.7311, 0.7423, 0.7448, 0.7478, 0.7438, 0.7506, 0.7353, 0.7348, 0.7478, 0.7514, 0.7357, 0.7473, 0.731, 0.7534, 0.7473, 0.7612, 0.7509, 0.7247, 0.7404, 0.7402, 0.7357, 0.7394, 0.7378, 0.745, 0.7403, 0.7397, 0.7429, 0.7502, 0.7417, 0.7381, 0.7436, 0.7445, 0.7407, 0.746, 0.7499, 0.7427, 0.7537, 0.7357, 0.7366, 0.7354, 0.7341, 0.7444, 0.7431, 0.7433, 0.7299, 0.7455, 0.7384, 0.751, 0.7445, 0.7445, 0.7429, 0.7542, 0.7275, 0.7581, 0.7441, 0.7362, 0.7354, 0.7308, 0.7426, 0.7446, 0.7487, 0.7423, 0.7362, 0.7377] + Layer 11 raw=[1.1193667650222778, 1.147385835647583, 1.097097635269165, 0.9933834075927734, 0.9980084896087646, 1.0441478490829468, 1.044007420539856, 0.9760099649429321, 1.0492956638336182, 1.0213388204574585, 1.0112487077713013, 1.1245192289352417, 1.0592941045761108, 1.0692217350006104, 1.0262787342071533, 1.1505814790725708, 1.0740840435028076, 1.0186302661895752, 1.05946946144104, 1.1090787649154663, 1.091670036315918, 1.0298750400543213, 0.9855073690414429, 1.0906612873077393, 1.1045197248458862, 0.9989240169525146, 0.996306300163269, 1.1303681135177612, 0.9611859917640686, 1.0414289236068726, 1.053889274597168, 1.0398911237716675, 1.0872108936309814, 1.047964334487915, 1.0676370859146118, 1.0832451581954956, 1.1516960859298706, 1.0477523803710938, 1.0061192512512207, 1.191788911819458, 0.9997351765632629, 1.0522451400756836, 1.0592756271362305, 1.0301644802093506, 1.0925674438476562, 1.1373350620269775, 1.049342155456543, 1.0478137731552124, 0.9966427683830261, 1.0834174156188965, 1.1824690103530884, 1.0456782579421997, 1.0131688117980957, 1.0488460063934326, 1.003916621208191, 1.0350840091705322, 1.1213569641113281, 1.0185515880584717, 1.0271755456924438, 1.0560983419418335, 1.077828288078308, 1.089206576347351, 1.1552215814590454, 1.0395045280456543] -> sigmoid=[0.7539, 0.759, 0.7497, 0.7298, 0.7307, 0.7396, 0.7396, 0.7263, 0.7406, 0.7352, 0.7333, 0.7548, 0.7426, 0.7444, 0.7362, 0.7596, 0.7454, 0.7347, 0.7426, 0.752, 0.7487, 0.7369, 0.7282, 0.7485, 0.7511, 0.7308, 0.7303, 0.7559, 0.7234, 0.7391, 0.7415, 0.7388, 0.7479, 0.7404, 0.7441, 0.7471, 0.7598, 0.7403, 0.7323, 0.7671, 0.731, 0.7412, 0.7426, 0.7369, 0.7489, 0.7572, 0.7406, 0.7404, 0.7304, 0.7471, 0.7654, 0.7399, 0.7336, 0.7406, 0.7318, 0.7379, 0.7542, 0.7347, 0.7364, 0.7419, 0.7461, 0.7482, 0.7605, 0.7388] + + Summary scale_y: + mean=0.7496 std=0.0203 min=0.6891 max=0.8037 + Layer sigmoid means: [0.7653, 0.7474, 0.7323, 0.7183, 0.7976, 0.7872, 0.7855, 0.7488, 0.739, 0.7338, 0.737, 0.7532, 0.7329, 0.7574, 0.7802, 0.7943, 0.7285, 0.7775, 0.734, 0.7358, 0.781, 0.7897, 0.7495, 0.7501, 0.7127, 0.8037, 0.7773, 0.7673, 0.7726, 0.7078, 0.7972, 0.7156, 0.7723, 0.7896, 0.7942, 0.7418, 0.7047, 0.7701, 0.7437, 0.7858, 0.7444, 0.712, 0.7589, 0.7856, 0.741, 0.7518, 0.7452, 0.7442, 0.7994, 0.7413, 0.7724, 0.7819, 0.7283, 0.7466, 0.7919, 0.7562, 0.7615, 0.798, 0.7831, 0.7279, 0.7372, 0.7501, 0.788, 0.7211, 0.7199, 0.6891, 0.7071, 0.7056, 0.711, 0.6969, 0.7146, 0.7111, 0.7096, 0.6931, 0.7134, 0.7221, 0.7118, 0.7299, 0.716, 0.7161, 0.7117, 0.6996, 0.7119, 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0.7689, 0.7924, 0.7765, 0.7715, 0.7737, 0.7867, 0.7844, 0.7847, 0.7665, 0.7881, 0.7868, 0.7782, 0.7651, 0.7861, 0.7978, 0.769, 0.7875, 0.7582, 0.7675, 0.7848, 0.7679, 0.7843, 0.7821, 0.7743, 0.7689, 0.7877, 0.7661, 0.7705, 0.7702, 0.7735, 0.771, 0.7935, 0.7769, 0.7818, 0.762, 0.7755, 0.7895, 0.7712, 0.7619, 0.7772, 0.7771, 0.7741, 0.7728, 0.7723, 0.7932, 0.7764, 0.7654, 0.7786, 0.7669, 0.7622, 0.7667, 0.7518, 0.7674, 0.7681, 0.7765, 0.7763, 0.7724, 0.7719, 0.758, 0.7735, 0.7695, 0.7707, 0.7697, 0.767, 0.7726, 0.7754, 0.7648, 0.7573, 0.7692, 0.7709, 0.7716, 0.7703, 0.7582, 0.7575, 0.7668, 0.7689, 0.7815, 0.7668, 0.7678, 0.7846, 0.7659, 0.7535, 0.7663, 0.7657, 0.7559, 0.7601, 0.7611, 0.7658, 0.7493, 0.7598, 0.776, 0.7647, 0.7695, 0.7747, 0.7596, 0.7677, 0.7745, 0.7735, 0.7621, 0.7652, 0.7523, 0.7555, 0.7611, 0.7706, 0.7702, 0.7443, 0.7543, 0.7618, 0.7829, 0.7575, 0.7689, 0.7633, 0.7557, 0.7585, 0.744, 0.7662, 0.7676, 0.7571, 0.7577, 0.7609, 0.7488, 0.7568, 0.7669, 0.7424, 0.7634, 0.7548, 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0.731, 0.7379, 0.7455, 0.7524, 0.7357, 0.7261, 0.753, 0.7481, 0.7373, 0.7497, 0.743, 0.7427, 0.7388, 0.7669, 0.7276, 0.7429, 0.7387, 0.7401, 0.7294, 0.7511, 0.744, 0.7419, 0.7449, 0.7494, 0.7259, 0.7287, 0.7465, 0.7529, 0.7356, 0.7469, 0.7412, 0.7491, 0.7486, 0.7453, 0.7383, 0.7358, 0.7478, 0.7462, 0.7384, 0.7312, 0.7469, 0.7247, 0.7485, 0.7449, 0.7424, 0.7234, 0.7432, 0.7463, 0.7327, 0.7319, 0.7399, 0.7467, 0.7446, 0.7311, 0.7423, 0.7448, 0.7478, 0.7438, 0.7506, 0.7353, 0.7348, 0.7478, 0.7514, 0.7357, 0.7473, 0.731, 0.7534, 0.7473, 0.7612, 0.7509, 0.7247, 0.7404, 0.7402, 0.7357, 0.7394, 0.7378, 0.745, 0.7403, 0.7397, 0.7429, 0.7502, 0.7417, 0.7381, 0.7436, 0.7445, 0.7407, 0.746, 0.7499, 0.7427, 0.7537, 0.7357, 0.7366, 0.7354, 0.7341, 0.7444, 0.7431, 0.7433, 0.7299, 0.7455, 0.7384, 0.751, 0.7445, 0.7445, 0.7429, 0.7542, 0.7275, 0.7581, 0.7441, 0.7362, 0.7354, 0.7308, 0.7426, 0.7446, 0.7487, 0.7423, 0.7362, 0.7377, 0.7539, 0.759, 0.7497, 0.7298, 0.7307, 0.7396, 0.7396, 0.7263, 0.7406, 0.7352, 0.7333, 0.7548, 0.7426, 0.7444, 0.7362, 0.7596, 0.7454, 0.7347, 0.7426, 0.752, 0.7487, 0.7369, 0.7282, 0.7485, 0.7511, 0.7308, 0.7303, 0.7559, 0.7234, 0.7391, 0.7415, 0.7388, 0.7479, 0.7404, 0.7441, 0.7471, 0.7598, 0.7403, 0.7323, 0.7671, 0.731, 0.7412, 0.7426, 0.7369, 0.7489, 0.7572, 0.7406, 0.7404, 0.7304, 0.7471, 0.7654, 0.7399, 0.7336, 0.7406, 0.7318, 0.7379, 0.7542, 0.7347, 0.7364, 0.7419, 0.7461, 0.7482, 0.7605, 0.7388] + +====================================================================== +COMPARISON: Phase 17 true ratios vs Phase 19b (for context) +====================================================================== + Phase 17 scale_q sigmoid mean: 0.7437 + Phase 17 scale_y sigmoid mean: 0.7496 + Phase 19b scale_q global mean: 0.4816 (from canonical run) + Phase 19b scale_y global mean: 0.5046 (from canonical run) + + If Phase 17 is also ~0.5, the per-layer 'preferences' were init artifacts. + If Phase 17 shows genuine spread, there IS a real per-layer signal. \ No newline at end of file diff --git a/results/phase18a_qa_softqa_pl_w1000_runs_20260402_0738.csv b/results/phase18a_qa_softqa_pl_w1000_runs_20260402_0738.csv new file mode 100644 index 0000000000000000000000000000000000000000..05441e86c617ba37a83487f27254bf903b9cd26e --- /dev/null +++ b/results/phase18a_qa_softqa_pl_w1000_runs_20260402_0738.csv @@ -0,0 +1,4 @@ +variant,short_name,warmup,seed,status,runtime_sec,params_m,best_val,best_step,final_val,stable,alpha_q_layers,alpha_y_layers,out_dir,error +dynamic_qa_conditioned_softqa_perlayer,qa_softqa_pl_w1000,1000,42,ok,4884.583526849747,123.69,4.5326,10000,4.5326,True,"[0.5783, 0.5083, 0.5031, 0.4884, 0.4893, 0.4728, 0.4466, 0.4148, 0.4211, 0.4273, 0.4191, 0.4421]","[0.664, 0.5536, 0.5349, 0.5248, 0.4907, 0.4702, 0.4463, 0.4236, 0.4525, 0.4653, 0.4975, 0.5152]",out-openwebtext_phase18a_w1000_qa_softqa_pl, +dynamic_qa_conditioned_softqa_perlayer,qa_softqa_pl_w1000,1000,1337,ok,4800.1115601062775,123.69,4.5471,9750,4.5891,False,"[0.5808, 0.5155, 0.488, 0.4997, 0.4858, 0.4847, 0.4677, 0.4414, 0.3835, 0.4202, 0.4327, 0.4386]","[0.6658, 0.5761, 0.5351, 0.5256, 0.4999, 0.4719, 0.4537, 0.4338, 0.4122, 0.4373, 0.4569, 0.5127]",out-openwebtext_phase18a_w1000_qa_softqa_pl, +dynamic_qa_conditioned_softqa_perlayer,qa_softqa_pl_w1000,1000,123,ok,4809.684059381485,123.69,4.5526,9750,4.556,False,"[0.5751, 0.5153, 0.5106, 0.4869, 0.4785, 0.4784, 0.4281, 0.4162, 0.4088, 0.4085, 0.4585, 0.417]","[0.6516, 0.5463, 0.5416, 0.5243, 0.509, 0.4793, 0.4395, 0.4087, 0.408, 0.4786, 0.5243, 0.5142]",out-openwebtext_phase18a_w1000_qa_softqa_pl, diff --git a/results/phase18a_qa_softqa_pl_w1000_summary_20260402_0738.txt b/results/phase18a_qa_softqa_pl_w1000_summary_20260402_0738.txt new file mode 100644 index 0000000000000000000000000000000000000000..14e8a32504a136eb4e89a52010baad820418ec54 --- /dev/null +++ b/results/phase18a_qa_softqa_pl_w1000_summary_20260402_0738.txt @@ -0,0 +1,55 @@ +PHASE 18A — QA SOFT-QA PER-LAYER NORMALIZATION (INDEPENDENT PER-LAYER SOFT BLENDS) +==================================================================================================== + +Scientific motivation: + Tests whether qa_softqa_perlayer collapses under reduced warmup like other high-dimensional gates, + or whether per-layer freedom provides robustness. Our warmup sensitivity law predicts + degraded performance at w1000. + +Scientific predictions: + alpha_q per layer: unknown profile — may show monotonic increase, U-curve, or flat + alpha_y per layer: unknown profile — may differ significantly from alpha_q pattern + Mean alpha_q expected near 0.663 (Phase 16 global mean) + Mean alpha_y expected near 0.740 (Phase 16 global mean) +- Performance prediction: WORSE than w2000 (high-dim gate sensitivity law) + Stability hypothesis: per-layer freedom may RESOLVE late-training instability + +Warmup sensitivity context: + Phase 17 w2000: mean=4.3890 1/3 stable + Phase 18a w1000: prediction — WORSE (high-dim gate sensitivity law) + Phase 18b w3000: prediction — potentially MORE stable, performance near 4.379 + +Results CSV: C:\Users\brian\Documents\nanoGPT\results\phase18a_qa_softqa_pl_w1000_runs_20260402_0738.csv + +Recipe: + batch_size=4 gradient_accumulation_steps=2 block_size=512 tokens/iter=4,096 + compile=False warmup=1000 max_iters=10000 seeds=[42, 1337, 123] + +Canon anchors to keep in mind: + cheap_qa 4.3897 (3/3 stable champion) + qa_softqa_pl w2000 4.3890 (1/3 stable, seed 42 best: 4.3790) + qa_softqa 4.3889 (1/3 stable) + qa_softq 4.3865 (1/3 stable) + +Completed runs: + 1. qa_softqa_pl_w1000 mean=4.5441 std=0.0084 done=3/3 + +---------------------------------------------------------------------------------------------------- +qa_softqa_pl_w1000 (dynamic_qa_conditioned_softqa_perlayer) + Independent per-layer learned blends for BOTH q and y before shared 128->64 gate + out_dir: out-openwebtext_phase18a_w1000_qa_softqa_pl + completed seeds: 3/3 + partial mean/std: 4.5441 ± 0.0084 + learned alpha_q layerwise mean across completed seeds: [0.5781, 0.513, 0.5006, 0.4917, 0.4845, 0.4786, 0.4475, 0.4241, 0.4045, 0.4187, 0.4368, 0.4326] + learned alpha_q global mean across completed seeds: 0.4675 + learned alpha_y layerwise mean across completed seeds: [0.6605, 0.5587, 0.5372, 0.5249, 0.4999, 0.4738, 0.4465, 0.422, 0.4242, 0.4604, 0.4929, 0.514] + learned alpha_y global mean across completed seeds: 0.5013 + seed= 42 best=4.5326 final=4.5326 best@10000 stable=True + alpha_q=[0.5783, 0.5083, 0.5031, 0.4884, 0.4893, 0.4728, 0.4466, 0.4148, 0.4211, 0.4273, 0.4191, 0.4421] + alpha_y=[0.664, 0.5536, 0.5349, 0.5248, 0.4907, 0.4702, 0.4463, 0.4236, 0.4525, 0.4653, 0.4975, 0.5152] + seed= 123 best=4.5526 final=4.5560 best@9750 stable=False + alpha_q=[0.5751, 0.5153, 0.5106, 0.4869, 0.4785, 0.4784, 0.4281, 0.4162, 0.4088, 0.4085, 0.4585, 0.417] + alpha_y=[0.6516, 0.5463, 0.5416, 0.5243, 0.509, 0.4793, 0.4395, 0.4087, 0.408, 0.4786, 0.5243, 0.5142] + seed=1337 best=4.5471 final=4.5891 best@9750 stable=False + alpha_q=[0.5808, 0.5155, 0.488, 0.4997, 0.4858, 0.4847, 0.4677, 0.4414, 0.3835, 0.4202, 0.4327, 0.4386] + alpha_y=[0.6658, 0.5761, 0.5351, 0.5256, 0.4999, 0.4719, 0.4537, 0.4338, 0.4122, 0.4373, 0.4569, 0.5127] diff --git a/results/phase18b_qa_softqa_pl_w3000_runs_20260402_1640.csv b/results/phase18b_qa_softqa_pl_w3000_runs_20260402_1640.csv new file mode 100644 index 0000000000000000000000000000000000000000..0e2e8280c59b65c40890b5cb6b39ab64810a424f --- /dev/null +++ b/results/phase18b_qa_softqa_pl_w3000_runs_20260402_1640.csv @@ -0,0 +1,4 @@ +variant,short_name,warmup,seed,status,runtime_sec,params_m,best_val,best_step,final_val,stable,alpha_q_layers,alpha_y_layers,out_dir,error +dynamic_qa_conditioned_softqa_perlayer,qa_softqa_pl_w3000,3000,42,ok,5538.173526287079,123.69,4.3747,10000,4.3747,True,"[0.5074, 0.4657, 0.4204, 0.3845, 0.4865, 0.4086, 0.3955, 0.4102, 0.3958, 0.4626, 0.4621, 0.4631]","[0.6308, 0.4924, 0.4931, 0.4604, 0.5081, 0.5096, 0.5197, 0.4726, 0.4664, 0.5384, 0.5515, 0.5482]",out-openwebtext_phase18b_w3000_qa_softqa_pl, +dynamic_qa_conditioned_softqa_perlayer,qa_softqa_pl_w3000,3000,1337,ok,4877.768312692642,123.69,4.3648,9750,4.4104,False,"[0.4962, 0.4608, 0.4374, 0.3873, 0.4712, 0.4133, 0.4019, 0.4024, 0.4241, 0.46, 0.4813, 0.4866]","[0.6291, 0.5223, 0.4892, 0.4645, 0.5202, 0.6036, 0.4696, 0.4402, 0.4668, 0.5373, 0.5515, 0.5402]",out-openwebtext_phase18b_w3000_qa_softqa_pl, +dynamic_qa_conditioned_softqa_perlayer,qa_softqa_pl_w3000,3000,123,ok,4851.970255851746,123.69,4.3888,9750,4.3995,False,"[0.4943, 0.4985, 0.4126, 0.3781, 0.4696, 0.4609, 0.3845, 0.4188, 0.4144, 0.4413, 0.4703, 0.4844]","[0.6345, 0.5595, 0.4973, 0.4671, 0.454, 0.5787, 0.5672, 0.4604, 0.4484, 0.5294, 0.5457, 0.5519]",out-openwebtext_phase18b_w3000_qa_softqa_pl, diff --git a/results/phase18b_qa_softqa_pl_w3000_summary_20260402_1640.txt b/results/phase18b_qa_softqa_pl_w3000_summary_20260402_1640.txt new file mode 100644 index 0000000000000000000000000000000000000000..f7723d67e01ea609bd65c7a34ed243c004fffce8 --- /dev/null +++ b/results/phase18b_qa_softqa_pl_w3000_summary_20260402_1640.txt @@ -0,0 +1,55 @@ +PHASE 18B — QA SOFT-QA PER-LAYER NORMALIZATION (INDEPENDENT PER-LAYER SOFT BLENDS) +==================================================================================================== + +Scientific motivation: + Tests whether extended warmup stabilizes qa_softqa_perlayer. Hypothesis: + gate Linear weights diverge late-training at w2000. More warmup may resolve this and + achieve 3/3 stability while maintaining performance near 4.379. + +Scientific predictions: + alpha_q per layer: unknown profile — may show monotonic increase, U-curve, or flat + alpha_y per layer: unknown profile — may differ significantly from alpha_q pattern + Mean alpha_q expected near 0.663 (Phase 16 global mean) + Mean alpha_y expected near 0.740 (Phase 16 global mean) +- Performance prediction: potentially MORE stable, with performance near 4.379 + Stability hypothesis: per-layer freedom may RESOLVE late-training instability + +Warmup sensitivity context: + Phase 17 w2000: mean=4.3890 1/3 stable + Phase 18a w1000: prediction — WORSE (high-dim gate sensitivity law) + Phase 18b w3000: prediction — potentially MORE stable, performance near 4.379 + +Results CSV: C:\Users\brian\Documents\nanoGPT\results\phase18b_qa_softqa_pl_w3000_runs_20260402_1640.csv + +Recipe: + batch_size=4 gradient_accumulation_steps=2 block_size=512 tokens/iter=4,096 + compile=False warmup=3000 max_iters=10000 seeds=[42, 1337, 123] + +Canon anchors to keep in mind: + cheap_qa 4.3897 (3/3 stable champion) + qa_softqa_pl w2000 4.3890 (1/3 stable, seed 42 best: 4.3790) + qa_softqa 4.3889 (1/3 stable) + qa_softq 4.3865 (1/3 stable) + +Completed runs: + 1. qa_softqa_pl_w3000 mean=4.3761 std=0.0098 done=3/3 + +---------------------------------------------------------------------------------------------------- +qa_softqa_pl_w3000 (dynamic_qa_conditioned_softqa_perlayer) + Independent per-layer learned blends for BOTH q and y before shared 128->64 gate + out_dir: out-openwebtext_phase18b_w3000_qa_softqa_pl + completed seeds: 3/3 + partial mean/std: 4.3761 ± 0.0098 + learned alpha_q layerwise mean across completed seeds: [0.4993, 0.475, 0.4235, 0.3833, 0.4758, 0.4276, 0.394, 0.4105, 0.4114, 0.4546, 0.4712, 0.478] + learned alpha_q global mean across completed seeds: 0.4420 + learned alpha_y layerwise mean across completed seeds: [0.6315, 0.5247, 0.4932, 0.464, 0.4941, 0.564, 0.5188, 0.4577, 0.4605, 0.535, 0.5496, 0.5468] + learned alpha_y global mean across completed seeds: 0.5200 + seed= 42 best=4.3747 final=4.3747 best@10000 stable=True + alpha_q=[0.5074, 0.4657, 0.4204, 0.3845, 0.4865, 0.4086, 0.3955, 0.4102, 0.3958, 0.4626, 0.4621, 0.4631] + alpha_y=[0.6308, 0.4924, 0.4931, 0.4604, 0.5081, 0.5096, 0.5197, 0.4726, 0.4664, 0.5384, 0.5515, 0.5482] + seed= 123 best=4.3888 final=4.3995 best@9750 stable=False + alpha_q=[0.4943, 0.4985, 0.4126, 0.3781, 0.4696, 0.4609, 0.3845, 0.4188, 0.4144, 0.4413, 0.4703, 0.4844] + alpha_y=[0.6345, 0.5595, 0.4973, 0.4671, 0.454, 0.5787, 0.5672, 0.4604, 0.4484, 0.5294, 0.5457, 0.5519] + seed=1337 best=4.3648 final=4.4104 best@9750 stable=False + alpha_q=[0.4962, 0.4608, 0.4374, 0.3873, 0.4712, 0.4133, 0.4019, 0.4024, 0.4241, 0.46, 0.4813, 0.4866] + alpha_y=[0.6291, 0.5223, 0.4892, 0.4645, 0.5202, 0.6036, 0.4696, 0.4402, 0.4668, 0.5373, 0.5515, 0.5402] diff --git a/results/phase19_full_scale_vectors.txt b/results/phase19_full_scale_vectors.txt new file mode 100644 index 0000000000000000000000000000000000000000..22b7e5795b1f21d331938e365cd7ccbba1100aff --- /dev/null +++ b/results/phase19_full_scale_vectors.txt @@ -0,0 +1,283 @@ +================================================================================ +PHASE 19 - FULL 64-DIM SCALE VECTOR INSPECTION v2 +================================================================================ +Loading: C:\Users\brian\Documents\nanoGPT\out-openwebtext_phase19_w2000_qa_softqa_pd\ckpt.pt +Found 12 scale_q layers, 12 scale_y layers + +================================================================================ + scale_q (qa_q_dim_scale) +================================================================================ + + Layer 0 mean=0.0790 std=0.0934 min=-0.0806 max=0.4130 [<0.2:57 mid:7 >0.8:0] BIMODAL=YES + 0.0682 0.2608 0.1676 0.0638 0.0739 -0.0019 0.2410 0.0398 + 0.1863 0.0422 0.1525 0.1270 0.1086 0.0262 0.0044 0.1661 + 0.0479 -0.0806 0.0931 0.4130 0.0258 -0.0517 -0.0157 0.2359 + -0.0188 0.0337 0.0592 0.0758 -0.0280 -0.0282 0.2401 0.1119 + 0.2303 0.1254 0.1197 0.2256 -0.0218 0.0811 -0.0601 0.1104 + -0.0722 0.0566 0.0350 0.0486 0.0919 0.0832 -0.0616 -0.0409 + -0.0040 0.1606 0.1111 0.1263 0.0920 -0.0159 0.1338 -0.0022 + 0.1347 0.0582 0.0378 0.1391 0.0300 0.0558 0.1494 0.0597 + + Layer 1 mean=0.0234 std=0.0588 min=-0.0634 max=0.1623 [<0.2:64 mid:0 >0.8:0] BIMODAL=YES + -0.0333 0.0241 -0.0369 0.0135 0.1349 -0.0171 -0.0456 0.1045 + -0.0216 0.1306 -0.0634 -0.0266 0.1308 0.0214 0.1623 -0.0274 + 0.0975 -0.0276 -0.0278 -0.0476 -0.0249 -0.0049 -0.0073 -0.0129 + 0.0994 0.0099 -0.0373 0.0193 -0.0210 -0.0306 -0.0428 0.0608 + 0.0039 -0.0049 0.0645 -0.0040 0.0828 0.1218 0.1273 0.0061 + 0.0512 0.0698 0.0489 0.1183 -0.0281 0.0553 0.0794 0.0081 + 0.0236 0.0996 -0.0196 -0.0573 -0.0557 0.1095 -0.0229 0.0175 + 0.0985 0.0105 -0.0120 -0.0171 0.0732 -0.0118 0.0008 0.0110 + + Layer 2 mean=-0.1320 std=0.1726 min=-0.5021 max=0.2549 [<0.2:62 mid:2 >0.8:0] BIMODAL=YES + -0.3513 -0.2760 0.2549 -0.0878 -0.0936 -0.0243 -0.1851 -0.3497 + -0.3123 -0.0331 -0.0396 -0.2175 -0.0830 -0.3750 -0.3468 -0.0948 + -0.1625 0.0117 -0.0463 0.1655 -0.4791 -0.1071 -0.3136 -0.0844 + -0.2461 0.1187 -0.1503 -0.3462 0.0978 -0.0501 0.1414 -0.0810 + 0.0718 0.1401 -0.1130 -0.1108 -0.0336 -0.4815 0.0341 0.2541 + -0.2072 -0.0111 -0.0003 -0.1670 -0.0930 -0.0999 -0.1449 -0.3034 + -0.0932 -0.2228 -0.0623 -0.1483 -0.2080 -0.2988 -0.5021 -0.1083 + -0.0432 -0.2733 -0.2168 -0.1029 -0.3359 -0.2430 0.1821 -0.3617 + + Layer 3 mean=-0.0732 std=0.1337 min=-0.4592 max=0.2311 [<0.2:63 mid:1 >0.8:0] BIMODAL=YES + -0.1414 0.2311 0.0879 0.1189 -0.1750 -0.1668 -0.1262 0.0771 + -0.1617 0.1263 -0.2357 0.0513 -0.1167 0.0649 -0.0202 -0.2110 + -0.0837 0.0176 -0.1082 -0.1179 0.1684 -0.3129 -0.2206 -0.1081 + -0.0380 0.1768 -0.1618 -0.0748 -0.2402 -0.1871 -0.0217 -0.2150 + -0.1476 -0.1231 -0.4592 -0.1439 -0.2512 -0.0352 -0.0854 0.1060 + 0.0097 0.0288 -0.0450 -0.1112 -0.1852 -0.1232 -0.2013 -0.0170 + -0.1852 -0.0555 -0.1016 -0.1029 0.0435 0.0373 -0.1726 -0.0049 + -0.0294 -0.0660 -0.2447 0.0335 0.1681 0.1953 -0.1690 -0.1244 + + Layer 4 mean=-0.0758 std=0.0549 min=-0.1951 max=0.0296 [<0.2:64 mid:0 >0.8:0] BIMODAL=YES + -0.1594 -0.1423 -0.1035 -0.0146 -0.0778 -0.1204 -0.1186 -0.0712 + -0.1340 -0.1489 0.0039 0.0067 -0.1268 -0.1305 -0.0309 -0.0629 + -0.0898 -0.0513 0.0086 -0.0970 -0.1735 -0.0162 -0.1408 -0.0664 + -0.0792 -0.1417 -0.0352 0.0109 -0.0784 0.0296 -0.0388 -0.0496 + -0.0861 -0.0641 -0.1951 -0.0163 -0.0409 -0.1111 -0.0832 -0.1434 + -0.0488 -0.1500 -0.1417 -0.0310 -0.1115 -0.1221 -0.0872 -0.1655 + -0.1014 0.0091 -0.0165 -0.0103 0.0246 -0.1176 -0.0834 -0.0995 + -0.1063 -0.0488 0.0099 -0.0797 -0.0763 -0.0336 -0.0231 -0.0600 + + Layer 5 mean=-0.0883 std=0.0621 min=-0.2214 max=0.0702 [<0.2:64 mid:0 >0.8:0] BIMODAL=YES + 0.0403 -0.0600 -0.0766 -0.1327 -0.1258 -0.0256 -0.1684 -0.0031 + -0.0802 -0.1019 -0.0613 -0.0280 -0.0281 -0.0607 -0.2031 0.0219 + -0.0162 -0.0506 -0.0305 -0.1155 -0.1081 -0.1724 -0.1755 -0.0694 + -0.1117 -0.1052 -0.0960 -0.0860 -0.0197 -0.1500 -0.1508 -0.1904 + -0.1177 -0.0482 -0.1126 -0.0950 0.0127 -0.1768 -0.0512 -0.0701 + -0.0723 -0.0554 -0.0913 -0.1441 -0.0865 -0.1208 -0.1136 -0.1461 + -0.0339 0.0702 -0.1967 -0.0704 -0.2214 -0.0384 -0.0417 -0.1480 + -0.0668 -0.0408 -0.1672 -0.0010 -0.1293 -0.1579 -0.1249 -0.0530 + + Layer 6 mean=-0.1225 std=0.0647 min=-0.2551 max=0.0110 [<0.2:64 mid:0 >0.8:0] BIMODAL=YES + 0.0110 -0.2412 -0.0663 -0.1510 -0.1671 -0.0141 -0.1872 -0.2182 + -0.0738 -0.0492 -0.0655 -0.1279 -0.0538 -0.1626 -0.0156 -0.2436 + -0.0761 -0.2067 -0.0994 -0.0894 -0.0789 -0.0620 -0.2551 -0.0963 + -0.1256 -0.1847 -0.1223 -0.1950 -0.1473 -0.1591 -0.0912 -0.1075 + -0.0770 -0.1664 -0.1883 -0.0800 -0.1130 -0.0938 -0.0824 -0.0654 + -0.1758 -0.1262 -0.0851 -0.1528 -0.0473 -0.0369 -0.2257 -0.2395 + -0.0781 -0.1229 -0.2157 -0.0530 -0.0456 -0.1533 -0.1333 -0.1013 + -0.1343 -0.0394 -0.1812 -0.1804 -0.1899 -0.0125 -0.1687 -0.1532 + + Layer 7 mean=-0.1207 std=0.0550 min=-0.2631 max=0.0169 [<0.2:64 mid:0 >0.8:0] BIMODAL=YES + -0.0868 -0.2631 -0.1366 -0.1293 -0.1275 -0.0922 -0.1506 -0.2360 + -0.1726 -0.0708 -0.2152 -0.1920 -0.2005 -0.1407 -0.1812 -0.1427 + -0.2005 -0.1551 -0.0932 -0.0744 -0.1557 -0.1480 -0.0815 -0.1288 + -0.1490 -0.0709 -0.2212 -0.1762 -0.0677 -0.0337 -0.1368 -0.0850 + -0.1158 -0.0797 -0.1231 -0.0585 -0.1588 -0.0811 -0.0831 -0.0612 + -0.0707 -0.1235 -0.1467 -0.0971 -0.0971 -0.1327 -0.0437 -0.1418 + -0.0042 -0.1904 0.0169 -0.1160 -0.0451 -0.0838 -0.1112 -0.1102 + -0.0984 -0.1260 -0.0967 -0.0725 -0.1307 -0.0965 -0.2263 -0.1033 + + Layer 8 mean=-0.1299 std=0.0823 min=-0.3197 max=0.0311 [<0.2:64 mid:0 >0.8:0] BIMODAL=YES + -0.1101 -0.1063 -0.1941 -0.1895 -0.2219 -0.1101 -0.0261 -0.0730 + -0.0278 -0.0684 -0.0931 -0.0687 0.0311 -0.0085 -0.0335 -0.1263 + -0.1722 -0.0713 -0.0698 -0.2524 0.0033 -0.1693 -0.2491 -0.2340 + -0.1439 -0.1118 -0.2002 -0.1873 -0.1336 -0.3197 -0.0349 -0.1110 + -0.0005 -0.1186 -0.2572 -0.1184 -0.2444 -0.1072 -0.0996 -0.2181 + -0.0658 -0.2923 0.0044 -0.1134 -0.1372 -0.1280 -0.0899 -0.0789 + -0.0920 -0.1247 -0.2863 -0.0464 -0.0319 -0.1676 -0.2257 -0.1831 + -0.0619 -0.2262 -0.1045 -0.0683 -0.2425 -0.2535 -0.1300 -0.1219 + + Layer 9 mean=-0.0680 std=0.0442 min=-0.1976 max=0.0521 [<0.2:64 mid:0 >0.8:0] BIMODAL=YES + -0.0927 -0.1177 -0.1066 -0.1195 -0.0955 -0.0429 -0.0637 -0.0977 + -0.0175 -0.1004 -0.0806 -0.1074 -0.0440 -0.0717 -0.0595 -0.0437 + 0.0046 -0.0585 -0.0042 0.0521 -0.1976 -0.0418 -0.0702 -0.1388 + -0.0523 -0.0384 -0.0208 -0.0400 -0.0156 -0.0770 -0.0477 -0.0719 + -0.0930 -0.0041 -0.0074 -0.0731 -0.1155 -0.0519 -0.1285 -0.0850 + -0.1159 -0.0840 -0.0820 -0.0385 -0.1007 -0.0212 -0.0838 -0.0208 + -0.1324 -0.0108 -0.0035 -0.0924 -0.0227 -0.0591 -0.0429 -0.0075 + -0.1193 -0.1093 -0.0837 -0.0485 -0.0996 -0.1339 -0.1066 -0.0954 + + Layer 10 mean=-0.0434 std=0.0395 min=-0.1149 max=0.0530 [<0.2:64 mid:0 >0.8:0] BIMODAL=YES + -0.0497 -0.0618 -0.0847 -0.0547 -0.0902 -0.0092 -0.0684 -0.0426 + -0.0377 -0.0591 0.0067 -0.0659 -0.0694 -0.1038 -0.0376 -0.0501 + -0.0504 -0.0093 0.0530 0.0420 -0.0117 0.0028 -0.0817 -0.1029 + -0.0689 -0.0203 -0.0645 -0.0508 -0.1006 -0.0996 0.0183 -0.0068 + -0.0411 -0.0091 -0.0148 -0.0052 -0.0600 -0.0403 -0.0641 -0.0912 + -0.0777 -0.0812 -0.0740 -0.0410 -0.0407 -0.0121 -0.0310 -0.0413 + 0.0192 -0.0125 -0.0209 0.0185 0.0419 -0.1149 -0.0283 -0.0932 + -0.0997 0.0092 -0.0797 -0.0825 -0.0783 -0.0215 -0.0219 -0.0566 + + Layer 11 mean=-0.0710 std=0.0529 min=-0.1747 max=0.0541 [<0.2:64 mid:0 >0.8:0] BIMODAL=YES + -0.0619 0.0541 -0.0643 -0.0263 -0.0555 -0.1057 -0.1385 -0.0628 + -0.0838 -0.0729 -0.0850 -0.0920 0.0027 0.0123 -0.1203 -0.1404 + -0.0379 -0.0465 -0.0351 -0.0898 -0.1636 -0.0726 -0.0641 -0.1365 + 0.0176 -0.1380 -0.0290 -0.1077 -0.0076 -0.0670 -0.1196 0.0273 + -0.0086 -0.0506 -0.0535 -0.1055 -0.0958 -0.1546 -0.1290 -0.0814 + -0.0635 -0.1363 -0.0471 -0.1719 -0.0400 -0.0192 -0.0409 -0.1747 + -0.1392 -0.0124 -0.0213 -0.1344 -0.1226 -0.0058 0.0009 -0.0711 + -0.1245 -0.0701 -0.0674 0.0001 -0.0596 -0.0337 -0.0682 -0.1305 + + --- GLOBAL scale_q (qa_q_dim_scale) across all layers --- + mean=-0.0685 std=0.1048 min=-0.5021 max=0.4130 + <0.2: 758 mid: 10 >0.8: 0 BIMODAL=YES + Distribution histogram (0.0 to 1.0 in 0.1 buckets): + 0.0-0.1 100 #################### + 0.1-0.2 38 ####### + 0.2-0.3 9 # + 0.3-0.4 0 + 0.4-0.5 1 + 0.5-0.6 0 + 0.6-0.7 0 + 0.7-0.8 0 + 0.8-0.9 0 + 0.9-1.0 0 + +================================================================================ + scale_y (qa_y_dim_scale) +================================================================================ + + Layer 0 mean=0.3035 std=0.2165 min=-0.0950 max=0.7360 [<0.2:24 mid:40 >0.8:0] BIMODAL=NO - flat + 0.3963 0.2318 0.0901 0.0252 0.7360 0.5234 0.5832 0.2723 + 0.2580 0.0317 0.2340 0.2937 0.1058 0.2797 0.5473 0.6510 + 0.2305 0.4515 0.1011 0.1684 0.4792 0.4900 0.2102 0.3133 + 0.0521 0.6712 0.4287 0.2725 0.4177 0.0067 0.6428 0.0566 + 0.0961 0.6850 0.6330 0.1438 -0.0950 0.4269 0.1495 0.3757 + 0.1831 0.0481 0.1438 0.5801 0.0748 0.2087 0.2900 0.1618 + 0.7232 0.2332 0.2148 0.3994 0.0631 0.2424 0.5871 0.1686 + 0.3753 0.6054 0.4468 0.0640 0.0276 0.1315 0.6654 0.1159 + + Layer 1 mean=0.1037 std=0.0433 min=-0.0077 max=0.2384 [<0.2:63 mid:1 >0.8:0] BIMODAL=YES + 0.0735 0.1966 0.0649 0.0597 0.1554 0.0997 0.0801 0.0948 + 0.1415 0.0953 0.1068 0.1740 0.1146 0.0824 0.1026 0.0862 + 0.0870 0.1271 0.0829 0.0883 0.1718 0.1306 0.0732 0.0847 + 0.0910 0.1065 0.1092 0.0505 0.0586 0.0522 0.1516 0.1814 + 0.0311 0.1789 0.0795 0.0849 0.1471 0.0763 0.1260 0.0881 + 0.1330 -0.0077 0.1138 0.0624 0.0650 0.0721 0.0489 0.0700 + 0.1430 0.0820 0.0631 0.1484 0.1067 0.1399 0.1110 0.1529 + 0.0647 0.0651 0.1430 0.2384 0.0630 0.1305 0.1178 0.1206 + + Layer 2 mean=0.0010 std=0.0764 min=-0.1481 max=0.1535 [<0.2:64 mid:0 >0.8:0] BIMODAL=YES + -0.1162 -0.0094 0.0357 -0.0167 -0.1329 -0.1481 0.0340 -0.1018 + -0.0253 0.0971 -0.0461 -0.0077 -0.1009 0.0669 -0.0471 0.0575 + -0.0721 -0.0003 -0.1183 0.0731 0.0956 0.1040 0.0738 0.0253 + 0.0652 -0.0717 0.0683 -0.0856 -0.0588 0.0378 0.0459 0.0602 + 0.0948 -0.0819 0.0392 0.0939 0.0870 0.0375 0.0513 0.0093 + -0.0882 -0.0151 -0.0961 0.1535 0.1420 0.0407 0.0090 0.0980 + -0.1361 -0.1244 -0.0983 0.0133 0.0423 0.0733 -0.0363 -0.0884 + -0.0953 -0.0231 0.0063 0.0868 -0.0098 -0.0007 0.0521 0.0446 + + Layer 3 mean=-0.0265 std=0.0576 min=-0.1340 max=0.0999 [<0.2:64 mid:0 >0.8:0] BIMODAL=YES + -0.0409 -0.0733 -0.0098 0.0173 0.0754 0.0806 0.0471 -0.1306 + -0.1089 0.0068 -0.0466 -0.0855 0.0164 -0.0042 0.0526 0.0172 + -0.1095 -0.0148 0.0174 -0.0356 -0.0249 -0.1267 0.0620 -0.0424 + -0.0571 -0.0663 -0.1340 -0.0438 -0.0447 -0.0816 -0.0166 0.0354 + -0.0698 -0.0761 0.0042 -0.0798 0.0810 -0.0484 0.0101 -0.0501 + 0.0024 -0.0159 0.0288 -0.0980 -0.1079 -0.1062 -0.0143 0.0582 + -0.0332 0.0215 -0.0113 0.0999 -0.0275 -0.0631 -0.0210 0.0158 + -0.0742 0.0152 -0.0955 -0.0625 -0.0060 0.0620 -0.0989 -0.0644 + + Layer 4 mean=-0.1084 std=0.1485 min=-0.4276 max=0.3985 [<0.2:62 mid:2 >0.8:0] BIMODAL=YES + -0.1584 -0.1609 -0.1542 -0.3121 0.1453 -0.2093 -0.1497 -0.1490 + -0.0618 -0.0568 0.0464 -0.1833 -0.1326 -0.0930 -0.1765 -0.0533 + -0.1648 0.0775 -0.1730 -0.1300 -0.2520 -0.0246 -0.0991 -0.0479 + -0.0917 -0.0022 -0.4276 -0.0479 -0.2760 -0.0431 -0.2438 -0.1345 + -0.1701 -0.1200 -0.0031 0.3985 -0.0210 -0.1757 -0.3817 -0.0601 + -0.1348 -0.1074 -0.0929 -0.0738 0.1781 -0.3058 0.1703 -0.0816 + -0.0487 -0.2688 -0.2700 -0.0680 -0.0529 -0.3278 -0.0646 -0.3576 + -0.2802 -0.1031 -0.0966 -0.2672 -0.0910 -0.0005 0.3336 -0.0562 + + Layer 5 mean=0.0064 std=0.1406 min=-0.3046 max=0.2671 [<0.2:57 mid:7 >0.8:0] BIMODAL=YES + -0.0467 0.0132 -0.0052 -0.0540 0.0534 -0.0757 0.1914 -0.0957 + -0.0952 0.2363 0.0818 -0.0648 0.2122 -0.1494 -0.3046 0.0503 + 0.1880 -0.1446 0.0429 0.2037 -0.0236 -0.0798 -0.1291 -0.0356 + -0.0224 -0.0662 -0.0875 -0.0273 0.1853 -0.1240 0.1433 0.1610 + 0.1258 -0.0420 -0.0834 0.1541 -0.0239 -0.2360 -0.0467 0.1554 + -0.2283 -0.1369 -0.1917 0.0818 0.0796 -0.0265 0.2671 0.2099 + 0.0764 -0.0436 -0.0928 0.2155 0.1707 -0.2804 -0.1311 0.1482 + 0.1669 0.1345 -0.0944 -0.1669 -0.1673 0.0024 0.0737 0.2066 + + Layer 6 mean=-0.0343 std=0.0756 min=-0.1499 max=0.1249 [<0.2:64 mid:0 >0.8:0] BIMODAL=YES + 0.0480 -0.1450 -0.0824 0.0285 -0.1382 -0.1258 -0.1009 -0.0710 + -0.0740 -0.0239 0.0424 -0.0689 0.0084 0.0598 -0.0514 0.0198 + -0.0368 -0.0066 -0.1397 -0.0855 -0.0059 -0.0864 -0.0722 -0.0710 + 0.0535 0.0024 -0.1240 0.1249 -0.0187 -0.0048 -0.0508 -0.0490 + -0.1121 -0.1499 0.0493 0.1240 -0.1414 -0.0602 0.0328 -0.0639 + 0.0410 -0.0784 -0.0331 -0.0917 -0.0773 0.0060 -0.1127 0.0805 + -0.0537 -0.0021 0.0279 0.0802 -0.1201 0.1197 0.0580 -0.1473 + 0.0912 0.0932 -0.0708 -0.0492 -0.0622 -0.0633 -0.1181 -0.1447 + + Layer 7 mean=-0.0703 std=0.0618 min=-0.2243 max=0.0646 [<0.2:64 mid:0 >0.8:0] BIMODAL=YES + -0.0252 -0.1124 -0.1184 -0.0823 -0.0014 -0.0559 -0.1076 -0.1430 + -0.0836 -0.0453 -0.1367 -0.0976 0.0082 -0.1380 -0.0186 -0.0412 + -0.1168 -0.0341 -0.1488 -0.1380 -0.1099 -0.0615 0.0290 -0.1475 + -0.0935 -0.0890 0.0646 -0.0196 -0.0464 -0.2234 -0.0958 -0.0018 + 0.0121 -0.0902 -0.0618 -0.1379 -0.0472 -0.1118 -0.0100 -0.0179 + -0.0070 -0.0869 -0.0296 -0.0518 -0.0571 -0.2084 0.0414 0.0114 + -0.1521 -0.1085 -0.0240 -0.0302 -0.0073 -0.0803 -0.0777 -0.1164 + -0.2243 -0.0607 -0.0117 -0.1183 0.0153 -0.1155 -0.0682 -0.0344 + + Layer 8 mean=-0.0396 std=0.1225 min=-0.5978 max=0.1549 [<0.2:64 mid:0 >0.8:0] BIMODAL=YES + -0.0695 -0.2426 -0.0824 0.0663 0.0142 -0.1065 -0.5978 0.0504 + -0.0961 -0.0112 -0.1469 -0.0792 0.0150 0.0854 -0.0045 -0.2822 + -0.2231 -0.0083 0.0213 0.0588 0.0361 -0.1143 -0.0652 -0.1117 + 0.0332 0.0366 -0.0097 -0.1263 0.0013 0.0755 -0.0732 -0.0768 + -0.0160 0.0930 0.0996 -0.0120 0.1419 0.0263 0.0870 0.0444 + -0.2048 -0.0299 0.0488 -0.0934 0.0045 0.1199 0.0161 0.0426 + -0.0892 -0.1079 -0.0973 0.0162 0.1549 0.0413 0.0965 -0.2118 + -0.0348 -0.0747 -0.0563 -0.0904 -0.0019 0.0192 -0.0938 -0.3377 + + Layer 9 mean=0.0309 std=0.0463 min=-0.1044 max=0.1232 [<0.2:64 mid:0 >0.8:0] BIMODAL=YES + 0.0195 -0.0473 0.0858 -0.0535 -0.0619 -0.0099 0.0082 0.0593 + 0.0519 -0.0424 0.0450 0.0320 0.0011 0.0970 -0.0713 0.0281 + 0.0037 0.0226 0.0689 0.0413 0.0248 0.0172 0.0497 0.0201 + 0.0600 0.0390 0.1161 0.0941 0.0134 0.0703 0.0079 0.0606 + 0.0510 0.0643 0.0520 0.0249 0.0408 0.0211 -0.1044 0.0658 + 0.0103 -0.0126 -0.0055 0.0203 0.0565 -0.0515 -0.0188 -0.0107 + 0.0362 0.0428 0.0527 0.0439 0.0671 0.1044 0.0344 -0.0101 + 0.1105 0.0286 0.1232 0.0969 0.0766 0.0097 0.0513 0.0549 + + Layer 10 mean=0.0678 std=0.0458 min=-0.0477 max=0.1759 [<0.2:64 mid:0 >0.8:0] BIMODAL=YES + -0.0043 0.0556 0.0721 0.0226 0.0442 0.0394 0.0856 -0.0477 + 0.1303 0.0231 0.0580 0.1201 0.0018 0.0286 0.0286 0.1578 + -0.0041 0.0142 0.0556 0.0870 0.0311 0.1172 0.0660 0.1117 + 0.0989 0.0370 0.0608 0.0988 0.1007 0.0391 0.0652 0.0366 + 0.1020 0.0180 0.0532 0.0311 0.0703 0.0398 0.0718 0.1188 + 0.1132 0.0609 0.0324 0.0463 0.0398 0.1653 0.0319 0.1292 + 0.1380 0.0653 0.0915 0.0725 0.1063 0.0779 0.0662 0.0543 + 0.1527 -0.0002 0.1759 0.1556 0.0414 0.0751 0.0723 0.0417 + + Layer 11 mean=0.0588 std=0.0461 min=-0.0529 max=0.1778 [<0.2:64 mid:0 >0.8:0] BIMODAL=YES + 0.0037 0.0861 0.0150 0.0113 0.0768 0.1165 0.0535 0.0769 + 0.0471 0.0142 -0.0002 0.0821 0.0774 0.0294 0.0873 0.0562 + 0.1115 0.0955 0.0497 0.0620 0.0709 0.0533 0.1123 0.1275 + 0.0499 0.0773 -0.0529 0.0265 0.0885 0.0636 0.0861 0.0128 + 0.0652 0.0462 0.0522 0.0929 -0.0444 0.0800 0.0529 0.1523 + 0.0121 0.0251 0.1232 0.0590 0.0555 0.0434 -0.0132 0.1414 + 0.0142 0.0585 0.1148 0.0766 0.1632 0.0318 -0.0052 0.0567 + 0.1778 0.0334 0.0300 0.0486 0.0913 0.0286 0.0535 -0.0212 + + --- GLOBAL scale_y (qa_y_dim_scale) across all layers --- + mean=0.0244 std=0.1459 min=-0.5978 max=0.7360 + <0.2: 718 mid: 50 >0.8: 0 BIMODAL=YES + Distribution histogram (0.0 to 1.0 in 0.1 buckets): + 0.0-0.1 299 ########################################################### + 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0.5162, 0.4951, 0.4884, 0.4444, 0.4761, 0.4995, 0.503, 0.4775, 0.4846, 0.4656, 0.4882, 0.4721, 0.4975, 0.4955, 0.4983, 0.4783, 0.4926, 0.487, 0.4857, 0.4481, 0.5103, 0.5029, 0.462, 0.4729, 0.494, 0.4924, 0.4982, 0.4799, 0.4806, 0.4709, 0.4442, 0.4848, 0.4971, 0.4705, 0.5038, 0.4712, 0.483, 0.4914], [0.4826, 0.4397, 0.4794, 0.5166, 0.5036, 0.4734, 0.3549, 0.5126, 0.476, 0.4972, 0.4633, 0.4802, 0.5038, 0.5213, 0.4989, 0.4299, 0.4445, 0.4979, 0.5053, 0.5147, 0.509, 0.4715, 0.4837, 0.4721, 0.5083, 0.5092, 0.4976, 0.4685, 0.5003, 0.5189, 0.4817, 0.4808, 0.496, 0.5232, 0.5249, 0.497, 0.5354, 0.5066, 0.5217, 0.5111, 0.449, 0.4925, 0.5122, 0.4767, 0.5011, 0.5299, 0.504, 0.5107, 0.4777, 0.473, 0.4757, 0.504, 0.5387, 0.5103, 0.5241, 0.4473, 0.4913, 0.4813, 0.4859, 0.4774, 0.4995, 0.5048, 0.4766, 0.4164], [0.5049, 0.4882, 0.5214, 0.4866, 0.4845, 0.4975, 0.502, 0.5148, 0.513, 0.4894, 0.5112, 0.508, 0.5003, 0.5242, 0.4822, 0.507, 0.5009, 0.5057, 0.5172, 0.5103, 0.5062, 0.5043, 0.5124, 0.505, 0.515, 0.5098, 0.529, 0.5235, 0.5034, 0.5176, 0.502, 0.5151, 0.5127, 0.5161, 0.513, 0.5062, 0.5102, 0.5053, 0.4739, 0.5164, 0.5026, 0.4969, 0.4986, 0.5051, 0.5141, 0.4871, 0.4953, 0.4973, 0.509, 0.5107, 0.5132, 0.511, 0.5168, 0.5261, 0.5086, 0.4975, 0.5276, 0.5072, 0.5308, 0.5242, 0.5191, 0.5024, 0.5128, 0.5137], [0.4989, 0.5139, 0.518, 0.5057, 0.511, 0.5099, 0.5214, 0.4881, 0.5325, 0.5058, 0.5145, 0.53, 0.5005, 0.5072, 0.5071, 0.5394, 0.499, 0.5036, 0.5139, 0.5217, 0.5078, 0.5293, 0.5165, 0.5279, 0.5247, 0.5093, 0.5152, 0.5247, 0.5252, 0.5098, 0.5163, 0.5091, 0.5255, 0.5045, 0.5133, 0.5078, 0.5176, 0.5099, 0.5179, 0.5297, 0.5283, 0.5152, 0.5081, 0.5116, 0.5099, 0.5412, 0.508, 0.5323, 0.5344, 0.5163, 0.5229, 0.5181, 0.5266, 0.5195, 0.5165, 0.5136, 0.5381, 0.4999, 0.5439, 0.5388, 0.5104, 0.5188, 0.5181, 0.5104], [0.5009, 0.5215, 0.5038, 0.5028, 0.5192, 0.5291, 0.5134, 0.5192, 0.5118, 0.5035, 0.4999, 0.5205, 0.5193, 0.5074, 0.5218, 0.514, 0.5278, 0.5239, 0.5124, 0.5155, 0.5177, 0.5133, 0.528, 0.5318, 0.5125, 0.5193, 0.4868, 0.5066, 0.5221, 0.5159, 0.5215, 0.5032, 0.5163, 0.5115, 0.513, 0.5232, 0.4889, 0.52, 0.5132, 0.538, 0.503, 0.5063, 0.5307, 0.5147, 0.5139, 0.5109, 0.4967, 0.5353, 0.5036, 0.5146, 0.5287, 0.5191, 0.5407, 0.5079, 0.4987, 0.5142, 0.5443, 0.5083, 0.5075, 0.5121, 0.5228, 0.5072, 0.5134, 0.4947]]",out-openwebtext_phase19_w2000_qa_softqa_pd, diff --git a/results/phase19_qa_softqa_pd_w2000_summary_20260402_2142.txt b/results/phase19_qa_softqa_pd_w2000_summary_20260402_2142.txt new file mode 100644 index 0000000000000000000000000000000000000000..c496ebbe8e23d58aec58956b6da2ec65c45196a2 --- /dev/null +++ b/results/phase19_qa_softqa_pd_w2000_summary_20260402_2142.txt @@ -0,0 +1,50 @@ +PHASE 19 - QA SOFT-QA PER-DIMENSION NORMALIZATION (INDEPENDENT PER-DIMENSION SOFT BLENDS) +==================================================================================================== + +Scientific motivation: + Phase 17 showed that per-layer scalar blend freedom was mechanistically + interesting but did not resolve instability. Phase 19 increases expressivity + by letting each of the 64 dimensions in q and y independently decide how + much normalization to apply. + +Scientific predictions: + Per-layer mean scale_q expected near 0.448 (Phase 17 global mean) + Per-layer mean scale_y expected near 0.514 (Phase 17 global mean) + Performance: potentially better than Phase 17 (more expressive) + Stability hypothesis: per-dimension freedom gives finer control + Most interesting finding: whether dimensions converge flat or become bimodal + +Results CSV: C:\Users\brian\Documents\nanoGPT\results\phase19_qa_softqa_pd_w2000_runs_20260402_2142.csv + +Recipe: + batch_size=4 gradient_accumulation_steps=2 block_size=512 tokens/iter=4,096 + compile=False warmup=2000 max_iters=10000 seeds=[42, 1337, 123] + +Canon anchors to keep in mind: + cheap_qa 4.3897 (3/3 stable champion) + qa_softqa_pl w2000 4.3890 (1/3 stable) + qa_softqa_pl seed42 4.3790 (best individual stable) + qa_softq 4.3865 (1/3 stable) + +Completed runs: + 1. qa_softqa_pd mean=4.3929 std=0.0042 done=3/3 + +---------------------------------------------------------------------------------------------------- +qa_softqa_pd (dynamic_qa_conditioned_softqa_perdim) + Independent per-dimension learned blends for BOTH q and y before shared 128->64 gate + out_dir: out-openwebtext_phase19_w2000_qa_softqa_pd + completed seeds: 3/3 + partial mean/std: 4.3929 +/- 0.0042 + learned mean scale_q layerwise mean across completed seeds: [0.515, 0.5047, 0.47, 0.4788, 0.4842, 0.4755, 0.4696, 0.4629, 0.4716, 0.4775, 0.4862, 0.4829] + learned mean scale_q global mean across completed seeds: 0.4816 + learned mean scale_y layerwise mean across completed seeds: [0.5739, 0.5217, 0.497, 0.4904, 0.4775, 0.4994, 0.494, 0.48, 0.4879, 0.5059, 0.5135, 0.5101] + learned mean scale_y global mean across completed seeds: 0.5043 + seed= 42 best=4.3883 final=4.3883 best@10000 stable=True + scale_q_means=[0.5185, 0.5041, 0.4775, 0.4809, 0.4841, 0.4807, 0.4692, 0.4557, 0.4723, 0.4707, 0.4835, 0.4796] + scale_y_means=[0.5753, 0.5178, 0.4981, 0.4881, 0.4841, 0.4913, 0.4987, 0.4796, 0.4859, 0.5076, 0.5113, 0.5033] + seed= 123 best=4.3985 final=4.4088 best@9750 stable=False + scale_q_means=[0.5197, 0.5059, 0.4673, 0.4818, 0.4811, 0.478, 0.4694, 0.4699, 0.4676, 0.483, 0.4892, 0.4823] + scale_y_means=[0.5744, 0.5259, 0.5002, 0.4934, 0.473, 0.5016, 0.4914, 0.4825, 0.4902, 0.5077, 0.5169, 0.5147] + seed=1337 best=4.3920 final=4.4384 best@9750 stable=False + scale_q_means=[0.5068, 0.5042, 0.4652, 0.4736, 0.4873, 0.4677, 0.4702, 0.463, 0.475, 0.4787, 0.4859, 0.4868] + scale_y_means=[0.5721, 0.5213, 0.4928, 0.4896, 0.4755, 0.5054, 0.492, 0.4778, 0.4875, 0.5024, 0.5122, 0.5122] diff --git a/results/phase19b_canonical_canonical_qa_softqa_pd_w2000_6seeds_summary_20260403_0709.txt b/results/phase19b_canonical_canonical_qa_softqa_pd_w2000_6seeds_summary_20260403_0709.txt new file mode 100644 index 0000000000000000000000000000000000000000..25b38fa77e21ad0bb27290f6bd9773910e70ecd4 --- /dev/null +++ b/results/phase19b_canonical_canonical_qa_softqa_pd_w2000_6seeds_summary_20260403_0709.txt @@ -0,0 +1,94 @@ +================================================================================ +PHASE 19b - QA SOFTQA PER-DIMENSION | SEEDS [42, 1337, 123, 0, 1, 2] +qv_variant : dynamic_qa_conditioned_softqa_perdim +Note : params init zeros -> sigmoid(0)=0.5 for all dims at start +================================================================================ +Anchors: cheap_qa=4.3897 (3/3 stable) Phase19-orig=4.3929 (1/3 stable) + +================================================================================ +SEED 42 -> C:\Users\brian\Documents\nanoGPT\out-openwebtext_phase19b_canonical_w2000_qa_softqa_pd_seed42 +================================================================================ +Started: 2026-04-03 07:09:18 + +Finished in 0.0 min returncode=2 +Ended: 2026-04-03 07:09:18 + + best=nan@0 final=nan@0 stable=False + WARNING: no checkpoint at C:\Users\brian\Documents\nanoGPT\out-openwebtext_phase19b_canonical_w2000_qa_softqa_pd_seed42\ckpt.pt + + [Summary updated: C:\Users\brian\Documents\nanoGPT\results\phase19b_canonical_canonical_qa_softqa_pd_w2000_6seeds_summary_20260403_0709.txt] + +================================================================================ +SEED 1337 -> C:\Users\brian\Documents\nanoGPT\out-openwebtext_phase19b_canonical_w2000_qa_softqa_pd_seed1337 +================================================================================ +Started: 2026-04-03 07:09:18 + +Finished in 0.0 min returncode=2 +Ended: 2026-04-03 07:09:18 + + best=nan@0 final=nan@0 stable=False + WARNING: no checkpoint at C:\Users\brian\Documents\nanoGPT\out-openwebtext_phase19b_canonical_w2000_qa_softqa_pd_seed1337\ckpt.pt + + [Summary updated: C:\Users\brian\Documents\nanoGPT\results\phase19b_canonical_canonical_qa_softqa_pd_w2000_6seeds_summary_20260403_0709.txt] + +================================================================================ +SEED 123 -> C:\Users\brian\Documents\nanoGPT\out-openwebtext_phase19b_canonical_w2000_qa_softqa_pd_seed123 +================================================================================ +Started: 2026-04-03 07:09:18 + +Finished in 0.0 min returncode=2 +Ended: 2026-04-03 07:09:18 + + best=nan@0 final=nan@0 stable=False + WARNING: no checkpoint at C:\Users\brian\Documents\nanoGPT\out-openwebtext_phase19b_canonical_w2000_qa_softqa_pd_seed123\ckpt.pt + + [Summary updated: C:\Users\brian\Documents\nanoGPT\results\phase19b_canonical_canonical_qa_softqa_pd_w2000_6seeds_summary_20260403_0709.txt] + +================================================================================ +SEED 0 -> C:\Users\brian\Documents\nanoGPT\out-openwebtext_phase19b_canonical_w2000_qa_softqa_pd_seed0 +================================================================================ +Started: 2026-04-03 07:09:18 + +Finished in 0.0 min returncode=2 +Ended: 2026-04-03 07:09:18 + + best=nan@0 final=nan@0 stable=False + WARNING: no checkpoint at C:\Users\brian\Documents\nanoGPT\out-openwebtext_phase19b_canonical_w2000_qa_softqa_pd_seed0\ckpt.pt + + [Summary updated: C:\Users\brian\Documents\nanoGPT\results\phase19b_canonical_canonical_qa_softqa_pd_w2000_6seeds_summary_20260403_0709.txt] + +================================================================================ +SEED 1 -> C:\Users\brian\Documents\nanoGPT\out-openwebtext_phase19b_canonical_w2000_qa_softqa_pd_seed1 +================================================================================ +Started: 2026-04-03 07:09:18 + +Finished in 0.0 min returncode=2 +Ended: 2026-04-03 07:09:18 + + best=nan@0 final=nan@0 stable=False + WARNING: no checkpoint at C:\Users\brian\Documents\nanoGPT\out-openwebtext_phase19b_canonical_w2000_qa_softqa_pd_seed1\ckpt.pt + + [Summary updated: C:\Users\brian\Documents\nanoGPT\results\phase19b_canonical_canonical_qa_softqa_pd_w2000_6seeds_summary_20260403_0709.txt] + +================================================================================ +SEED 2 -> C:\Users\brian\Documents\nanoGPT\out-openwebtext_phase19b_canonical_w2000_qa_softqa_pd_seed2 +================================================================================ +Started: 2026-04-03 07:09:18 + +Finished in 0.0 min returncode=2 +Ended: 2026-04-03 07:09:18 + + best=nan@0 final=nan@0 stable=False + WARNING: no checkpoint at C:\Users\brian\Documents\nanoGPT\out-openwebtext_phase19b_canonical_w2000_qa_softqa_pd_seed2\ckpt.pt + + [Summary updated: C:\Users\brian\Documents\nanoGPT\results\phase19b_canonical_canonical_qa_softqa_pd_w2000_6seeds_summary_20260403_0709.txt] + +================================================================================ +PHASE 19b COMPLETE +================================================================================ + seed=42 best=nan final=nan best@0 stable=False + seed=1337 best=nan final=nan best@0 stable=False + seed=123 best=nan final=nan best@0 stable=False + seed=0 best=nan final=nan best@0 stable=False + seed=1 best=nan final=nan best@0 stable=False + seed=2 best=nan final=nan best@0 stable=False \ No newline at end of file diff --git a/results/phase19b_canonical_qa_softqa_pd_w2000_6seeds_20260403_0709.csv b/results/phase19b_canonical_qa_softqa_pd_w2000_6seeds_20260403_0709.csv new file mode 100644 index 0000000000000000000000000000000000000000..ce76d95430292241b838360995cab848217332d9 --- /dev/null +++ b/results/phase19b_canonical_qa_softqa_pd_w2000_6seeds_20260403_0709.csv @@ -0,0 +1,7 @@ +seed,best_val,final_val,best_step,stable,sq_layer_means,sy_layer_means +42,nan,nan,0,False,[],[] +1337,nan,nan,0,False,[],[] +123,nan,nan,0,False,[],[] +0,nan,nan,0,False,[],[] +1,nan,nan,0,False,[],[] +2,nan,nan,0,False,[],[] diff --git a/results/phase19b_qa_softqa_pd_w2000_6seeds_20260403_0711.csv b/results/phase19b_qa_softqa_pd_w2000_6seeds_20260403_0711.csv new file mode 100644 index 0000000000000000000000000000000000000000..a224c2a63bf74f48be126e9dcefdcaa054027ecc --- /dev/null +++ b/results/phase19b_qa_softqa_pd_w2000_6seeds_20260403_0711.csv @@ -0,0 +1,7 @@ +seed,best_val,final_val,best_step,stable,sq_layer_means,sy_layer_means +42,4.3927,4.3927,10000,True,"[0.5174, 0.4997, 0.4782, 0.4707, 0.4802, 0.4831, 0.4651, 0.4638, 0.4713, 0.4708, 0.485, 0.4758]","[0.573, 0.5147, 0.5053, 0.4864, 0.4823, 0.4966, 0.4992, 0.4865, 0.4801, 0.5129, 0.5167, 0.506]" +1337,4.3847,4.4304,9750,False,"[0.5134, 0.5012, 0.4709, 0.4724, 0.4843, 0.4738, 0.4674, 0.4587, 0.4732, 0.4807, 0.4843, 0.4762]","[0.5741, 0.5222, 0.497, 0.4833, 0.4698, 0.5047, 0.4991, 0.481, 0.4826, 0.4997, 0.5043, 0.5096]" +123,4.4098,4.4183,9750,False,"[0.5202, 0.501, 0.4715, 0.471, 0.4871, 0.4772, 0.4659, 0.4727, 0.4703, 0.4822, 0.4867, 0.482]","[0.5703, 0.5165, 0.4972, 0.4908, 0.4687, 0.5067, 0.4886, 0.4904, 0.4901, 0.5041, 0.5114, 0.5084]" +0,4.4051,4.4051,10000,True,"[0.5047, 0.5033, 0.4737, 0.4833, 0.4894, 0.4778, 0.4731, 0.4706, 0.4802, 0.4761, 0.4922, 0.4869]","[0.5714, 0.5241, 0.4977, 0.4857, 0.4738, 0.4796, 0.4993, 0.4803, 0.4891, 0.4941, 0.5197, 0.5134]" +1,4.4099,4.4292,9750,False,"[0.5163, 0.5072, 0.4816, 0.4776, 0.4795, 0.4817, 0.4703, 0.4656, 0.4728, 0.4695, 0.4721, 0.4856]","[0.5732, 0.5209, 0.5071, 0.4941, 0.4891, 0.4905, 0.4899, 0.4705, 0.4846, 0.5048, 0.5083, 0.5106]" +2,4.407,4.4109,9500,False,"[0.5073, 0.5044, 0.4774, 0.4643, 0.4852, 0.4812, 0.462, 0.4713, 0.4723, 0.4735, 0.4816, 0.4771]","[0.5766, 0.5237, 0.4935, 0.4902, 0.4827, 0.4935, 0.4844, 0.4849, 0.4937, 0.5027, 0.5154, 0.5134]" diff --git a/results/phase19b_qa_softqa_pd_w2000_6seeds_summary_20260403_0711.txt b/results/phase19b_qa_softqa_pd_w2000_6seeds_summary_20260403_0711.txt new file mode 100644 index 0000000000000000000000000000000000000000..6c54a39068f1bb6143b3b89ddf34fe3f8efab12f --- /dev/null +++ b/results/phase19b_qa_softqa_pd_w2000_6seeds_summary_20260403_0711.txt @@ -0,0 +1,1608 @@ +================================================================================ +PHASE 19b - QA SOFTQA PER-DIMENSION | SEEDS [42, 1337, 123, 0, 1, 2] +qv_variant : dynamic_qa_conditioned_softqa_perdim +Note : params init zeros -> sigmoid(0)=0.5 for all dims at start +================================================================================ +Anchors: cheap_qa=4.3897 (3/3 stable) Phase19-orig=4.3929 (1/3 stable) + +================================================================================ +SEED 42 -> C:\Users\brian\Documents\nanoGPT\out-openwebtext_phase19b_w2000_qa_softqa_pd_seed42 +================================================================================ +Started: 2026-04-03 07:11:04 + +Finished in 83.9 min returncode=0 +Ended: 2026-04-03 08:34:55 + + best=4.3927@10000 final=4.3927@10000 stable=True + + Scale vectors (POST-SIGMOID | init=0.5 everywhere): + ckpt best_val_loss=4.3927 iter_num=10000 + + scale_q [0=raw 1=norm] + L 0 mean=0.5174 std=0.0231 min=0.4693 max=0.5931 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5421 0.5095 0.5144 0.5440 0.5256 0.5910 0.5229 0.5273 + 0.5173 0.4693 0.5206 0.5213 0.4947 0.5031 0.5310 0.5165 + 0.5071 0.5241 0.5257 0.5258 0.5272 0.4879 0.5192 0.5397 + 0.5166 0.5444 0.4737 0.4729 0.4944 0.5079 0.5241 0.5197 + 0.5931 0.5325 0.5372 0.4902 0.5066 0.4942 0.5262 0.4896 + 0.5135 0.5241 0.4783 0.4954 0.5046 0.5190 0.5645 0.4969 + 0.5292 0.4902 0.5179 0.5075 0.5302 0.5134 0.5027 0.4969 + 0.5198 0.5116 0.5313 0.5438 0.5137 0.5247 0.5428 0.5125 + L 1 mean=0.4997 std=0.0130 min=0.4766 max=0.5330 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4850 0.4877 0.4815 0.4996 0.4920 0.4946 0.4779 0.4824 + 0.4870 0.5044 0.5204 0.5154 0.4912 0.4884 0.4937 0.5068 + 0.5005 0.4955 0.4888 0.5077 0.4965 0.4941 0.4899 0.5089 + 0.5093 0.4932 0.4969 0.4845 0.5127 0.4889 0.5330 0.5164 + 0.4904 0.5061 0.5109 0.4977 0.4952 0.5013 0.5165 0.4942 + 0.4856 0.5207 0.5091 0.5124 0.4792 0.4884 0.4851 0.5030 + 0.5075 0.5036 0.4937 0.4949 0.5186 0.5094 0.4766 0.4945 + 0.4968 0.4938 0.5330 0.5065 0.5074 0.5069 0.4897 0.5278 + L 2 mean=0.4782 std=0.0241 min=0.4234 max=0.5614 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4728 0.4844 0.4765 0.4783 0.4782 0.4394 0.4960 0.4436 + 0.5011 0.5205 0.4565 0.5614 0.4933 0.5084 0.4833 0.5049 + 0.5054 0.4885 0.4994 0.4992 0.4495 0.5082 0.5087 0.4490 + 0.4832 0.4923 0.4928 0.4714 0.4581 0.4970 0.4989 0.4650 + 0.4405 0.4618 0.4635 0.4815 0.4989 0.4734 0.4844 0.4964 + 0.4594 0.4499 0.4805 0.4547 0.4788 0.4602 0.4823 0.4944 + 0.5207 0.4804 0.4982 0.4780 0.4526 0.4789 0.4500 0.4559 + 0.4438 0.4734 0.4234 0.4779 0.4553 0.4444 0.4814 0.4661 + L 3 mean=0.4707 std=0.0428 min=0.3881 max=0.5694 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5323 0.5424 0.4840 0.4418 0.5164 0.4630 0.4774 0.4907 + 0.4638 0.4368 0.4298 0.4743 0.5027 0.5540 0.5148 0.4087 + 0.3980 0.4439 0.4240 0.4969 0.5110 0.4507 0.5047 0.4144 + 0.5077 0.4884 0.5035 0.4149 0.4484 0.4777 0.5254 0.5694 + 0.3978 0.4576 0.4719 0.4406 0.4911 0.4617 0.4489 0.4577 + 0.5321 0.4146 0.4961 0.4566 0.4948 0.4540 0.4919 0.4248 + 0.3881 0.4167 0.4991 0.4272 0.5321 0.3964 0.4382 0.4835 + 0.5058 0.4976 0.4889 0.5252 0.5188 0.4316 0.4218 0.4499 + L 4 mean=0.4802 std=0.0148 min=0.4522 max=0.5148 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4985 0.4778 0.4707 0.4526 0.4901 0.4531 0.4742 0.4824 + 0.4920 0.4875 0.4522 0.4707 0.4978 0.4668 0.4829 0.4694 + 0.4997 0.4696 0.4581 0.5116 0.4945 0.4726 0.4885 0.4923 + 0.4736 0.4780 0.4664 0.4950 0.4865 0.4804 0.4848 0.5035 + 0.4649 0.4680 0.4930 0.4769 0.4870 0.4651 0.4674 0.5148 + 0.4750 0.4795 0.5064 0.4849 0.4634 0.4801 0.4820 0.4928 + 0.5020 0.4633 0.4913 0.4661 0.5034 0.4750 0.4698 0.4983 + 0.4653 0.4958 0.4643 0.4872 0.4716 0.4729 0.4706 0.4638 + L 5 mean=0.4831 std=0.0138 min=0.4525 max=0.5200 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4673 0.4783 0.4865 0.4689 0.4955 0.4795 0.4719 0.4958 + 0.4912 0.4940 0.4829 0.4783 0.4724 0.4815 0.4902 0.4710 + 0.4772 0.4745 0.4975 0.4888 0.5032 0.4865 0.4935 0.4677 + 0.4574 0.4705 0.4695 0.4999 0.4883 0.5122 0.4928 0.4977 + 0.4709 0.4938 0.4620 0.4703 0.4743 0.4866 0.5026 0.4561 + 0.5200 0.4930 0.4853 0.4930 0.4525 0.4899 0.4976 0.4849 + 0.4750 0.4729 0.4933 0.4750 0.4987 0.4728 0.4763 0.4814 + 0.4891 0.4576 0.4762 0.4800 0.4718 0.4832 0.5084 0.4933 + L 6 mean=0.4651 std=0.0214 min=0.4134 max=0.5101 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4743 0.4900 0.4819 0.4807 0.4942 0.4459 0.4765 0.4400 + 0.4799 0.4604 0.4947 0.4716 0.4388 0.4562 0.5101 0.4434 + 0.4714 0.4271 0.4436 0.4666 0.4635 0.4773 0.4567 0.4231 + 0.4498 0.4751 0.4562 0.4929 0.4668 0.4987 0.4532 0.4433 + 0.4596 0.4757 0.4926 0.4715 0.4988 0.4575 0.4455 0.4476 + 0.4701 0.4134 0.4649 0.4544 0.4537 0.4735 0.4991 0.4918 + 0.4721 0.4797 0.4562 0.4513 0.4235 0.4873 0.4700 0.4805 + 0.4545 0.4752 0.4310 0.4955 0.4811 0.4514 0.4336 0.4486 + L 7 mean=0.4638 std=0.0197 min=0.4112 max=0.4943 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4571 0.4129 0.4634 0.4728 0.4563 0.4894 0.4655 0.4904 + 0.4455 0.4249 0.4885 0.4812 0.4725 0.4593 0.4896 0.4671 + 0.4755 0.4736 0.4782 0.4720 0.4828 0.4701 0.4491 0.4574 + 0.4943 0.4786 0.4609 0.4276 0.4693 0.4522 0.4762 0.4723 + 0.4695 0.4765 0.4817 0.4611 0.4499 0.4868 0.4473 0.4749 + 0.4718 0.4737 0.4915 0.4527 0.4644 0.4774 0.4484 0.4388 + 0.4255 0.4680 0.4934 0.4431 0.4706 0.4378 0.4566 0.4887 + 0.4684 0.4262 0.4671 0.4112 0.4726 0.4691 0.4531 0.4380 + L 8 mean=0.4713 std=0.0173 min=0.4146 max=0.5092 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4146 0.4862 0.4919 0.4700 0.4765 0.4656 0.4567 0.4702 + 0.4550 0.4910 0.4535 0.4800 0.4881 0.4871 0.4213 0.4611 + 0.4705 0.4751 0.4601 0.4433 0.4634 0.4895 0.4823 0.4367 + 0.4706 0.4670 0.5092 0.4746 0.4642 0.4766 0.4765 0.4764 + 0.4833 0.4963 0.4636 0.4763 0.5002 0.4734 0.4528 0.4884 + 0.4709 0.4708 0.4758 0.4822 0.4485 0.4690 0.4788 0.4738 + 0.4818 0.4654 0.4368 0.4827 0.4647 0.4662 0.4525 0.4669 + 0.4693 0.4800 0.4828 0.4735 0.4627 0.4861 0.4772 0.5031 + L 9 mean=0.4708 std=0.0193 min=0.4156 max=0.5076 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4567 0.4616 0.4549 0.4835 0.4588 0.4304 0.4870 0.4422 + 0.4781 0.4646 0.4942 0.4790 0.4494 0.4666 0.4311 0.4722 + 0.4833 0.4156 0.4895 0.4975 0.4704 0.4520 0.4981 0.4943 + 0.4732 0.4586 0.4824 0.4549 0.4773 0.5019 0.4816 0.4769 + 0.4938 0.4915 0.4804 0.4187 0.4862 0.4344 0.4767 0.4690 + 0.4671 0.4452 0.4657 0.4682 0.4760 0.5076 0.4729 0.4566 + 0.4686 0.4880 0.4633 0.4734 0.4804 0.4701 0.4951 0.4824 + 0.4740 0.4687 0.4846 0.4654 0.4798 0.4806 0.4831 0.4483 + L10 mean=0.4850 std=0.0106 min=0.4640 max=0.5054 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5008 0.4905 0.4840 0.4792 0.4844 0.4960 0.4943 0.4818 + 0.4851 0.5027 0.4826 0.4945 0.4744 0.4690 0.4668 0.5000 + 0.4705 0.4811 0.4852 0.5050 0.4733 0.4885 0.4980 0.4799 + 0.5054 0.4640 0.4997 0.4962 0.4911 0.5008 0.4893 0.4752 + 0.4795 0.4810 0.4651 0.4910 0.4805 0.4875 0.4771 0.4868 + 0.4837 0.4837 0.4721 0.4945 0.5027 0.4962 0.4707 0.4751 + 0.4844 0.4799 0.4914 0.4852 0.4730 0.4740 0.4899 0.4870 + 0.4775 0.4902 0.4856 0.5027 0.4723 0.4869 0.4767 0.4699 + L11 mean=0.4758 std=0.0122 min=0.4485 max=0.5146 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4980 0.4910 0.4789 0.4782 0.4674 0.4693 0.4613 0.4690 + 0.4665 0.4913 0.4821 0.4759 0.4944 0.5043 0.4709 0.4712 + 0.4756 0.4690 0.4809 0.4724 0.4628 0.4614 0.5146 0.4806 + 0.5033 0.4636 0.4871 0.4733 0.4712 0.4794 0.4776 0.4918 + 0.4703 0.4772 0.4620 0.4793 0.4799 0.4682 0.4771 0.4668 + 0.5015 0.4781 0.4707 0.4485 0.4830 0.4655 0.4878 0.4745 + 0.4630 0.4572 0.4840 0.4672 0.4714 0.4729 0.4709 0.4647 + 0.4644 0.4766 0.4809 0.4595 0.4792 0.4729 0.4664 0.4736 + + GLOBAL mean=0.4801 std=0.0256 min=0.3881 max=0.5931 BIMODAL=NO-flat + <0.2:0 mid:768 >0.8:0 + Layer means: [0.5174, 0.4997, 0.4782, 0.4707, 0.4802, 0.4831, 0.4651, 0.4638, 0.4713, 0.4708, 0.485, 0.4758] + Histogram (sigmoid blend ratio 0.0-1.0): + 0.0-0.1 0 + 0.1-0.2 0 + 0.2-0.3 0 + 0.3-0.4 4 # + 0.4-0.5 632 ############################################################################################################################################################## + 0.5-0.6 132 ################################# + 0.6-0.7 0 + 0.7-0.8 0 + 0.8-0.9 0 + 0.9-1.0 0 + + scale_y [0=raw 1=norm] + L 0 mean=0.5730 std=0.0399 min=0.4939 max=0.6333 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5942 0.5879 0.5407 0.4995 0.5518 0.6040 0.5276 0.6021 + 0.5999 0.5835 0.5750 0.5225 0.5887 0.6155 0.5763 0.5618 + 0.5196 0.5878 0.5832 0.5536 0.6213 0.5853 0.6138 0.5835 + 0.5521 0.5169 0.5764 0.5505 0.6155 0.5320 0.5178 0.5026 + 0.6121 0.6104 0.6296 0.5793 0.5228 0.6267 0.6075 0.5743 + 0.5369 0.5135 0.6314 0.5393 0.5507 0.5223 0.5467 0.6325 + 0.6057 0.6326 0.6239 0.5015 0.5610 0.5622 0.5753 0.6129 + 0.5807 0.6304 0.5128 0.4939 0.5949 0.6333 0.5784 0.5929 + L 1 mean=0.5147 std=0.0108 min=0.4958 max=0.5422 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5029 0.4958 0.5197 0.5267 0.5142 0.5317 0.5044 0.5246 + 0.5091 0.5140 0.5107 0.5155 0.5177 0.4966 0.5048 0.5068 + 0.4991 0.5078 0.5148 0.5109 0.5071 0.5255 0.5131 0.5228 + 0.5158 0.5168 0.4959 0.5404 0.5065 0.5045 0.5379 0.5177 + 0.5102 0.5127 0.5299 0.5157 0.5109 0.5079 0.5195 0.5384 + 0.5041 0.5158 0.5242 0.5095 0.5117 0.5232 0.5320 0.5106 + 0.5101 0.5178 0.5152 0.5135 0.5285 0.5013 0.5007 0.5040 + 0.5422 0.5089 0.5244 0.5230 0.5033 0.5079 0.5114 0.5206 + L 2 mean=0.5053 std=0.0134 min=0.4821 max=0.5445 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5395 0.5018 0.5022 0.5333 0.5127 0.5069 0.5088 0.5025 + 0.5006 0.4874 0.4899 0.5103 0.4860 0.5025 0.5127 0.4874 + 0.5212 0.4944 0.5132 0.5445 0.4903 0.5023 0.5184 0.5095 + 0.5209 0.5012 0.5302 0.5084 0.4992 0.4887 0.5116 0.4887 + 0.4971 0.4893 0.5068 0.4890 0.5209 0.5054 0.5096 0.5002 + 0.4914 0.5183 0.4967 0.5145 0.5029 0.5015 0.4853 0.4896 + 0.4928 0.5153 0.5153 0.4821 0.5107 0.5116 0.5081 0.4872 + 0.5136 0.5157 0.5026 0.5027 0.5304 0.5072 0.5008 0.4988 + L 3 mean=0.4864 std=0.0132 min=0.4550 max=0.5348 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5014 0.5098 0.4909 0.4868 0.5068 0.4961 0.4768 0.5348 + 0.4879 0.4709 0.4800 0.4819 0.4758 0.4738 0.4856 0.4833 + 0.4792 0.4881 0.4783 0.4979 0.5193 0.4814 0.4835 0.4978 + 0.4824 0.4905 0.4759 0.4793 0.4910 0.4728 0.4942 0.4755 + 0.4794 0.4886 0.4870 0.4904 0.4908 0.4822 0.4865 0.4774 + 0.4885 0.4817 0.4793 0.4925 0.4856 0.4846 0.4958 0.4720 + 0.4715 0.4723 0.4995 0.4813 0.4868 0.4550 0.4859 0.4867 + 0.4969 0.4639 0.4896 0.4939 0.4641 0.5207 0.4937 0.4776 + L 4 mean=0.4823 std=0.0266 min=0.4359 max=0.5620 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4425 0.4565 0.4998 0.4727 0.4613 0.4984 0.5093 0.4655 + 0.4836 0.4496 0.5228 0.4523 0.4433 0.4925 0.4565 0.5165 + 0.4804 0.5116 0.4359 0.4849 0.4858 0.4975 0.4664 0.5620 + 0.4924 0.4918 0.5018 0.4661 0.4671 0.4978 0.4716 0.4840 + 0.4961 0.4439 0.4794 0.4878 0.4804 0.4658 0.4819 0.5315 + 0.4631 0.5162 0.4596 0.4843 0.5206 0.4637 0.4469 0.4431 + 0.4714 0.5038 0.4634 0.4527 0.4816 0.4458 0.5031 0.5322 + 0.5018 0.5016 0.4815 0.5317 0.4479 0.5073 0.4917 0.4658 + L 5 mean=0.4966 std=0.0347 min=0.4140 max=0.5780 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4443 0.5292 0.4941 0.5513 0.5113 0.4758 0.4405 0.4865 + 0.4705 0.4712 0.4803 0.5080 0.4876 0.4953 0.4644 0.5476 + 0.4911 0.4715 0.5688 0.4790 0.5051 0.4960 0.5688 0.4966 + 0.4380 0.4746 0.5780 0.5229 0.5049 0.5030 0.4886 0.5076 + 0.4367 0.4700 0.5507 0.5003 0.5138 0.4140 0.5394 0.4427 + 0.5501 0.4866 0.5206 0.4822 0.5083 0.4969 0.5187 0.4973 + 0.5039 0.5074 0.5506 0.4958 0.4910 0.4299 0.4755 0.4983 + 0.5057 0.4891 0.4823 0.5172 0.5507 0.4547 0.4648 0.4830 + L 6 mean=0.4992 std=0.0307 min=0.4389 max=0.5788 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5129 0.4904 0.4827 0.4993 0.5203 0.4887 0.5788 0.5061 + 0.4591 0.5064 0.4699 0.5722 0.5186 0.4987 0.4984 0.4721 + 0.4820 0.5313 0.5074 0.4759 0.4875 0.4747 0.4852 0.4844 + 0.4927 0.4723 0.5211 0.4655 0.4924 0.4791 0.4769 0.4903 + 0.5059 0.5165 0.5008 0.4464 0.5645 0.5534 0.5097 0.5527 + 0.5102 0.4736 0.4945 0.4667 0.4891 0.5312 0.4741 0.5237 + 0.5092 0.5094 0.5284 0.5720 0.5471 0.4583 0.5123 0.4882 + 0.5094 0.4843 0.4389 0.4565 0.4733 0.4771 0.5192 0.4557 + L 7 mean=0.4865 std=0.0212 min=0.4410 max=0.5415 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5105 0.5292 0.4669 0.5001 0.4916 0.4753 0.4755 0.4859 + 0.4961 0.4601 0.4500 0.5057 0.5184 0.4700 0.4813 0.4744 + 0.4649 0.4410 0.4676 0.4820 0.5098 0.4543 0.4630 0.4787 + 0.4820 0.4794 0.4895 0.4892 0.4774 0.4849 0.4761 0.4795 + 0.4860 0.4719 0.4809 0.4821 0.4704 0.5157 0.4906 0.4727 + 0.4901 0.4957 0.4961 0.5254 0.4768 0.4861 0.4796 0.5194 + 0.5155 0.4450 0.5294 0.5051 0.4582 0.4904 0.5225 0.4902 + 0.4884 0.4835 0.4893 0.4863 0.4601 0.5415 0.4676 0.5132 + L 8 mean=0.4801 std=0.0211 min=0.4098 max=0.5209 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4935 0.4655 0.4373 0.4643 0.4960 0.4618 0.4908 0.4571 + 0.4835 0.4935 0.5139 0.5110 0.4520 0.4795 0.4975 0.4968 + 0.4797 0.4977 0.4756 0.4899 0.4912 0.4796 0.4849 0.4656 + 0.5104 0.4837 0.4941 0.4782 0.4596 0.4722 0.4524 0.4597 + 0.4769 0.5049 0.4875 0.4831 0.4738 0.4098 0.4831 0.4604 + 0.4869 0.4896 0.4746 0.4922 0.4687 0.4764 0.4484 0.4974 + 0.4919 0.4239 0.5104 0.5077 0.4512 0.4608 0.4745 0.4780 + 0.5134 0.4922 0.5209 0.4919 0.4687 0.4810 0.5008 0.4801 + L 9 mean=0.5129 std=0.0144 min=0.4783 max=0.5486 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5486 0.5207 0.5249 0.5294 0.5025 0.5218 0.5162 0.5093 + 0.5290 0.4956 0.5375 0.5186 0.5050 0.5018 0.4996 0.4955 + 0.4855 0.4953 0.5259 0.5418 0.5029 0.5190 0.5184 0.5107 + 0.5031 0.4879 0.5243 0.5211 0.5220 0.5169 0.4991 0.5151 + 0.4817 0.5241 0.5235 0.5141 0.5051 0.4888 0.5072 0.5006 + 0.5184 0.5312 0.5271 0.4783 0.5162 0.4947 0.5295 0.5171 + 0.5048 0.5135 0.5172 0.5087 0.4909 0.5209 0.5082 0.5055 + 0.5149 0.5223 0.5297 0.5271 0.5096 0.5186 0.5078 0.5238 + L10 mean=0.5167 std=0.0084 min=0.4861 max=0.5386 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5181 0.5113 0.5220 0.5246 0.4953 0.5235 0.5198 0.5274 + 0.5273 0.5172 0.5250 0.5141 0.5039 0.5148 0.5199 0.5197 + 0.5110 0.5095 0.5094 0.5245 0.5275 0.5217 0.5165 0.5141 + 0.5182 0.5188 0.5138 0.5200 0.5188 0.5212 0.5200 0.5106 + 0.4861 0.5234 0.5207 0.5235 0.5058 0.5228 0.5023 0.5077 + 0.5214 0.5206 0.5089 0.5182 0.5109 0.5199 0.5216 0.5250 + 0.5114 0.5098 0.5094 0.5124 0.5184 0.5193 0.5190 0.5109 + 0.5007 0.5108 0.5386 0.5151 0.5267 0.5233 0.5236 0.5216 + L11 mean=0.5060 std=0.0154 min=0.4671 max=0.5398 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4934 0.4924 0.5078 0.4996 0.5043 0.5186 0.5126 0.5104 + 0.5105 0.4706 0.4971 0.4933 0.4887 0.5298 0.4671 0.5140 + 0.5169 0.4987 0.4842 0.5221 0.4873 0.5116 0.5375 0.5107 + 0.5060 0.5384 0.4859 0.5016 0.5270 0.4947 0.5105 0.5155 + 0.5269 0.4918 0.5398 0.5205 0.5106 0.4891 0.5369 0.5248 + 0.5219 0.5022 0.5058 0.5128 0.4768 0.4992 0.4935 0.5106 + 0.5057 0.4913 0.5094 0.4884 0.5003 0.4918 0.5052 0.5090 + 0.5116 0.5169 0.5095 0.4985 0.5119 0.5094 0.4987 0.5042 + + GLOBAL mean=0.5050 std=0.0331 min=0.4098 max=0.6333 BIMODAL=NO-flat + <0.2:0 mid:768 >0.8:0 + Layer means: [0.573, 0.5147, 0.5053, 0.4864, 0.4823, 0.4966, 0.4992, 0.4865, 0.4801, 0.5129, 0.5167, 0.506] + Histogram (sigmoid blend ratio 0.0-1.0): + 0.0-0.1 0 + 0.1-0.2 0 + 0.2-0.3 0 + 0.3-0.4 0 + 0.4-0.5 346 ###################################################################################### + 0.5-0.6 403 #################################################################################################### + 0.6-0.7 19 #### + 0.7-0.8 0 + 0.8-0.9 0 + 0.9-1.0 0 + + [Summary updated: C:\Users\brian\Documents\nanoGPT\results\phase19b_qa_softqa_pd_w2000_6seeds_summary_20260403_0711.txt] + +================================================================================ +SEED 1337 -> C:\Users\brian\Documents\nanoGPT\out-openwebtext_phase19b_w2000_qa_softqa_pd_seed1337 +================================================================================ +Started: 2026-04-03 08:35:22 + +Finished in 80.7 min returncode=0 +Ended: 2026-04-03 09:56:05 + + best=4.3847@9750 final=4.4304@10000 stable=False + + Scale vectors (POST-SIGMOID | init=0.5 everywhere): + ckpt best_val_loss=4.4304 iter_num=10000 + + scale_q [0=raw 1=norm] + L 0 mean=0.5134 std=0.0246 min=0.4659 max=0.5894 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4774 0.5155 0.5308 0.5204 0.5213 0.5885 0.5456 0.5128 + 0.5026 0.5227 0.5266 0.5133 0.4932 0.5001 0.4779 0.5245 + 0.5167 0.5278 0.4978 0.4689 0.5207 0.4933 0.5067 0.5450 + 0.5160 0.4785 0.5278 0.4977 0.4991 0.4994 0.5357 0.5009 + 0.5053 0.4988 0.5202 0.5352 0.5066 0.5514 0.5145 0.4701 + 0.4826 0.5219 0.5049 0.4894 0.5009 0.5431 0.5366 0.5406 + 0.5213 0.5285 0.5027 0.5168 0.5055 0.4869 0.5398 0.4704 + 0.5215 0.4836 0.4659 0.5242 0.5894 0.5213 0.5302 0.5209 + L 1 mean=0.5012 std=0.0171 min=0.4774 max=0.5493 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5253 0.4942 0.5061 0.5485 0.5130 0.5350 0.5493 0.4837 + 0.4926 0.4822 0.4849 0.4774 0.5127 0.5054 0.4999 0.4826 + 0.5204 0.4994 0.5163 0.4815 0.5023 0.4871 0.5019 0.5005 + 0.5372 0.5193 0.5115 0.4880 0.5136 0.4876 0.4842 0.4930 + 0.4968 0.5003 0.4858 0.4912 0.4959 0.4903 0.5101 0.4824 + 0.5065 0.5330 0.5296 0.4900 0.4902 0.4914 0.4873 0.4981 + 0.5021 0.4778 0.5232 0.5193 0.4948 0.4855 0.4965 0.5129 + 0.4953 0.4929 0.4995 0.4860 0.4832 0.5038 0.5077 0.4808 + L 2 mean=0.4709 std=0.0318 min=0.3996 max=0.5612 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4727 0.4051 0.4995 0.4694 0.4388 0.4382 0.5275 0.5007 + 0.4593 0.5612 0.4329 0.5113 0.4276 0.4471 0.4932 0.5082 + 0.4703 0.4934 0.4224 0.4643 0.4933 0.4350 0.4589 0.4969 + 0.4438 0.5030 0.4919 0.4187 0.4868 0.5438 0.4528 0.4602 + 0.4754 0.4370 0.4152 0.4827 0.3996 0.4474 0.4529 0.4747 + 0.4687 0.4508 0.4481 0.5171 0.4461 0.4680 0.4692 0.4687 + 0.4777 0.4549 0.4928 0.5048 0.4849 0.4998 0.5083 0.4798 + 0.4725 0.4677 0.4595 0.4520 0.4998 0.5043 0.4759 0.4555 + L 3 mean=0.4724 std=0.0415 min=0.3919 max=0.5840 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4862 0.4050 0.5438 0.4422 0.4038 0.4714 0.4310 0.4368 + 0.4751 0.4899 0.4745 0.5092 0.4669 0.4851 0.4363 0.5043 + 0.4562 0.4503 0.5260 0.4425 0.5116 0.4859 0.5140 0.4392 + 0.4865 0.4668 0.5298 0.5000 0.4400 0.4864 0.4828 0.4716 + 0.4082 0.4596 0.4417 0.4993 0.4646 0.4570 0.4671 0.4053 + 0.4016 0.4810 0.5840 0.5068 0.4987 0.5502 0.4652 0.4706 + 0.5257 0.5064 0.4113 0.4767 0.4877 0.5341 0.4706 0.3959 + 0.4324 0.4597 0.5356 0.5087 0.4232 0.3919 0.5191 0.4404 + L 4 mean=0.4843 std=0.0167 min=0.4449 max=0.5195 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4748 0.4657 0.4983 0.5025 0.4773 0.4853 0.4449 0.4703 + 0.4785 0.5046 0.5123 0.4705 0.4818 0.4781 0.4847 0.4871 + 0.4816 0.4641 0.5021 0.4972 0.5116 0.4674 0.4556 0.4704 + 0.5026 0.4878 0.4775 0.4728 0.4938 0.4732 0.4777 0.4961 + 0.4901 0.4776 0.4539 0.4937 0.5062 0.4919 0.4740 0.4754 + 0.4593 0.5046 0.5065 0.4775 0.5195 0.4971 0.5036 0.4964 + 0.4562 0.4909 0.5193 0.4835 0.4832 0.4736 0.4938 0.4635 + 0.4895 0.4764 0.4617 0.4856 0.4964 0.4598 0.5044 0.4818 + L 5 mean=0.4738 std=0.0166 min=0.4234 max=0.5066 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4707 0.4509 0.4768 0.4784 0.4818 0.4723 0.4726 0.4919 + 0.4941 0.4912 0.4738 0.4533 0.4967 0.4744 0.4831 0.4990 + 0.4741 0.4547 0.4883 0.4594 0.4784 0.4590 0.4775 0.4823 + 0.4946 0.4956 0.4818 0.4574 0.4707 0.4728 0.4734 0.4880 + 0.4948 0.4788 0.4547 0.4543 0.4348 0.4989 0.4533 0.4731 + 0.5066 0.4597 0.4686 0.4688 0.4915 0.4686 0.4651 0.4595 + 0.4590 0.4561 0.4747 0.4876 0.4539 0.4829 0.4743 0.4737 + 0.4234 0.4678 0.4459 0.4939 0.4835 0.4944 0.4908 0.4588 + L 6 mean=0.4674 std=0.0143 min=0.4339 max=0.4971 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4415 0.4841 0.4788 0.4588 0.4753 0.4551 0.4557 0.4572 + 0.4840 0.4537 0.4339 0.4760 0.4580 0.4576 0.4769 0.4374 + 0.4643 0.4481 0.4759 0.4585 0.4802 0.4767 0.4805 0.4877 + 0.4836 0.4683 0.4775 0.4819 0.4566 0.4853 0.4638 0.4555 + 0.4783 0.4714 0.4695 0.4737 0.4754 0.4548 0.4707 0.4847 + 0.4717 0.4653 0.4613 0.4638 0.4913 0.4763 0.4792 0.4805 + 0.4727 0.4539 0.4836 0.4971 0.4761 0.4749 0.4602 0.4454 + 0.4504 0.4390 0.4653 0.4393 0.4614 0.4821 0.4602 0.4533 + L 7 mean=0.4587 std=0.0139 min=0.4221 max=0.4857 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4528 0.4546 0.4842 0.4432 0.4704 0.4813 0.4632 0.4640 + 0.4535 0.4269 0.4679 0.4851 0.4614 0.4452 0.4782 0.4644 + 0.4541 0.4459 0.4857 0.4433 0.4640 0.4477 0.4567 0.4538 + 0.4695 0.4649 0.4376 0.4649 0.4704 0.4451 0.4395 0.4587 + 0.4601 0.4710 0.4605 0.4550 0.4510 0.4839 0.4221 0.4515 + 0.4549 0.4400 0.4546 0.4655 0.4770 0.4526 0.4803 0.4500 + 0.4400 0.4647 0.4268 0.4671 0.4605 0.4521 0.4753 0.4718 + 0.4591 0.4678 0.4529 0.4534 0.4588 0.4611 0.4581 0.4570 + L 8 mean=0.4732 std=0.0146 min=0.4346 max=0.4990 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4990 0.4627 0.4968 0.4868 0.4761 0.4714 0.4707 0.4543 + 0.4684 0.4534 0.4367 0.4746 0.4840 0.4439 0.4893 0.4529 + 0.4888 0.4719 0.4721 0.4773 0.4887 0.4667 0.4681 0.4739 + 0.4959 0.4692 0.4816 0.4872 0.4657 0.4760 0.4856 0.4727 + 0.4623 0.4861 0.4563 0.4907 0.4851 0.4729 0.4875 0.4824 + 0.4531 0.4581 0.4631 0.4900 0.4346 0.4595 0.4869 0.4431 + 0.4868 0.4787 0.4746 0.4637 0.4897 0.4679 0.4869 0.4786 + 0.4876 0.4829 0.4696 0.4732 0.4676 0.4789 0.4735 0.4521 + L 9 mean=0.4807 std=0.0130 min=0.4488 max=0.5112 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4562 0.4860 0.4488 0.4939 0.5001 0.4657 0.4968 0.4801 + 0.4926 0.4620 0.4906 0.4747 0.5001 0.4589 0.4923 0.4891 + 0.4774 0.4752 0.4931 0.4704 0.5112 0.4924 0.4793 0.4826 + 0.4688 0.4761 0.4723 0.4855 0.4984 0.4766 0.4828 0.4766 + 0.4767 0.4789 0.4929 0.4728 0.4976 0.4774 0.4977 0.4754 + 0.4850 0.4837 0.5033 0.4819 0.4827 0.4830 0.4711 0.5079 + 0.4711 0.4790 0.4522 0.4563 0.4675 0.4812 0.4920 0.4759 + 0.4891 0.4785 0.4759 0.4659 0.4798 0.4734 0.4753 0.4786 + L10 mean=0.4843 std=0.0144 min=0.4485 max=0.5139 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4858 0.4876 0.4997 0.4781 0.5131 0.4912 0.4654 0.4837 + 0.4955 0.4915 0.4802 0.4828 0.4776 0.4940 0.4775 0.4742 + 0.4652 0.4815 0.4790 0.4633 0.4832 0.5139 0.4487 0.4861 + 0.4946 0.4889 0.4860 0.4663 0.4822 0.4945 0.5098 0.4899 + 0.4790 0.4840 0.5013 0.4849 0.4885 0.4736 0.4914 0.4914 + 0.4847 0.4938 0.5051 0.4718 0.4995 0.4675 0.4535 0.4974 + 0.4961 0.4485 0.4860 0.5044 0.4766 0.5040 0.4911 0.4507 + 0.4907 0.4897 0.4790 0.4844 0.4902 0.4747 0.4866 0.4617 + L11 mean=0.4762 std=0.0112 min=0.4465 max=0.5092 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4741 0.4851 0.4704 0.4692 0.4690 0.4626 0.4908 0.4701 + 0.4829 0.5092 0.4858 0.4894 0.4685 0.4652 0.4712 0.4639 + 0.4622 0.4862 0.4715 0.4709 0.4731 0.4653 0.4792 0.4845 + 0.4596 0.4695 0.4611 0.4881 0.4636 0.4709 0.4776 0.4672 + 0.4755 0.4819 0.4892 0.4720 0.4721 0.4805 0.4791 0.4639 + 0.4526 0.4847 0.4677 0.4896 0.4897 0.4658 0.4465 0.4903 + 0.4704 0.4822 0.4763 0.4794 0.4856 0.4947 0.4832 0.4806 + 0.4791 0.4808 0.4780 0.4769 0.4609 0.4983 0.4857 0.4839 + + GLOBAL mean=0.4797 std=0.0254 min=0.3919 max=0.5894 BIMODAL=NO-flat + <0.2:0 mid:768 >0.8:0 + Layer means: [0.5134, 0.5012, 0.4709, 0.4724, 0.4843, 0.4738, 0.4674, 0.4587, 0.4732, 0.4807, 0.4843, 0.4762] + Histogram (sigmoid blend ratio 0.0-1.0): + 0.0-0.1 0 + 0.1-0.2 0 + 0.2-0.3 0 + 0.3-0.4 3 + 0.4-0.5 636 ############################################################################################################################################################### + 0.5-0.6 129 ################################ + 0.6-0.7 0 + 0.7-0.8 0 + 0.8-0.9 0 + 0.9-1.0 0 + + scale_y [0=raw 1=norm] + L 0 mean=0.5741 std=0.0418 min=0.5012 max=0.6341 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5472 0.5449 0.5448 0.5443 0.6161 0.6254 0.5507 0.5829 + 0.5827 0.5289 0.5259 0.6169 0.5130 0.6196 0.6100 0.5879 + 0.6230 0.6214 0.5888 0.5264 0.6072 0.5417 0.5235 0.5829 + 0.5012 0.5064 0.5890 0.6313 0.5929 0.5206 0.5747 0.6253 + 0.5897 0.5689 0.6072 0.6068 0.6242 0.5147 0.5771 0.6302 + 0.5209 0.5947 0.5601 0.6006 0.5790 0.5073 0.5131 0.6188 + 0.6103 0.6201 0.6251 0.6044 0.5092 0.5410 0.5472 0.5839 + 0.5141 0.6191 0.6016 0.5176 0.6276 0.5588 0.6341 0.5164 + L 1 mean=0.5222 std=0.0113 min=0.4927 max=0.5443 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5097 0.5186 0.5216 0.5387 0.5279 0.5143 0.5065 0.5389 + 0.5306 0.5203 0.5301 0.5221 0.5209 0.5246 0.5177 0.5176 + 0.5376 0.5132 0.5344 0.5104 0.5238 0.5315 0.5097 0.5407 + 0.5137 0.5130 0.5150 0.5345 0.5100 0.5105 0.5213 0.5093 + 0.5316 0.5172 0.4927 0.5443 0.5388 0.5057 0.5394 0.5218 + 0.5157 0.5072 0.5405 0.5016 0.5262 0.5215 0.5395 0.5380 + 0.5143 0.5105 0.5318 0.5195 0.5185 0.5248 0.5371 0.5328 + 0.5195 0.5171 0.5206 0.5283 0.5210 0.5309 0.5078 0.5181 + L 2 mean=0.4970 std=0.0207 min=0.4516 max=0.5417 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4762 0.4949 0.5005 0.4903 0.4911 0.5234 0.4648 0.5334 + 0.4944 0.4995 0.4986 0.4886 0.5110 0.5250 0.4916 0.4773 + 0.5155 0.4969 0.4951 0.5012 0.4947 0.4808 0.5119 0.5175 + 0.5176 0.4772 0.4750 0.4834 0.5244 0.4840 0.5250 0.5151 + 0.4805 0.5076 0.4764 0.4996 0.4961 0.4720 0.4792 0.4516 + 0.5182 0.4917 0.4787 0.5034 0.5222 0.4711 0.5112 0.5006 + 0.4985 0.5243 0.5417 0.4780 0.4622 0.5174 0.4580 0.5151 + 0.4582 0.5071 0.4878 0.4793 0.5130 0.5393 0.5134 0.4773 + L 3 mean=0.4833 std=0.0135 min=0.4561 max=0.5244 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5128 0.4613 0.5028 0.4790 0.4699 0.4839 0.4875 0.4869 + 0.4806 0.4670 0.4907 0.4688 0.4961 0.4773 0.4847 0.4735 + 0.4873 0.4715 0.4733 0.4851 0.4971 0.5244 0.4838 0.4792 + 0.4797 0.4811 0.4607 0.4831 0.4787 0.4869 0.4749 0.5013 + 0.4777 0.5071 0.4701 0.4595 0.4934 0.4823 0.4797 0.4758 + 0.4840 0.4836 0.4955 0.4865 0.5217 0.4674 0.4863 0.4884 + 0.4940 0.4866 0.4561 0.4661 0.4829 0.4725 0.4978 0.4889 + 0.5048 0.4756 0.4678 0.4770 0.4789 0.4817 0.4877 0.4846 + L 4 mean=0.4698 std=0.0281 min=0.3875 max=0.5403 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4907 0.4496 0.5125 0.4575 0.4683 0.4567 0.4454 0.4832 + 0.3875 0.4160 0.4130 0.4945 0.4442 0.4815 0.4817 0.4211 + 0.4494 0.4854 0.4279 0.4426 0.5033 0.4564 0.4566 0.4891 + 0.4686 0.4428 0.4601 0.4807 0.4586 0.4595 0.4874 0.4926 + 0.4651 0.4495 0.4791 0.4624 0.4347 0.4662 0.5260 0.4954 + 0.4722 0.5007 0.4811 0.5403 0.4377 0.4442 0.4783 0.5138 + 0.4612 0.4926 0.4795 0.4866 0.4510 0.4501 0.4745 0.4858 + 0.5141 0.4761 0.4524 0.4841 0.4533 0.4928 0.4974 0.5058 + L 5 mean=0.5047 std=0.0333 min=0.4435 max=0.6031 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5100 0.4937 0.4970 0.5368 0.5481 0.4857 0.4882 0.4485 + 0.4795 0.4917 0.4585 0.4922 0.4991 0.6031 0.4754 0.5250 + 0.4712 0.4784 0.5217 0.5718 0.5115 0.4928 0.5081 0.4701 + 0.4464 0.4758 0.4988 0.4898 0.4917 0.4977 0.5007 0.5149 + 0.5429 0.4974 0.5265 0.4798 0.5157 0.5175 0.4435 0.5532 + 0.5391 0.4849 0.4900 0.5463 0.4834 0.5508 0.4702 0.4591 + 0.4787 0.5229 0.5310 0.4755 0.5121 0.5284 0.4627 0.5138 + 0.5245 0.5914 0.5075 0.5779 0.5080 0.4955 0.5095 0.4880 + L 6 mean=0.4991 std=0.0206 min=0.4575 max=0.5634 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4972 0.5222 0.4940 0.5063 0.5121 0.4814 0.4855 0.4717 + 0.4663 0.4779 0.5215 0.4575 0.4905 0.5093 0.5190 0.4828 + 0.5634 0.4610 0.5129 0.5282 0.4964 0.5182 0.5448 0.5054 + 0.5211 0.4749 0.5175 0.4834 0.4764 0.4803 0.5155 0.5181 + 0.5144 0.5338 0.4888 0.5046 0.4755 0.4989 0.4991 0.4955 + 0.5150 0.5127 0.4911 0.5090 0.5187 0.4831 0.4871 0.4966 + 0.4974 0.4817 0.4832 0.4740 0.5089 0.4948 0.5075 0.5190 + 0.4914 0.4913 0.4730 0.4878 0.5303 0.5064 0.4692 0.4933 + L 7 mean=0.4810 std=0.0193 min=0.4513 max=0.5381 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4676 0.4684 0.4626 0.5293 0.4668 0.4778 0.4754 0.5204 + 0.4599 0.4513 0.5129 0.4589 0.4691 0.4603 0.5069 0.4595 + 0.5381 0.4706 0.4799 0.4976 0.4929 0.4907 0.4843 0.4731 + 0.4768 0.4560 0.4644 0.4907 0.5225 0.4709 0.4715 0.4846 + 0.4747 0.4898 0.4658 0.4744 0.4817 0.4860 0.4527 0.4971 + 0.4762 0.4721 0.4733 0.5050 0.4768 0.4713 0.5001 0.4978 + 0.4775 0.5180 0.4529 0.4836 0.4577 0.4681 0.4992 0.4608 + 0.4845 0.4782 0.4547 0.4917 0.4913 0.4860 0.4858 0.4860 + L 8 mean=0.4826 std=0.0288 min=0.4052 max=0.5339 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5053 0.4595 0.5004 0.4796 0.4570 0.5070 0.4997 0.5065 + 0.5127 0.4937 0.5024 0.4863 0.4958 0.4855 0.4669 0.4731 + 0.5339 0.4803 0.5017 0.5060 0.5047 0.4922 0.5045 0.4669 + 0.4706 0.4333 0.4185 0.4052 0.4955 0.4702 0.4604 0.4966 + 0.4904 0.4344 0.4846 0.4317 0.5211 0.4925 0.4969 0.4670 + 0.4954 0.4374 0.5007 0.5000 0.5027 0.4882 0.5049 0.4995 + 0.4102 0.4885 0.4932 0.4565 0.4659 0.4210 0.4964 0.4306 + 0.5220 0.4987 0.5131 0.5002 0.5234 0.4920 0.4798 0.4751 + L 9 mean=0.4997 std=0.0192 min=0.4376 max=0.5329 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5307 0.4844 0.5119 0.5053 0.4707 0.5058 0.4376 0.4989 + 0.4990 0.5286 0.4927 0.5004 0.5193 0.5072 0.5047 0.5090 + 0.4844 0.4650 0.4744 0.5137 0.5093 0.4844 0.4425 0.5050 + 0.4996 0.5162 0.5060 0.5329 0.4960 0.5058 0.4967 0.4930 + 0.5249 0.4587 0.5034 0.4777 0.5197 0.5089 0.5181 0.5040 + 0.5074 0.4885 0.5179 0.5148 0.4994 0.4936 0.4963 0.5164 + 0.5044 0.4977 0.4901 0.5277 0.4918 0.5018 0.5133 0.5027 + 0.5148 0.5273 0.4838 0.4959 0.4800 0.4668 0.5053 0.4971 + L10 mean=0.5043 std=0.0111 min=0.4762 max=0.5255 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5038 0.5176 0.5088 0.4928 0.5134 0.4968 0.5067 0.4931 + 0.4901 0.4968 0.5084 0.5027 0.5151 0.5056 0.4959 0.5194 + 0.5156 0.5122 0.5043 0.5137 0.4917 0.5044 0.5128 0.4996 + 0.4762 0.5146 0.5036 0.5255 0.4952 0.4952 0.5192 0.4994 + 0.4965 0.5035 0.5214 0.5103 0.5093 0.5010 0.4987 0.5207 + 0.5063 0.4996 0.5183 0.4912 0.4891 0.4853 0.5123 0.4775 + 0.4951 0.5250 0.5102 0.5112 0.5089 0.5000 0.5189 0.4842 + 0.4909 0.5005 0.5044 0.4901 0.5115 0.5104 0.5091 0.5146 + L11 mean=0.5096 std=0.0089 min=0.4912 max=0.5356 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5085 0.4932 0.4912 0.5173 0.4988 0.5093 0.5194 0.4983 + 0.5142 0.5348 0.5029 0.5054 0.5104 0.5185 0.5106 0.5107 + 0.5127 0.5033 0.5011 0.5324 0.5164 0.5120 0.4931 0.5170 + 0.5008 0.5185 0.5139 0.5356 0.5073 0.4938 0.5035 0.5068 + 0.5070 0.5115 0.5146 0.5061 0.5049 0.5116 0.5070 0.5046 + 0.5056 0.4956 0.5058 0.5156 0.5038 0.5155 0.5174 0.5102 + 0.5114 0.5130 0.5071 0.5037 0.5064 0.5110 0.5145 0.5123 + 0.5121 0.4955 0.5085 0.5097 0.5062 0.5156 0.5236 0.5127 + + GLOBAL mean=0.5023 std=0.0347 min=0.3875 max=0.6341 BIMODAL=NO-flat + <0.2:0 mid:768 >0.8:0 + Layer means: [0.5741, 0.5222, 0.497, 0.4833, 0.4698, 0.5047, 0.4991, 0.481, 0.4826, 0.4997, 0.5043, 0.5096] + Histogram (sigmoid blend ratio 0.0-1.0): + 0.0-0.1 0 + 0.1-0.2 0 + 0.2-0.3 0 + 0.3-0.4 1 + 0.4-0.5 380 ############################################################################################### + 0.5-0.6 362 ########################################################################################## + 0.6-0.7 25 ###### + 0.7-0.8 0 + 0.8-0.9 0 + 0.9-1.0 0 + + [Summary updated: C:\Users\brian\Documents\nanoGPT\results\phase19b_qa_softqa_pd_w2000_6seeds_summary_20260403_0711.txt] + +================================================================================ +SEED 123 -> C:\Users\brian\Documents\nanoGPT\out-openwebtext_phase19b_w2000_qa_softqa_pd_seed123 +================================================================================ +Started: 2026-04-03 09:56:31 + +Finished in 81.3 min returncode=0 +Ended: 2026-04-03 11:17:46 + + best=4.4098@9750 final=4.4183@10000 stable=False + + Scale vectors (POST-SIGMOID | init=0.5 everywhere): + ckpt best_val_loss=4.4183 iter_num=10000 + + scale_q [0=raw 1=norm] + L 0 mean=0.5202 std=0.0226 min=0.4775 max=0.6088 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5082 0.5378 0.5221 0.5254 0.5151 0.5111 0.5630 0.5049 + 0.5495 0.5191 0.5319 0.5330 0.5147 0.4899 0.4970 0.5496 + 0.5141 0.4927 0.5191 0.6088 0.4930 0.4943 0.4855 0.5530 + 0.4962 0.5153 0.5068 0.5244 0.5057 0.4849 0.5532 0.5297 + 0.5216 0.5147 0.5392 0.5674 0.5170 0.5265 0.4865 0.5308 + 0.4893 0.5176 0.4959 0.5368 0.5127 0.5172 0.5045 0.5124 + 0.4775 0.5091 0.5238 0.5309 0.5259 0.5079 0.5565 0.5189 + 0.5247 0.5316 0.5325 0.5327 0.5148 0.5169 0.5392 0.5115 + L 1 mean=0.5010 std=0.0116 min=0.4812 max=0.5342 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4951 0.5154 0.4918 0.4940 0.5059 0.5138 0.4926 0.4995 + 0.4901 0.5270 0.4853 0.4968 0.5211 0.4987 0.5226 0.5001 + 0.5260 0.4932 0.4887 0.4952 0.4927 0.4964 0.5031 0.4984 + 0.5030 0.4898 0.5042 0.5011 0.4843 0.4922 0.4812 0.5171 + 0.4960 0.4999 0.4990 0.4985 0.5158 0.5073 0.5073 0.5041 + 0.4972 0.5026 0.5000 0.5073 0.4931 0.5000 0.5229 0.5236 + 0.5005 0.5186 0.4882 0.4890 0.4884 0.5107 0.4948 0.4920 + 0.5342 0.4882 0.4930 0.4961 0.4999 0.4952 0.4893 0.4971 + L 2 mean=0.4715 std=0.0458 min=0.3810 max=0.5884 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4134 0.4124 0.5480 0.4997 0.4790 0.4945 0.4570 0.4162 + 0.4329 0.4818 0.4688 0.4463 0.4603 0.4064 0.4788 0.5004 + 0.4442 0.4669 0.4654 0.5147 0.3860 0.4608 0.4341 0.4760 + 0.4268 0.4895 0.4740 0.4438 0.5587 0.4931 0.5252 0.5288 + 0.5173 0.5801 0.5133 0.4492 0.4863 0.3810 0.5276 0.5884 + 0.4687 0.4567 0.4598 0.4766 0.4713 0.4969 0.5118 0.4484 + 0.4569 0.4365 0.4990 0.4389 0.4495 0.4308 0.3820 0.4330 + 0.5177 0.4356 0.4802 0.5155 0.4299 0.4443 0.5794 0.4301 + L 3 mean=0.4710 std=0.0379 min=0.3888 max=0.5631 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4405 0.5299 0.4044 0.4511 0.4512 0.4840 0.4080 0.5163 + 0.5011 0.5110 0.4235 0.4738 0.4812 0.5423 0.4583 0.4312 + 0.5631 0.4511 0.5383 0.4576 0.4673 0.4642 0.4446 0.4261 + 0.5224 0.5009 0.4590 0.4745 0.4335 0.4209 0.5138 0.4862 + 0.4811 0.4671 0.4299 0.4414 0.4841 0.4534 0.5157 0.5040 + 0.4527 0.4608 0.4868 0.4436 0.4879 0.4289 0.4589 0.4881 + 0.4319 0.4975 0.3980 0.4907 0.5279 0.4960 0.5256 0.4692 + 0.4557 0.5276 0.4281 0.4875 0.4442 0.4943 0.3888 0.4668 + L 4 mean=0.4871 std=0.0152 min=0.4571 max=0.5209 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4914 0.4968 0.4753 0.5094 0.4879 0.4771 0.5032 0.4898 + 0.4665 0.4817 0.4927 0.4922 0.4704 0.4710 0.5053 0.5016 + 0.4838 0.4842 0.5158 0.4750 0.4820 0.5145 0.4823 0.4813 + 0.4694 0.4609 0.4809 0.4877 0.4638 0.4988 0.4866 0.4928 + 0.4864 0.5022 0.4775 0.4963 0.4809 0.5036 0.4881 0.4571 + 0.4940 0.4708 0.5000 0.5122 0.4583 0.4997 0.4807 0.4947 + 0.4738 0.4936 0.4942 0.5068 0.5209 0.4608 0.4672 0.4654 + 0.4890 0.4984 0.4841 0.4916 0.5042 0.4670 0.5041 0.4771 + L 5 mean=0.4772 std=0.0180 min=0.4483 max=0.5155 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4859 0.4586 0.4960 0.4663 0.4617 0.4887 0.4491 0.4904 + 0.4569 0.4677 0.4654 0.4891 0.4618 0.4513 0.4618 0.5086 + 0.5129 0.4685 0.4647 0.4483 0.4931 0.4649 0.4574 0.4700 + 0.4848 0.4860 0.4774 0.4975 0.4933 0.4576 0.4530 0.4579 + 0.4805 0.4947 0.4965 0.5143 0.5087 0.4519 0.4907 0.4905 + 0.4645 0.5155 0.4926 0.4628 0.4627 0.4721 0.4739 0.4640 + 0.5001 0.4828 0.4634 0.4846 0.4649 0.5034 0.4798 0.4561 + 0.4839 0.4997 0.4616 0.4781 0.4830 0.4516 0.4791 0.4840 + L 6 mean=0.4659 std=0.0197 min=0.4228 max=0.5136 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4958 0.4318 0.4514 0.4298 0.4553 0.4818 0.4527 0.4386 + 0.4659 0.5136 0.4815 0.4649 0.4947 0.4533 0.4829 0.4486 + 0.4761 0.4498 0.4971 0.4804 0.4782 0.4738 0.4302 0.4805 + 0.4423 0.4493 0.4795 0.4228 0.4621 0.4567 0.4674 0.4764 + 0.4867 0.4588 0.4641 0.4601 0.4550 0.4734 0.4693 0.4952 + 0.4723 0.4649 0.4814 0.4691 0.4736 0.4733 0.4355 0.4290 + 0.4850 0.4505 0.4733 0.4765 0.4755 0.4988 0.4519 0.4847 + 0.4767 0.4875 0.4572 0.4414 0.4365 0.4780 0.4460 0.4736 + L 7 mean=0.4727 std=0.0148 min=0.4325 max=0.5081 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4853 0.4511 0.4896 0.4531 0.4843 0.4828 0.4586 0.4647 + 0.4719 0.4843 0.4417 0.4629 0.4733 0.4667 0.4627 0.4565 + 0.4764 0.4610 0.4671 0.4459 0.4759 0.4695 0.4844 0.4914 + 0.4813 0.4849 0.4325 0.4723 0.4858 0.4718 0.4440 0.4581 + 0.4786 0.4848 0.4691 0.4661 0.4429 0.4918 0.4744 0.4908 + 0.4722 0.4794 0.4819 0.5081 0.4879 0.4857 0.4966 0.4601 + 0.4780 0.4464 0.4875 0.4783 0.4711 0.4774 0.4705 0.4666 + 0.4903 0.4646 0.4874 0.4842 0.4800 0.4705 0.4652 0.4722 + L 8 mean=0.4703 std=0.0217 min=0.4280 max=0.5133 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4607 0.4814 0.4557 0.4511 0.4798 0.4773 0.4900 0.4682 + 0.4726 0.4820 0.5133 0.4775 0.5005 0.4868 0.4832 0.5094 + 0.4375 0.4888 0.4785 0.4304 0.4669 0.4642 0.4792 0.4663 + 0.4427 0.4682 0.4455 0.4495 0.4513 0.4371 0.5023 0.4730 + 0.4825 0.4819 0.4280 0.4993 0.4827 0.4961 0.4951 0.4864 + 0.4813 0.4450 0.5100 0.4648 0.4288 0.4766 0.4805 0.4776 + 0.4659 0.4744 0.4296 0.4932 0.4956 0.4441 0.4369 0.4511 + 0.4752 0.4348 0.4864 0.4778 0.4531 0.4468 0.4763 0.4675 + L 9 mean=0.4822 std=0.0143 min=0.4514 max=0.5233 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4886 0.4665 0.5024 0.4704 0.4556 0.4897 0.5035 0.4571 + 0.4842 0.5056 0.4781 0.4795 0.4946 0.4861 0.4962 0.4872 + 0.4992 0.4735 0.4852 0.4900 0.4669 0.4956 0.4713 0.4744 + 0.4848 0.5233 0.4788 0.4755 0.4961 0.5017 0.4755 0.4760 + 0.4590 0.4874 0.4758 0.4978 0.4662 0.4882 0.4674 0.4869 + 0.4905 0.4886 0.4780 0.4998 0.4592 0.4769 0.4740 0.5064 + 0.4550 0.4873 0.4993 0.4670 0.4811 0.4823 0.4882 0.4893 + 0.4743 0.4679 0.4929 0.4748 0.4514 0.4759 0.4873 0.4713 + L10 mean=0.4867 std=0.0121 min=0.4585 max=0.5124 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4892 0.4735 0.5006 0.4843 0.4585 0.4943 0.4992 0.4779 + 0.4762 0.4981 0.4897 0.4686 0.4700 0.4740 0.5020 0.4977 + 0.4674 0.4857 0.4971 0.4985 0.4765 0.4862 0.4875 0.4824 + 0.5005 0.4745 0.4783 0.5007 0.4746 0.4845 0.5056 0.4887 + 0.4880 0.5124 0.4987 0.4850 0.4739 0.5047 0.4793 0.4664 + 0.4769 0.4959 0.4824 0.4736 0.4870 0.4900 0.4692 0.4794 + 0.4921 0.4888 0.4889 0.5077 0.4965 0.4672 0.4781 0.4885 + 0.4925 0.5014 0.5038 0.4765 0.4738 0.4910 0.4926 0.5028 + L11 mean=0.4820 std=0.0162 min=0.4428 max=0.5188 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4820 0.5056 0.4973 0.4915 0.4632 0.4698 0.4795 0.4608 + 0.5000 0.4748 0.4661 0.4785 0.4881 0.5188 0.4428 0.4673 + 0.4818 0.4989 0.4729 0.4808 0.4462 0.5129 0.4861 0.4665 + 0.4796 0.4504 0.5027 0.4859 0.4871 0.4733 0.4693 0.4890 + 0.4881 0.5050 0.4759 0.4850 0.4942 0.4698 0.4854 0.5016 + 0.4903 0.4598 0.4884 0.4752 0.5048 0.5038 0.4822 0.4845 + 0.4708 0.5023 0.5014 0.4553 0.4685 0.4997 0.5045 0.4751 + 0.4636 0.4730 0.4711 0.4886 0.4777 0.4902 0.4729 0.4696 + + GLOBAL mean=0.4823 std=0.0274 min=0.3810 max=0.6088 BIMODAL=NO-flat + <0.2:0 mid:768 >0.8:0 + Layer means: [0.5202, 0.501, 0.4715, 0.471, 0.4871, 0.4772, 0.4659, 0.4727, 0.4703, 0.4822, 0.4867, 0.482] + Histogram (sigmoid blend ratio 0.0-1.0): + 0.0-0.1 0 + 0.1-0.2 0 + 0.2-0.3 0 + 0.3-0.4 5 # + 0.4-0.5 598 ##################################################################################################################################################### + 0.5-0.6 164 ######################################### + 0.6-0.7 1 + 0.7-0.8 0 + 0.8-0.9 0 + 0.9-1.0 0 + + scale_y [0=raw 1=norm] + L 0 mean=0.5703 std=0.0504 min=0.4900 max=0.6752 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5900 0.5436 0.4984 0.5051 0.6741 0.6309 0.6363 0.5521 + 0.5747 0.5357 0.5199 0.5792 0.5141 0.5534 0.6069 0.6585 + 0.5458 0.5760 0.5271 0.5472 0.6122 0.6147 0.5449 0.5665 + 0.5048 0.6596 0.6036 0.5688 0.6013 0.4916 0.6245 0.4997 + 0.5327 0.6621 0.6552 0.5190 0.4958 0.5858 0.5420 0.5855 + 0.5505 0.4900 0.5428 0.6221 0.5228 0.5520 0.5444 0.5386 + 0.6752 0.5475 0.5773 0.6083 0.5468 0.5534 0.6202 0.5764 + 0.5807 0.6697 0.6114 0.5293 0.5101 0.5464 0.6265 0.5199 + L 1 mean=0.5165 std=0.0125 min=0.4911 max=0.5617 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5231 0.5136 0.5316 0.5335 0.5174 0.5057 0.5109 0.5158 + 0.5189 0.5200 0.5154 0.5260 0.5282 0.5025 0.4911 0.5136 + 0.5015 0.5313 0.4982 0.5265 0.5101 0.5132 0.5181 0.5223 + 0.5100 0.5247 0.5308 0.5184 0.5131 0.5242 0.5246 0.5617 + 0.5111 0.5235 0.5476 0.5305 0.5340 0.5173 0.5101 0.5100 + 0.5127 0.4956 0.4918 0.5164 0.5110 0.5037 0.4980 0.5077 + 0.5192 0.5224 0.5223 0.5116 0.4952 0.5011 0.5257 0.5125 + 0.5202 0.5090 0.5145 0.5351 0.5201 0.5074 0.5109 0.5145 + L 2 mean=0.4972 std=0.0216 min=0.4519 max=0.5565 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4626 0.4750 0.5089 0.5264 0.4827 0.4673 0.5101 0.5064 + 0.4936 0.5565 0.4867 0.4951 0.4570 0.5317 0.4716 0.5169 + 0.4928 0.5073 0.4902 0.5096 0.5149 0.5179 0.4972 0.5102 + 0.4771 0.4756 0.4923 0.4970 0.4856 0.4924 0.5041 0.5318 + 0.5129 0.4937 0.5101 0.5231 0.5049 0.5249 0.5203 0.5368 + 0.4623 0.4816 0.4697 0.5129 0.4808 0.4956 0.4971 0.4781 + 0.4808 0.4519 0.4955 0.5112 0.4955 0.5081 0.5292 0.4587 + 0.4632 0.5138 0.4955 0.5028 0.4793 0.4755 0.5134 0.4979 + L 3 mean=0.4908 std=0.0185 min=0.4651 max=0.5393 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4901 0.4889 0.5151 0.5275 0.5121 0.4885 0.5393 0.4863 + 0.4910 0.4886 0.4825 0.4651 0.4755 0.5136 0.5378 0.5014 + 0.4766 0.5038 0.4843 0.4705 0.4864 0.4665 0.4824 0.4832 + 0.4696 0.5282 0.4697 0.4843 0.4890 0.4883 0.5045 0.4968 + 0.4763 0.4967 0.5147 0.4752 0.4722 0.4817 0.4727 0.5096 + 0.5029 0.4987 0.4826 0.4856 0.4709 0.5098 0.4876 0.5211 + 0.4946 0.4902 0.4898 0.4712 0.5381 0.5009 0.4761 0.4820 + 0.4700 0.4821 0.4969 0.4843 0.4816 0.4684 0.4746 0.4665 + L 4 mean=0.4687 std=0.0436 min=0.3806 max=0.5957 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4670 0.4717 0.4339 0.4178 0.5233 0.4325 0.4696 0.4315 + 0.4214 0.5158 0.4863 0.4580 0.5476 0.4659 0.3806 0.4884 + 0.4257 0.5251 0.5007 0.5487 0.4592 0.5334 0.4570 0.4386 + 0.4394 0.5957 0.4130 0.4818 0.4636 0.4895 0.4864 0.4602 + 0.4686 0.4692 0.4808 0.4995 0.5657 0.3996 0.5108 0.4143 + 0.4248 0.4569 0.4565 0.4548 0.4292 0.4360 0.4878 0.4919 + 0.5479 0.4206 0.4566 0.5259 0.4483 0.4249 0.4578 0.4402 + 0.4145 0.5084 0.4571 0.4180 0.4275 0.5293 0.4634 0.4784 + L 5 mean=0.5067 std=0.0326 min=0.4414 max=0.6053 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5451 0.4740 0.4903 0.4850 0.4620 0.5509 0.5016 0.5047 + 0.4872 0.5613 0.5423 0.4742 0.6053 0.5177 0.4677 0.5247 + 0.5805 0.5034 0.5264 0.4696 0.4754 0.4830 0.4942 0.5042 + 0.4883 0.4536 0.5275 0.4535 0.5211 0.5065 0.5392 0.5283 + 0.5127 0.5001 0.5112 0.5268 0.4800 0.5065 0.4656 0.5707 + 0.4414 0.5115 0.5036 0.4814 0.5211 0.5052 0.5918 0.5261 + 0.5049 0.4842 0.5102 0.4882 0.4798 0.4891 0.5086 0.5080 + 0.4839 0.4922 0.4863 0.5017 0.5055 0.5375 0.4896 0.5556 + L 6 mean=0.4886 std=0.0215 min=0.4383 max=0.5483 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5125 0.4602 0.4440 0.4948 0.4812 0.4638 0.4726 0.4841 + 0.5079 0.4898 0.4808 0.4969 0.5483 0.4989 0.4751 0.4811 + 0.4736 0.4583 0.5113 0.5029 0.4951 0.5180 0.4875 0.4824 + 0.5005 0.4923 0.4842 0.4882 0.4829 0.5062 0.4610 0.4769 + 0.4564 0.4919 0.4868 0.4984 0.4957 0.4854 0.5138 0.4597 + 0.5319 0.5086 0.4870 0.4535 0.4790 0.5119 0.4722 0.5248 + 0.4991 0.4628 0.4878 0.5197 0.4939 0.5215 0.4935 0.4672 + 0.5152 0.5158 0.4639 0.4383 0.4756 0.4776 0.4791 0.4890 + L 7 mean=0.4904 std=0.0189 min=0.4306 max=0.5271 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5026 0.4816 0.4740 0.4945 0.4884 0.4855 0.5094 0.4885 + 0.4945 0.4584 0.5055 0.4831 0.5202 0.5040 0.4985 0.4995 + 0.4891 0.4844 0.4710 0.4774 0.4707 0.4916 0.5271 0.5043 + 0.5095 0.4663 0.4994 0.5162 0.4709 0.4830 0.4901 0.5084 + 0.4914 0.5069 0.4306 0.4811 0.5055 0.4933 0.4704 0.4485 + 0.4898 0.5038 0.5049 0.4983 0.5129 0.5005 0.5036 0.4783 + 0.4371 0.4907 0.4874 0.4813 0.4929 0.4949 0.5191 0.5163 + 0.4863 0.4768 0.5027 0.4607 0.5134 0.4826 0.5010 0.4726 + L 8 mean=0.4901 std=0.0273 min=0.3834 max=0.5426 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4399 0.4532 0.5060 0.4906 0.4952 0.5035 0.3834 0.5084 + 0.4591 0.4344 0.5055 0.4889 0.4707 0.5310 0.5004 0.4975 + 0.4748 0.4503 0.4969 0.4860 0.4988 0.4968 0.5110 0.4656 + 0.4956 0.4891 0.4987 0.4989 0.4690 0.5021 0.5191 0.4699 + 0.5001 0.5224 0.5340 0.4721 0.5253 0.5251 0.4891 0.5043 + 0.4549 0.5009 0.4796 0.4943 0.5005 0.5350 0.4888 0.5083 + 0.4672 0.5084 0.5202 0.5012 0.5426 0.4744 0.4692 0.4462 + 0.4838 0.5190 0.5014 0.4587 0.4821 0.5090 0.4808 0.4787 + L 9 mean=0.5041 std=0.0149 min=0.4495 max=0.5324 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5113 0.5185 0.5067 0.4836 0.5054 0.4914 0.5298 0.5202 + 0.5246 0.4861 0.5015 0.5127 0.5112 0.5201 0.4799 0.4841 + 0.5200 0.5324 0.4994 0.5088 0.5137 0.4948 0.4495 0.5106 + 0.4746 0.5135 0.5324 0.5094 0.5142 0.5139 0.4828 0.5091 + 0.5069 0.5089 0.4941 0.5095 0.5034 0.4752 0.4927 0.5051 + 0.4852 0.5045 0.5105 0.4942 0.5131 0.5038 0.4902 0.5050 + 0.4917 0.5071 0.4948 0.5012 0.4976 0.5225 0.4998 0.5101 + 0.5225 0.5249 0.4927 0.5165 0.5044 0.5030 0.5021 0.5017 + L10 mean=0.5114 std=0.0108 min=0.4915 max=0.5385 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5179 0.5094 0.5157 0.4999 0.5063 0.4960 0.4970 0.5084 + 0.5248 0.4984 0.4957 0.5094 0.5118 0.4979 0.5222 0.4915 + 0.5070 0.5064 0.5079 0.5254 0.5079 0.5004 0.5162 0.5177 + 0.5323 0.5080 0.5092 0.5235 0.5205 0.4994 0.5007 0.5238 + 0.5277 0.4961 0.5046 0.5170 0.5223 0.5072 0.5147 0.5109 + 0.5197 0.5081 0.4980 0.5002 0.5073 0.5190 0.4995 0.5156 + 0.5359 0.5177 0.5132 0.5094 0.5167 0.5148 0.5049 0.4999 + 0.5264 0.5015 0.5316 0.5385 0.5134 0.5164 0.5142 0.4961 + L11 mean=0.5084 std=0.0082 min=0.4859 max=0.5264 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5050 0.4999 0.5228 0.5025 0.5086 0.4918 0.5126 0.5113 + 0.5111 0.5144 0.5092 0.5174 0.5123 0.5076 0.5050 0.5073 + 0.5120 0.5140 0.5187 0.5169 0.5156 0.5046 0.5159 0.5107 + 0.5083 0.5072 0.5010 0.4979 0.5109 0.5107 0.4970 0.5075 + 0.5159 0.5027 0.4970 0.5123 0.4964 0.5093 0.5119 0.5176 + 0.4985 0.5038 0.5093 0.4989 0.5145 0.5157 0.4954 0.5018 + 0.5021 0.5151 0.5131 0.5116 0.5209 0.5208 0.5061 0.5027 + 0.4994 0.5098 0.5064 0.4859 0.5242 0.5128 0.5264 0.4941 + + GLOBAL mean=0.5036 std=0.0355 min=0.3806 max=0.6752 BIMODAL=NO-flat + <0.2:0 mid:768 >0.8:0 + Layer means: [0.5703, 0.5165, 0.4972, 0.4908, 0.4687, 0.5067, 0.4886, 0.4904, 0.4901, 0.5041, 0.5114, 0.5084] + Histogram (sigmoid blend ratio 0.0-1.0): + 0.0-0.1 0 + 0.1-0.2 0 + 0.2-0.3 0 + 0.3-0.4 3 + 0.4-0.5 340 ##################################################################################### + 0.5-0.6 404 ##################################################################################################### + 0.6-0.7 21 ##### + 0.7-0.8 0 + 0.8-0.9 0 + 0.9-1.0 0 + + [Summary updated: C:\Users\brian\Documents\nanoGPT\results\phase19b_qa_softqa_pd_w2000_6seeds_summary_20260403_0711.txt] + +================================================================================ +SEED 0 -> C:\Users\brian\Documents\nanoGPT\out-openwebtext_phase19b_w2000_qa_softqa_pd_seed0 +================================================================================ +Started: 2026-04-03 11:18:13 + +Finished in 81.0 min returncode=0 +Ended: 2026-04-03 12:39:11 + + best=4.4051@10000 final=4.4051@10000 stable=True + + Scale vectors (POST-SIGMOID | init=0.5 everywhere): + ckpt best_val_loss=4.4051 iter_num=10000 + + scale_q [0=raw 1=norm] + L 0 mean=0.5047 std=0.0217 min=0.4587 max=0.5532 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5124 0.4836 0.4875 0.5147 0.5472 0.5107 0.4712 0.5386 + 0.4816 0.5077 0.4762 0.5404 0.4713 0.5151 0.5120 0.4865 + 0.5221 0.5081 0.5161 0.5532 0.4949 0.4949 0.5262 0.5004 + 0.4998 0.5083 0.5107 0.5045 0.5119 0.5121 0.5057 0.5200 + 0.5059 0.5029 0.4914 0.5092 0.4727 0.5319 0.4804 0.4587 + 0.5144 0.5170 0.5197 0.4823 0.5041 0.5416 0.5457 0.4821 + 0.4986 0.4812 0.5332 0.4781 0.4994 0.5341 0.4805 0.4616 + 0.5046 0.4707 0.5077 0.5059 0.5390 0.4992 0.4992 0.5000 + L 1 mean=0.5033 std=0.0094 min=0.4852 max=0.5263 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5263 0.4933 0.5091 0.5028 0.5092 0.5007 0.4968 0.5089 + 0.5160 0.4919 0.4949 0.5014 0.5002 0.5259 0.4852 0.4973 + 0.4972 0.5231 0.5117 0.4995 0.4970 0.4909 0.4983 0.4967 + 0.5020 0.5041 0.4967 0.4974 0.5038 0.5199 0.5137 0.5069 + 0.5021 0.5008 0.5015 0.5121 0.4888 0.5008 0.5131 0.5067 + 0.4980 0.4963 0.5240 0.5002 0.5228 0.4999 0.5028 0.5127 + 0.4965 0.5222 0.4990 0.4985 0.5006 0.4985 0.4987 0.5024 + 0.4916 0.4989 0.5007 0.5034 0.4954 0.5111 0.4973 0.4931 + L 2 mean=0.4737 std=0.0337 min=0.3936 max=0.5648 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4725 0.4441 0.5025 0.4973 0.5021 0.4419 0.4519 0.4970 + 0.4737 0.5045 0.4135 0.4863 0.4457 0.5002 0.4640 0.5157 + 0.4802 0.4532 0.4844 0.4656 0.4611 0.4434 0.4772 0.4382 + 0.4211 0.4688 0.5100 0.4409 0.4743 0.4487 0.4418 0.5024 + 0.5195 0.4527 0.3936 0.4789 0.4543 0.4454 0.4548 0.4007 + 0.4346 0.4895 0.4693 0.4833 0.4974 0.4662 0.5145 0.5648 + 0.4526 0.4669 0.5566 0.4568 0.5037 0.5380 0.5130 0.4475 + 0.4616 0.4683 0.5000 0.4374 0.4954 0.4749 0.4732 0.5263 + L 3 mean=0.4833 std=0.0428 min=0.4038 max=0.5883 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4839 0.4984 0.4871 0.5646 0.5017 0.4649 0.5453 0.4981 + 0.4512 0.4779 0.4647 0.5068 0.5624 0.5883 0.5259 0.4651 + 0.4974 0.4669 0.4665 0.4468 0.4038 0.5127 0.5008 0.4746 + 0.5147 0.4961 0.4471 0.4771 0.4066 0.4749 0.4928 0.4789 + 0.4541 0.4621 0.5581 0.4926 0.4837 0.4957 0.5015 0.4436 + 0.5026 0.4687 0.5329 0.4756 0.5312 0.5760 0.4155 0.4673 + 0.4378 0.4918 0.5276 0.5451 0.4140 0.4284 0.4421 0.4407 + 0.5210 0.4106 0.4124 0.4948 0.4061 0.5067 0.4764 0.4672 + L 4 mean=0.4894 std=0.0117 min=0.4638 max=0.5191 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4779 0.4978 0.5191 0.4877 0.4913 0.4963 0.4837 0.5076 + 0.4939 0.5030 0.4906 0.5036 0.4834 0.4638 0.4794 0.4747 + 0.4825 0.4881 0.4788 0.4902 0.4905 0.4849 0.4652 0.4898 + 0.4832 0.4873 0.4719 0.4850 0.4925 0.4955 0.4947 0.4839 + 0.4732 0.4922 0.4972 0.5050 0.4694 0.4963 0.4978 0.4781 + 0.4854 0.4952 0.4839 0.4936 0.4769 0.4945 0.5035 0.5066 + 0.5153 0.4856 0.5073 0.5087 0.4858 0.4951 0.4880 0.4660 + 0.4835 0.4858 0.5048 0.4892 0.4731 0.4924 0.4882 0.4877 + L 5 mean=0.4778 std=0.0166 min=0.4393 max=0.5055 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4565 0.4835 0.4745 0.4829 0.4730 0.4532 0.4535 0.4832 + 0.4797 0.4905 0.4883 0.4965 0.4686 0.5031 0.4393 0.4974 + 0.4966 0.4666 0.4693 0.5036 0.4690 0.4469 0.4674 0.4871 + 0.4473 0.4642 0.4815 0.4987 0.4730 0.4822 0.4697 0.4929 + 0.4663 0.5001 0.4830 0.4875 0.4948 0.4703 0.4600 0.4734 + 0.4727 0.4930 0.4618 0.4503 0.4619 0.4947 0.4850 0.4584 + 0.4637 0.4962 0.5028 0.4851 0.4978 0.5055 0.4647 0.4721 + 0.4770 0.4776 0.4534 0.4862 0.5010 0.5022 0.4668 0.4738 + L 6 mean=0.4731 std=0.0179 min=0.4066 max=0.5045 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4967 0.4759 0.5045 0.4920 0.4668 0.4851 0.4590 0.4719 + 0.4804 0.4614 0.4890 0.4554 0.4313 0.4639 0.4823 0.4804 + 0.4581 0.4860 0.4912 0.4762 0.4663 0.4871 0.4580 0.4815 + 0.4681 0.4822 0.4498 0.4534 0.4872 0.4720 0.4612 0.4693 + 0.4703 0.4735 0.4968 0.4066 0.4363 0.4584 0.4708 0.4673 + 0.4892 0.5015 0.4946 0.4784 0.4770 0.4735 0.4634 0.4432 + 0.4965 0.4634 0.4603 0.4791 0.4647 0.4813 0.4785 0.4600 + 0.4906 0.4537 0.4999 0.4721 0.4676 0.4765 0.4859 0.5014 + L 7 mean=0.4706 std=0.0151 min=0.4366 max=0.4948 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4568 0.4553 0.4668 0.4918 0.4468 0.4679 0.4366 0.4731 + 0.4670 0.4869 0.4684 0.4817 0.4824 0.4860 0.4855 0.4508 + 0.4891 0.4610 0.4704 0.4849 0.4860 0.4725 0.4564 0.4381 + 0.4717 0.4476 0.4842 0.4578 0.4596 0.4685 0.4368 0.4735 + 0.4616 0.4800 0.4729 0.4947 0.4742 0.4714 0.4697 0.4685 + 0.4948 0.4870 0.4456 0.4824 0.4666 0.4909 0.4835 0.4725 + 0.4757 0.4931 0.4608 0.4527 0.4698 0.4895 0.4773 0.4940 + 0.4602 0.4806 0.4572 0.4579 0.4683 0.4681 0.4464 0.4875 + L 8 mean=0.4802 std=0.0157 min=0.4467 max=0.5110 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4752 0.4692 0.4783 0.4732 0.4803 0.4763 0.4506 0.4786 + 0.4735 0.4804 0.4467 0.4521 0.4586 0.4788 0.4684 0.4760 + 0.4575 0.4902 0.4909 0.4878 0.5110 0.4700 0.4900 0.4494 + 0.4882 0.5084 0.4953 0.4850 0.4673 0.4849 0.5024 0.4738 + 0.4615 0.4836 0.4745 0.4882 0.4787 0.4962 0.4892 0.4706 + 0.4609 0.4486 0.4821 0.4761 0.4991 0.5039 0.4895 0.4677 + 0.4538 0.4966 0.4856 0.4956 0.5066 0.4696 0.4817 0.4869 + 0.4849 0.4813 0.5016 0.4739 0.4940 0.5110 0.4923 0.4762 + L 9 mean=0.4761 std=0.0136 min=0.4355 max=0.5042 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4797 0.4448 0.4764 0.4765 0.4703 0.4843 0.4771 0.4915 + 0.4568 0.4921 0.4667 0.4926 0.4843 0.4754 0.4625 0.4880 + 0.4817 0.4697 0.4697 0.4717 0.4627 0.4834 0.4783 0.5042 + 0.4866 0.4914 0.4888 0.4806 0.4747 0.5014 0.4572 0.4734 + 0.4562 0.4513 0.4615 0.4906 0.4917 0.4811 0.4829 0.4709 + 0.4861 0.4867 0.4802 0.4884 0.4830 0.4803 0.4754 0.4743 + 0.4671 0.4873 0.4825 0.4654 0.4845 0.4542 0.4726 0.4455 + 0.4355 0.4806 0.4649 0.4856 0.4874 0.4589 0.4774 0.4857 + L10 mean=0.4922 std=0.0087 min=0.4717 max=0.5095 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4961 0.4881 0.5031 0.4815 0.4939 0.4929 0.4991 0.5095 + 0.4912 0.5072 0.4959 0.5062 0.4845 0.5069 0.4955 0.5027 + 0.4893 0.4830 0.4973 0.4775 0.5068 0.4983 0.4999 0.4952 + 0.4783 0.4918 0.4898 0.4932 0.4956 0.4717 0.4945 0.4936 + 0.4938 0.4887 0.4761 0.4898 0.4963 0.5067 0.4904 0.4769 + 0.4838 0.4845 0.4823 0.4816 0.4926 0.4803 0.4818 0.5093 + 0.4857 0.5068 0.4954 0.4925 0.4964 0.4959 0.4875 0.4913 + 0.4848 0.4950 0.4825 0.4911 0.4936 0.4884 0.4968 0.4946 + L11 mean=0.4869 std=0.0152 min=0.4510 max=0.5292 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4853 0.4906 0.4872 0.4961 0.4760 0.5121 0.4947 0.4692 + 0.4648 0.4721 0.4924 0.4725 0.4995 0.4987 0.4714 0.4906 + 0.4583 0.5195 0.4544 0.5003 0.4759 0.4940 0.4763 0.4750 + 0.4772 0.5292 0.5026 0.4877 0.4858 0.4784 0.4840 0.4689 + 0.5009 0.4880 0.4898 0.4941 0.4978 0.4808 0.4852 0.4811 + 0.4510 0.4765 0.4832 0.4828 0.4872 0.4950 0.5102 0.4810 + 0.4692 0.4920 0.4994 0.5097 0.4611 0.4938 0.5136 0.4862 + 0.4795 0.4933 0.4936 0.4650 0.4985 0.4896 0.4889 0.5044 + + GLOBAL mean=0.4843 std=0.0235 min=0.3936 max=0.5883 BIMODAL=NO-flat + <0.2:0 mid:768 >0.8:0 + Layer means: [0.5047, 0.5033, 0.4737, 0.4833, 0.4894, 0.4778, 0.4731, 0.4706, 0.4802, 0.4761, 0.4922, 0.4869] + Histogram (sigmoid blend ratio 0.0-1.0): + 0.0-0.1 0 + 0.1-0.2 0 + 0.2-0.3 0 + 0.3-0.4 1 + 0.4-0.5 607 ####################################################################################################################################################### + 0.5-0.6 160 ######################################## + 0.6-0.7 0 + 0.7-0.8 0 + 0.8-0.9 0 + 0.9-1.0 0 + + scale_y [0=raw 1=norm] + L 0 mean=0.5714 std=0.0419 min=0.5021 max=0.6316 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5758 0.5805 0.6063 0.6170 0.5679 0.6256 0.6140 0.5512 + 0.5181 0.5765 0.5245 0.5994 0.5227 0.6021 0.5809 0.6027 + 0.5969 0.5939 0.6059 0.5648 0.6132 0.6152 0.5127 0.6152 + 0.5275 0.5125 0.6124 0.5624 0.6254 0.6109 0.5101 0.5129 + 0.6218 0.6170 0.5468 0.5632 0.6106 0.5524 0.6077 0.5103 + 0.5205 0.5459 0.5482 0.5577 0.5106 0.6278 0.6288 0.6069 + 0.5056 0.5021 0.6135 0.5332 0.6127 0.6074 0.5948 0.5253 + 0.6316 0.5355 0.5104 0.5316 0.6259 0.5492 0.5215 0.5345 + L 1 mean=0.5241 std=0.0113 min=0.4994 max=0.5568 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5123 0.5263 0.5431 0.5052 0.5274 0.5081 0.5275 0.5157 + 0.5333 0.5175 0.5265 0.5326 0.5320 0.5204 0.5265 0.5337 + 0.5144 0.5257 0.5304 0.5377 0.5328 0.5248 0.5234 0.5194 + 0.5266 0.5240 0.5259 0.5179 0.5268 0.4994 0.5139 0.5249 + 0.5145 0.5145 0.5369 0.5323 0.5162 0.5367 0.5177 0.5154 + 0.5132 0.5398 0.5146 0.5307 0.5483 0.5210 0.5017 0.5131 + 0.5427 0.5186 0.5230 0.5247 0.5326 0.5438 0.5303 0.5129 + 0.5210 0.5144 0.5164 0.5060 0.5568 0.5313 0.5325 0.5104 + L 2 mean=0.4977 std=0.0180 min=0.4559 max=0.5333 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4865 0.5190 0.5097 0.5026 0.4775 0.5104 0.4955 0.4836 + 0.5131 0.5028 0.4891 0.5031 0.5009 0.4880 0.5197 0.5236 + 0.4979 0.5035 0.5045 0.4791 0.4759 0.4719 0.4651 0.4984 + 0.4784 0.5032 0.5293 0.4559 0.5115 0.5333 0.4940 0.4963 + 0.5267 0.5074 0.4900 0.5024 0.5111 0.4968 0.4946 0.5147 + 0.4644 0.5109 0.4885 0.4668 0.5100 0.5014 0.5149 0.4832 + 0.5308 0.4855 0.5033 0.4811 0.4901 0.4754 0.5228 0.4821 + 0.5109 0.5127 0.4956 0.4637 0.4846 0.5240 0.5046 0.4797 + L 3 mean=0.4857 std=0.0148 min=0.4576 max=0.5328 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5043 0.4916 0.4674 0.4899 0.4982 0.4588 0.5092 0.4717 + 0.4766 0.4743 0.5060 0.5019 0.4576 0.4854 0.4737 0.4991 + 0.4910 0.5074 0.4769 0.4834 0.5085 0.5180 0.4967 0.4893 + 0.4842 0.4797 0.4932 0.5065 0.4845 0.4812 0.4877 0.4957 + 0.4785 0.4705 0.4747 0.4967 0.4827 0.5002 0.4628 0.4751 + 0.4729 0.4798 0.4803 0.4885 0.4732 0.4590 0.4783 0.4588 + 0.4778 0.4941 0.4854 0.4677 0.4963 0.4973 0.4927 0.4701 + 0.4851 0.4823 0.5328 0.4756 0.4922 0.4769 0.4821 0.4949 + L 4 mean=0.4738 std=0.0236 min=0.4254 max=0.5514 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5067 0.4572 0.4409 0.4781 0.4629 0.4865 0.4521 0.4856 + 0.4728 0.4707 0.4876 0.4719 0.4500 0.4348 0.4807 0.4777 + 0.4760 0.4637 0.4615 0.4851 0.4668 0.4805 0.4857 0.5514 + 0.4795 0.4254 0.4269 0.4851 0.4788 0.4796 0.4613 0.4800 + 0.4656 0.5036 0.4766 0.4954 0.4956 0.4578 0.4624 0.4680 + 0.5087 0.4542 0.4792 0.4754 0.4312 0.4785 0.4468 0.4650 + 0.4647 0.5032 0.4869 0.4279 0.4568 0.4933 0.4914 0.4752 + 0.5194 0.5197 0.4515 0.4888 0.4573 0.4516 0.4544 0.5114 + L 5 mean=0.4796 std=0.0398 min=0.4051 max=0.5876 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4261 0.4519 0.4393 0.5151 0.5339 0.4780 0.5214 0.4522 + 0.4754 0.4475 0.5073 0.5004 0.4233 0.4625 0.5031 0.4816 + 0.4694 0.4489 0.5586 0.4860 0.4319 0.4519 0.5002 0.4606 + 0.4908 0.5876 0.4965 0.5015 0.5096 0.4915 0.5149 0.4969 + 0.4703 0.4788 0.4225 0.4475 0.4417 0.5049 0.4936 0.4117 + 0.5139 0.4109 0.5142 0.4793 0.5401 0.4871 0.4280 0.4430 + 0.4449 0.5050 0.5708 0.4051 0.5566 0.4234 0.4932 0.4460 + 0.4993 0.5062 0.5166 0.4669 0.4512 0.4868 0.4668 0.4506 + L 6 mean=0.4993 std=0.0410 min=0.4234 max=0.6122 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4698 0.4949 0.4593 0.4588 0.5062 0.5205 0.5048 0.4707 + 0.5343 0.4982 0.5250 0.4771 0.4713 0.5017 0.4586 0.4602 + 0.4653 0.4962 0.4629 0.4994 0.5973 0.4689 0.5412 0.4763 + 0.4924 0.5206 0.5026 0.4944 0.4517 0.4807 0.5852 0.4823 + 0.4745 0.5051 0.4730 0.5109 0.6122 0.5326 0.5033 0.5130 + 0.4613 0.4612 0.6097 0.4870 0.4790 0.4624 0.4666 0.5117 + 0.4936 0.4963 0.4543 0.5093 0.5200 0.4999 0.4495 0.5438 + 0.5951 0.4830 0.6002 0.4576 0.4874 0.4234 0.5350 0.5165 + L 7 mean=0.4803 std=0.0127 min=0.4521 max=0.5176 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4697 0.4749 0.4757 0.4835 0.4718 0.4843 0.4684 0.4848 + 0.4634 0.4706 0.4879 0.4565 0.4762 0.4938 0.4846 0.4931 + 0.4601 0.4879 0.4521 0.4942 0.4714 0.4937 0.4936 0.4800 + 0.4742 0.4737 0.4977 0.4751 0.4963 0.4858 0.4815 0.4703 + 0.4796 0.4687 0.4915 0.5176 0.4826 0.4926 0.4801 0.4808 + 0.5045 0.4718 0.4902 0.4596 0.4986 0.4793 0.4663 0.4749 + 0.4710 0.4772 0.4793 0.4793 0.4637 0.4784 0.4790 0.4592 + 0.4759 0.4642 0.4988 0.4889 0.5045 0.4798 0.4934 0.4799 + L 8 mean=0.4891 std=0.0155 min=0.4482 max=0.5248 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4933 0.4787 0.4919 0.4808 0.5014 0.5066 0.5134 0.5194 + 0.5117 0.4994 0.4818 0.4700 0.4600 0.5055 0.5030 0.4988 + 0.4663 0.5027 0.5001 0.4886 0.4926 0.5248 0.4740 0.4830 + 0.4814 0.4843 0.4898 0.4995 0.5000 0.4865 0.4799 0.4963 + 0.4866 0.4938 0.4875 0.4650 0.5084 0.4744 0.4838 0.4884 + 0.4771 0.4514 0.4994 0.4929 0.4758 0.4828 0.5022 0.4734 + 0.5042 0.4690 0.4925 0.4768 0.5123 0.4801 0.4813 0.4875 + 0.4875 0.5152 0.4727 0.4975 0.4960 0.4769 0.4937 0.4482 + L 9 mean=0.4941 std=0.0229 min=0.4188 max=0.5347 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4923 0.4835 0.5017 0.4188 0.5155 0.4691 0.4740 0.5066 + 0.5108 0.4360 0.4780 0.4931 0.5081 0.4915 0.5083 0.5205 + 0.5112 0.4732 0.5004 0.5009 0.4746 0.4869 0.4929 0.5201 + 0.4728 0.4884 0.5034 0.5118 0.4715 0.4953 0.5085 0.4727 + 0.4892 0.4554 0.5102 0.5130 0.5092 0.4468 0.5232 0.5244 + 0.4969 0.5021 0.4580 0.5317 0.4526 0.5005 0.4960 0.4799 + 0.5114 0.5032 0.4697 0.4947 0.4837 0.5271 0.4776 0.5166 + 0.5076 0.5101 0.5015 0.4827 0.4932 0.5149 0.5148 0.5347 + L10 mean=0.5197 std=0.0098 min=0.5001 max=0.5518 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5305 0.5133 0.5162 0.5054 0.5267 0.5138 0.5088 0.5184 + 0.5255 0.5177 0.5133 0.5365 0.5170 0.5055 0.5179 0.5518 + 0.5258 0.5323 0.5193 0.5279 0.5227 0.5296 0.5042 0.5154 + 0.5296 0.5072 0.5166 0.5086 0.5234 0.5123 0.5266 0.5287 + 0.5118 0.5148 0.5051 0.5266 0.5124 0.5221 0.5060 0.5344 + 0.5132 0.5264 0.5369 0.5173 0.5232 0.5294 0.5203 0.5313 + 0.5001 0.5241 0.5227 0.5233 0.5149 0.5208 0.5287 0.5199 + 0.5085 0.5275 0.5084 0.5197 0.5253 0.5045 0.5091 0.5269 + L11 mean=0.5134 std=0.0087 min=0.4920 max=0.5304 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5234 0.5162 0.5219 0.5137 0.5188 0.5168 0.5131 0.5146 + 0.5092 0.5026 0.5273 0.5103 0.5153 0.5007 0.4920 0.5124 + 0.5230 0.5296 0.5089 0.4920 0.5142 0.5117 0.5149 0.5283 + 0.5096 0.5201 0.5120 0.5226 0.5148 0.5078 0.5124 0.5159 + 0.5072 0.5147 0.4925 0.5009 0.5140 0.5167 0.5152 0.5140 + 0.5145 0.4987 0.5102 0.5242 0.5091 0.5236 0.5175 0.5073 + 0.5036 0.5092 0.5131 0.5117 0.5239 0.5218 0.5151 0.4999 + 0.5172 0.5221 0.5060 0.5304 0.5042 0.5169 0.5110 0.5277 + + GLOBAL mean=0.5023 std=0.0358 min=0.4051 max=0.6316 BIMODAL=NO-flat + <0.2:0 mid:768 >0.8:0 + Layer means: [0.5714, 0.5241, 0.4977, 0.4857, 0.4738, 0.4796, 0.4993, 0.4803, 0.4891, 0.4941, 0.5197, 0.5134] + Histogram (sigmoid blend ratio 0.0-1.0): + 0.0-0.1 0 + 0.1-0.2 0 + 0.2-0.3 0 + 0.3-0.4 0 + 0.4-0.5 371 ############################################################################################ + 0.5-0.6 369 ############################################################################################ + 0.6-0.7 28 ####### + 0.7-0.8 0 + 0.8-0.9 0 + 0.9-1.0 0 + + [Summary updated: C:\Users\brian\Documents\nanoGPT\results\phase19b_qa_softqa_pd_w2000_6seeds_summary_20260403_0711.txt] + +================================================================================ +SEED 1 -> C:\Users\brian\Documents\nanoGPT\out-openwebtext_phase19b_w2000_qa_softqa_pd_seed1 +================================================================================ +Started: 2026-04-03 12:39:37 + +Finished in 80.9 min returncode=0 +Ended: 2026-04-03 14:00:32 + + best=4.4099@9750 final=4.4292@10000 stable=False + + Scale vectors (POST-SIGMOID | init=0.5 everywhere): + ckpt best_val_loss=4.4292 iter_num=10000 + + scale_q [0=raw 1=norm] + L 0 mean=0.5163 std=0.0199 min=0.4619 max=0.5808 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5287 0.5261 0.5064 0.5133 0.5536 0.4970 0.5258 0.4958 + 0.5172 0.5120 0.5177 0.5311 0.5219 0.5408 0.4875 0.5222 + 0.4858 0.5246 0.5608 0.5361 0.5276 0.4965 0.5058 0.4619 + 0.5024 0.5170 0.5176 0.5015 0.4933 0.5183 0.5002 0.5176 + 0.5055 0.5254 0.5159 0.5232 0.5366 0.5069 0.4781 0.5179 + 0.5095 0.5233 0.5355 0.5152 0.5248 0.5572 0.5808 0.5419 + 0.5302 0.4868 0.5117 0.5260 0.4932 0.5003 0.5097 0.5159 + 0.5355 0.5170 0.5059 0.5338 0.5090 0.4993 0.5074 0.4997 + L 1 mean=0.5072 std=0.0192 min=0.4788 max=0.5515 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4938 0.4857 0.5007 0.4796 0.4844 0.5484 0.5414 0.4934 + 0.5515 0.5418 0.5271 0.4869 0.5143 0.4911 0.4890 0.5425 + 0.5099 0.5013 0.5437 0.5066 0.5402 0.5202 0.5336 0.4964 + 0.5282 0.4926 0.4963 0.4967 0.4932 0.4909 0.4855 0.4933 + 0.4938 0.4842 0.4921 0.5082 0.5245 0.5174 0.5014 0.5307 + 0.5364 0.5120 0.4936 0.4927 0.5085 0.5187 0.4993 0.5049 + 0.4918 0.4992 0.5293 0.4951 0.4970 0.5129 0.5199 0.4952 + 0.5049 0.4892 0.4908 0.5001 0.5155 0.4788 0.4889 0.5309 + L 2 mean=0.4816 std=0.0233 min=0.4198 max=0.5364 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4434 0.4605 0.5364 0.4788 0.5011 0.5039 0.4833 0.4916 + 0.4749 0.5124 0.4651 0.4834 0.4841 0.5088 0.4670 0.4858 + 0.4980 0.4906 0.4948 0.4536 0.4881 0.4957 0.4649 0.4529 + 0.4914 0.5070 0.4781 0.5090 0.5024 0.4540 0.4654 0.4654 + 0.4985 0.4491 0.4985 0.5200 0.4625 0.4607 0.4616 0.4801 + 0.4553 0.4866 0.4631 0.4852 0.4596 0.4669 0.4790 0.4435 + 0.4974 0.5113 0.5048 0.4679 0.4887 0.5233 0.4949 0.4198 + 0.5009 0.4896 0.4526 0.4482 0.4984 0.5084 0.5104 0.4463 + L 3 mean=0.4776 std=0.0310 min=0.4171 max=0.5521 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4861 0.4559 0.4721 0.4432 0.4426 0.5049 0.5013 0.4365 + 0.4464 0.4757 0.4417 0.4832 0.4373 0.4511 0.4359 0.5251 + 0.5028 0.4583 0.4804 0.4515 0.4604 0.4213 0.5444 0.4490 + 0.5038 0.4205 0.5244 0.5117 0.4729 0.5133 0.4828 0.4618 + 0.4792 0.5004 0.4253 0.4722 0.4875 0.4617 0.5080 0.5171 + 0.4228 0.4171 0.5002 0.4604 0.4804 0.5183 0.4851 0.4640 + 0.5133 0.4894 0.5055 0.4946 0.5521 0.4712 0.4618 0.5063 + 0.5091 0.4964 0.4750 0.4749 0.4790 0.4466 0.5017 0.4907 + L 4 mean=0.4795 std=0.0400 min=0.3893 max=0.5598 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4458 0.4895 0.4629 0.5420 0.5546 0.4940 0.4655 0.5263 + 0.4423 0.5339 0.4727 0.4563 0.3893 0.4194 0.5157 0.4524 + 0.4916 0.4850 0.4450 0.4906 0.4805 0.4953 0.4537 0.4872 + 0.4908 0.4907 0.4924 0.4845 0.4016 0.4756 0.4847 0.4144 + 0.4851 0.4922 0.5048 0.4416 0.5239 0.5304 0.4039 0.4637 + 0.4825 0.3968 0.4236 0.4896 0.5056 0.5223 0.5162 0.5197 + 0.5591 0.5045 0.4446 0.5598 0.4942 0.4824 0.5067 0.5066 + 0.4116 0.5036 0.4585 0.4203 0.5131 0.4620 0.4907 0.4438 + L 5 mean=0.4817 std=0.0159 min=0.4456 max=0.5138 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4791 0.4743 0.4841 0.4865 0.4632 0.4846 0.4814 0.4851 + 0.5085 0.4601 0.4873 0.4926 0.5062 0.4644 0.4754 0.4926 + 0.4717 0.4941 0.4807 0.4728 0.4762 0.5033 0.5138 0.4888 + 0.4626 0.5087 0.4684 0.4666 0.4999 0.4961 0.4703 0.4784 + 0.4626 0.4851 0.5065 0.4597 0.4876 0.4661 0.4876 0.4563 + 0.4728 0.4780 0.4579 0.4776 0.4714 0.4835 0.5096 0.5131 + 0.4651 0.4787 0.5032 0.4942 0.4790 0.4779 0.4815 0.4646 + 0.4762 0.5130 0.4729 0.4785 0.4643 0.4992 0.4811 0.4456 + L 6 mean=0.4703 std=0.0167 min=0.4330 max=0.5116 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4791 0.4680 0.4732 0.4626 0.4528 0.4909 0.4477 0.4492 + 0.4814 0.4642 0.4701 0.4600 0.4729 0.4932 0.4915 0.5116 + 0.4713 0.4660 0.4819 0.4455 0.4671 0.4844 0.4945 0.4810 + 0.4979 0.4708 0.4784 0.4680 0.4822 0.4406 0.4699 0.4779 + 0.4878 0.4868 0.4764 0.4338 0.4853 0.4770 0.4795 0.4948 + 0.4630 0.4533 0.4936 0.4870 0.4726 0.4620 0.4740 0.4407 + 0.4687 0.4685 0.4529 0.4735 0.4555 0.4493 0.4757 0.4712 + 0.4782 0.4389 0.4720 0.4330 0.4578 0.4729 0.4726 0.4431 + L 7 mean=0.4656 std=0.0163 min=0.4214 max=0.4944 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4496 0.4618 0.4753 0.4815 0.4533 0.4667 0.4880 0.4635 + 0.4730 0.4886 0.4861 0.4852 0.4915 0.4409 0.4702 0.4909 + 0.4424 0.4606 0.4696 0.4603 0.4739 0.4725 0.4598 0.4518 + 0.4714 0.4771 0.4464 0.4892 0.4782 0.4611 0.4214 0.4664 + 0.4596 0.4650 0.4454 0.4729 0.4738 0.4915 0.4674 0.4437 + 0.4756 0.4594 0.4695 0.4920 0.4543 0.4524 0.4774 0.4944 + 0.4521 0.4666 0.4792 0.4353 0.4514 0.4535 0.4476 0.4692 + 0.4325 0.4616 0.4556 0.4815 0.4765 0.4489 0.4813 0.4456 + L 8 mean=0.4728 std=0.0155 min=0.4163 max=0.5096 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4860 0.4769 0.4899 0.4633 0.4921 0.4736 0.4726 0.4780 + 0.4548 0.4856 0.4592 0.4841 0.4676 0.4901 0.4764 0.4808 + 0.4904 0.4837 0.4705 0.4347 0.4163 0.4451 0.4954 0.4691 + 0.4800 0.4737 0.5090 0.4755 0.4752 0.4556 0.4713 0.4679 + 0.4775 0.4802 0.4715 0.4788 0.4572 0.4595 0.4821 0.5034 + 0.4598 0.4593 0.4658 0.4744 0.4864 0.4722 0.4477 0.4670 + 0.5096 0.4705 0.4749 0.4874 0.4643 0.4665 0.4628 0.4791 + 0.4738 0.4632 0.4660 0.4749 0.4551 0.4736 0.4753 0.4772 + L 9 mean=0.4695 std=0.0173 min=0.4312 max=0.4973 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4765 0.4902 0.4473 0.4869 0.4555 0.4671 0.4613 0.4459 + 0.4973 0.4535 0.4678 0.4752 0.4816 0.4870 0.4438 0.4633 + 0.4378 0.4631 0.4790 0.4838 0.4791 0.4526 0.4963 0.4547 + 0.4350 0.4686 0.4697 0.4837 0.4597 0.4718 0.4527 0.4800 + 0.4571 0.4546 0.4346 0.4524 0.4831 0.4660 0.4723 0.4864 + 0.4812 0.4805 0.4784 0.4829 0.4845 0.4639 0.4886 0.4365 + 0.4954 0.4940 0.4670 0.4882 0.4714 0.4498 0.4528 0.4728 + 0.4884 0.4813 0.4775 0.4899 0.4312 0.4728 0.4493 0.4929 + L10 mean=0.4721 std=0.0148 min=0.4358 max=0.5016 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4508 0.4498 0.4489 0.4558 0.4754 0.4819 0.4696 0.4832 + 0.4714 0.4698 0.4787 0.4803 0.4652 0.4867 0.4775 0.4763 + 0.4832 0.4743 0.4705 0.4651 0.4972 0.4895 0.4861 0.4895 + 0.4794 0.4861 0.4624 0.4839 0.4889 0.4870 0.4696 0.4881 + 0.4863 0.4523 0.4544 0.4582 0.4563 0.4870 0.4439 0.4725 + 0.5016 0.4610 0.4835 0.4742 0.4819 0.4538 0.4661 0.4632 + 0.4594 0.4881 0.4771 0.4751 0.4609 0.4507 0.4457 0.4358 + 0.4839 0.4512 0.4803 0.4814 0.4930 0.4832 0.4798 0.4534 + L11 mean=0.4856 std=0.0123 min=0.4608 max=0.5169 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4966 0.4788 0.4991 0.4770 0.4788 0.4833 0.4917 0.4891 + 0.5111 0.4947 0.4697 0.4922 0.4845 0.4821 0.4757 0.4817 + 0.4886 0.4955 0.5000 0.4835 0.4776 0.4683 0.4768 0.4934 + 0.4919 0.4709 0.4654 0.4981 0.4963 0.5169 0.4836 0.4695 + 0.4953 0.4780 0.5023 0.4782 0.4747 0.4728 0.4835 0.4947 + 0.4723 0.4760 0.4733 0.5162 0.4901 0.4719 0.4835 0.4951 + 0.5092 0.4918 0.4818 0.4904 0.4917 0.4854 0.4791 0.4608 + 0.4810 0.4828 0.5112 0.4868 0.4711 0.4831 0.4789 0.4735 + + GLOBAL mean=0.4817 std=0.0261 min=0.3893 max=0.5808 BIMODAL=NO-flat + <0.2:0 mid:768 >0.8:0 + Layer means: [0.5163, 0.5072, 0.4816, 0.4776, 0.4795, 0.4817, 0.4703, 0.4656, 0.4728, 0.4695, 0.4721, 0.4856] + Histogram (sigmoid blend ratio 0.0-1.0): + 0.0-0.1 0 + 0.1-0.2 0 + 0.2-0.3 0 + 0.3-0.4 2 + 0.4-0.5 606 ####################################################################################################################################################### + 0.5-0.6 160 ######################################## + 0.6-0.7 0 + 0.7-0.8 0 + 0.8-0.9 0 + 0.9-1.0 0 + + scale_y [0=raw 1=norm] + L 0 mean=0.5732 std=0.0408 min=0.5018 max=0.6370 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5944 0.5329 0.5802 0.5140 0.5838 0.6252 0.6157 0.6028 + 0.6277 0.6183 0.5541 0.6187 0.6367 0.5456 0.5907 0.6071 + 0.5277 0.5365 0.5675 0.5241 0.5396 0.6361 0.5203 0.6370 + 0.5784 0.5784 0.5885 0.5672 0.5741 0.6368 0.5385 0.6202 + 0.5448 0.6287 0.6123 0.5707 0.5837 0.5936 0.5615 0.5595 + 0.6121 0.5018 0.6277 0.5984 0.5403 0.6249 0.5302 0.5914 + 0.6253 0.5175 0.5330 0.5172 0.5382 0.6201 0.5112 0.5644 + 0.5149 0.6291 0.5214 0.5326 0.5385 0.5292 0.5504 0.5401 + L 1 mean=0.5209 std=0.0123 min=0.4908 max=0.5428 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5211 0.5123 0.5109 0.5158 0.5126 0.5159 0.5380 0.5217 + 0.5299 0.5304 0.5401 0.5204 0.5147 0.4999 0.5239 0.5306 + 0.4998 0.5242 0.5362 0.5243 0.5420 0.5260 0.5428 0.5241 + 0.5083 0.5237 0.5227 0.5025 0.5371 0.5361 0.5242 0.5002 + 0.5089 0.5357 0.5128 0.5297 0.5238 0.4908 0.5125 0.5245 + 0.5261 0.5171 0.5026 0.5151 0.5171 0.5201 0.5019 0.4969 + 0.5213 0.5130 0.5182 0.5270 0.5288 0.5293 0.5303 0.5034 + 0.5107 0.5197 0.5229 0.5403 0.5353 0.5381 0.5147 0.5390 + L 2 mean=0.5071 std=0.0157 min=0.4747 max=0.5431 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4920 0.5020 0.5062 0.5083 0.4914 0.5222 0.5126 0.4782 + 0.5155 0.5013 0.4900 0.5260 0.4941 0.5086 0.4803 0.5223 + 0.5239 0.5214 0.4892 0.5265 0.5193 0.4879 0.5271 0.5062 + 0.5044 0.5128 0.5180 0.5153 0.5080 0.5175 0.5220 0.5022 + 0.4841 0.5204 0.5184 0.5069 0.5065 0.5147 0.4809 0.4858 + 0.5082 0.4747 0.4830 0.4805 0.4974 0.5096 0.5160 0.4809 + 0.5092 0.5234 0.5182 0.5413 0.5065 0.5228 0.5199 0.5220 + 0.4997 0.4867 0.5095 0.5016 0.5431 0.5089 0.4974 0.5233 + L 3 mean=0.4941 std=0.0128 min=0.4680 max=0.5258 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4924 0.5096 0.4772 0.4839 0.4821 0.4852 0.5026 0.5226 + 0.4897 0.5027 0.4888 0.5111 0.5234 0.5022 0.4764 0.4831 + 0.4877 0.4997 0.4970 0.5149 0.4799 0.4822 0.4791 0.5010 + 0.4969 0.4931 0.4804 0.4909 0.4904 0.4992 0.5258 0.4746 + 0.4893 0.5008 0.4796 0.4815 0.5074 0.4934 0.4891 0.4824 + 0.5198 0.4868 0.4680 0.4915 0.4940 0.5157 0.4992 0.4877 + 0.5057 0.4719 0.4904 0.4912 0.4904 0.4817 0.5037 0.4900 + 0.5124 0.4969 0.4999 0.5069 0.4939 0.4910 0.4947 0.4899 + L 4 mean=0.4891 std=0.0196 min=0.4465 max=0.5334 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4655 0.4633 0.4758 0.4949 0.4770 0.5217 0.5074 0.5181 + 0.4633 0.4698 0.4816 0.4465 0.5072 0.4879 0.4966 0.4659 + 0.5057 0.4888 0.4860 0.5029 0.4574 0.5313 0.5019 0.4821 + 0.4947 0.5160 0.5334 0.4928 0.4771 0.4970 0.5275 0.4696 + 0.4781 0.4987 0.4795 0.4863 0.4983 0.4692 0.4687 0.4954 + 0.4991 0.4943 0.4767 0.4621 0.5312 0.4860 0.4913 0.5124 + 0.4940 0.4821 0.4740 0.4606 0.5065 0.4629 0.4819 0.4984 + 0.4902 0.5194 0.4618 0.4825 0.4816 0.4974 0.4888 0.4893 + L 5 mean=0.4905 std=0.0395 min=0.4013 max=0.5824 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5348 0.4553 0.4514 0.5330 0.4984 0.4647 0.5240 0.4666 + 0.4410 0.4013 0.5058 0.5215 0.4542 0.5261 0.4331 0.4580 + 0.4945 0.4577 0.4425 0.5404 0.5740 0.5597 0.5060 0.4421 + 0.5192 0.5747 0.4960 0.5125 0.5824 0.5038 0.4910 0.5162 + 0.4589 0.4785 0.4725 0.4847 0.5111 0.4375 0.4849 0.4793 + 0.4662 0.5511 0.5065 0.4524 0.4383 0.5355 0.4635 0.5066 + 0.5205 0.5553 0.4758 0.5342 0.4452 0.5054 0.4602 0.4605 + 0.5228 0.4825 0.4440 0.4772 0.4964 0.4441 0.4609 0.4961 + L 6 mean=0.4899 std=0.0222 min=0.4404 max=0.5505 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5231 0.4732 0.4761 0.4877 0.4948 0.5040 0.5074 0.4745 + 0.4404 0.5041 0.5286 0.5107 0.4624 0.4997 0.4776 0.4824 + 0.4697 0.5107 0.4861 0.4844 0.4939 0.4579 0.5297 0.5165 + 0.4796 0.4853 0.4916 0.4609 0.4979 0.4715 0.4754 0.4788 + 0.4829 0.4787 0.5089 0.4763 0.4826 0.5078 0.4896 0.4667 + 0.4830 0.4547 0.4878 0.4638 0.4522 0.5505 0.4669 0.5198 + 0.4956 0.5078 0.4911 0.5288 0.5456 0.4826 0.4941 0.4849 + 0.4725 0.5004 0.4792 0.4691 0.4808 0.4832 0.5060 0.5227 + L 7 mean=0.4705 std=0.0200 min=0.4361 max=0.5286 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4767 0.4410 0.4382 0.4533 0.4767 0.4580 0.5286 0.4789 + 0.4669 0.4439 0.4782 0.4683 0.4369 0.4577 0.5122 0.4712 + 0.4658 0.4997 0.4688 0.4817 0.4548 0.4992 0.4858 0.4670 + 0.4806 0.4482 0.4872 0.4523 0.4446 0.4411 0.4654 0.4689 + 0.4656 0.4800 0.4577 0.4544 0.4672 0.4501 0.4993 0.4867 + 0.4481 0.4899 0.4786 0.4819 0.4361 0.4828 0.4873 0.4573 + 0.4670 0.4656 0.4749 0.4938 0.5049 0.4777 0.4884 0.4551 + 0.4521 0.4877 0.4663 0.4532 0.4364 0.4746 0.4897 0.5021 + L 8 mean=0.4846 std=0.0278 min=0.3943 max=0.5391 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4730 0.5171 0.4833 0.5092 0.4991 0.4306 0.5116 0.5199 + 0.4453 0.4171 0.4783 0.4925 0.4747 0.5259 0.5131 0.4211 + 0.4904 0.4329 0.4776 0.5391 0.3943 0.5100 0.5078 0.4655 + 0.5135 0.4966 0.4856 0.5003 0.4899 0.5076 0.4702 0.4881 + 0.4619 0.5082 0.4977 0.4627 0.5018 0.4716 0.4443 0.4962 + 0.4217 0.4990 0.5079 0.4940 0.4832 0.4903 0.4837 0.4910 + 0.5001 0.5127 0.4822 0.4697 0.4655 0.5033 0.5016 0.4809 + 0.5061 0.4836 0.4979 0.4861 0.5036 0.4836 0.4711 0.4717 + L 9 mean=0.5048 std=0.0143 min=0.4533 max=0.5344 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5036 0.4854 0.4946 0.5238 0.5217 0.4981 0.4820 0.5085 + 0.5102 0.4822 0.4777 0.4830 0.4988 0.5074 0.5194 0.5086 + 0.5344 0.5108 0.5032 0.4815 0.5007 0.5068 0.5082 0.4874 + 0.5197 0.5080 0.5077 0.4956 0.5166 0.5049 0.5127 0.5035 + 0.5083 0.5132 0.4963 0.5162 0.4970 0.5198 0.5096 0.5044 + 0.5264 0.5263 0.5140 0.5259 0.5010 0.4958 0.4962 0.5056 + 0.5184 0.5129 0.5121 0.5122 0.4899 0.4533 0.5197 0.4997 + 0.4821 0.4969 0.4906 0.5094 0.5252 0.5105 0.5030 0.5098 + L10 mean=0.5083 std=0.0110 min=0.4840 max=0.5369 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4994 0.5088 0.5057 0.5068 0.5028 0.5009 0.5054 0.4915 + 0.5207 0.4970 0.5079 0.5050 0.5009 0.5009 0.4945 0.5004 + 0.5053 0.5249 0.5055 0.5050 0.5086 0.5328 0.5031 0.5062 + 0.5062 0.5141 0.5123 0.5138 0.5202 0.4851 0.4879 0.5190 + 0.4974 0.5369 0.5090 0.5204 0.5079 0.4983 0.5052 0.5058 + 0.5242 0.5079 0.4840 0.5185 0.5062 0.5034 0.5133 0.5168 + 0.5203 0.5007 0.4857 0.5207 0.5003 0.5151 0.5117 0.4871 + 0.5146 0.5147 0.5160 0.5239 0.5182 0.5195 0.5135 0.5178 + L11 mean=0.5106 std=0.0124 min=0.4660 max=0.5528 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5026 0.5202 0.4963 0.5025 0.5163 0.5227 0.5093 0.5055 + 0.4990 0.5009 0.5279 0.5077 0.5218 0.5199 0.5155 0.5203 + 0.4946 0.5041 0.5083 0.4953 0.5233 0.4871 0.5201 0.5172 + 0.5061 0.5172 0.5143 0.5121 0.5066 0.5097 0.5044 0.5237 + 0.5170 0.5375 0.4957 0.5045 0.5199 0.5295 0.5036 0.4960 + 0.5099 0.4932 0.5090 0.5195 0.5039 0.5082 0.4660 0.5161 + 0.5064 0.5151 0.5528 0.5014 0.5163 0.5031 0.5141 0.5092 + 0.4986 0.5091 0.5021 0.5194 0.5161 0.5124 0.5193 0.5211 + + GLOBAL mean=0.5036 std=0.0337 min=0.3943 max=0.6370 BIMODAL=NO-flat + <0.2:0 mid:768 >0.8:0 + Layer means: [0.5732, 0.5209, 0.5071, 0.4941, 0.4891, 0.4905, 0.4899, 0.4705, 0.4846, 0.5048, 0.5083, 0.5106] + Histogram (sigmoid blend ratio 0.0-1.0): + 0.0-0.1 0 + 0.1-0.2 0 + 0.2-0.3 0 + 0.3-0.4 1 + 0.4-0.5 345 ###################################################################################### + 0.5-0.6 402 #################################################################################################### + 0.6-0.7 20 ##### + 0.7-0.8 0 + 0.8-0.9 0 + 0.9-1.0 0 + + [Summary updated: C:\Users\brian\Documents\nanoGPT\results\phase19b_qa_softqa_pd_w2000_6seeds_summary_20260403_0711.txt] + +================================================================================ +SEED 2 -> C:\Users\brian\Documents\nanoGPT\out-openwebtext_phase19b_w2000_qa_softqa_pd_seed2 +================================================================================ +Started: 2026-04-03 14:00:58 + +Finished in 80.9 min returncode=0 +Ended: 2026-04-03 15:21:54 + + best=4.4070@9500 final=4.4109@10000 stable=False + + Scale vectors (POST-SIGMOID | init=0.5 everywhere): + ckpt best_val_loss=4.4109 iter_num=10000 + + scale_q [0=raw 1=norm] + L 0 mean=0.5073 std=0.0218 min=0.4588 max=0.5595 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5143 0.5467 0.5050 0.4879 0.4855 0.4896 0.4839 0.5257 + 0.4936 0.4753 0.4902 0.5243 0.4961 0.5003 0.5531 0.5076 + 0.4818 0.5046 0.5493 0.4889 0.5149 0.4777 0.5233 0.4981 + 0.5078 0.5230 0.5273 0.5269 0.5060 0.5595 0.5052 0.5043 + 0.5017 0.5408 0.4899 0.5158 0.5362 0.5175 0.5022 0.5032 + 0.5073 0.4793 0.5192 0.5047 0.5253 0.4749 0.4938 0.5145 + 0.4994 0.4865 0.4993 0.5014 0.5346 0.5172 0.5094 0.4607 + 0.5099 0.5424 0.4838 0.5473 0.4588 0.4902 0.5190 0.5025 + L 1 mean=0.5044 std=0.0172 min=0.4797 max=0.5506 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4906 0.5082 0.5048 0.4950 0.4822 0.5337 0.5269 0.4939 + 0.5443 0.5020 0.4902 0.4822 0.5002 0.5137 0.4879 0.5358 + 0.5026 0.5255 0.4942 0.5018 0.4962 0.5226 0.4901 0.4919 + 0.4797 0.4921 0.4956 0.4969 0.5077 0.4988 0.4908 0.5240 + 0.5305 0.4874 0.4907 0.4960 0.4893 0.5506 0.4932 0.4934 + 0.4913 0.5154 0.5336 0.5274 0.5240 0.5273 0.5232 0.4916 + 0.5148 0.5080 0.4812 0.4918 0.4899 0.5106 0.5053 0.5354 + 0.5008 0.4991 0.4864 0.4875 0.4922 0.4882 0.5043 0.5182 + L 2 mean=0.4774 std=0.0288 min=0.4298 max=0.5533 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4299 0.4717 0.4505 0.4919 0.4645 0.4420 0.4564 0.5165 + 0.4590 0.4769 0.4859 0.4665 0.5233 0.4666 0.5037 0.4508 + 0.4821 0.4992 0.4728 0.4992 0.4454 0.4660 0.4749 0.4604 + 0.4574 0.4606 0.4498 0.5100 0.4565 0.4585 0.4298 0.4546 + 0.4692 0.4309 0.4395 0.5038 0.4850 0.4989 0.4567 0.5047 + 0.4505 0.4542 0.4988 0.4883 0.5032 0.4473 0.5269 0.5108 + 0.4913 0.4790 0.4540 0.4748 0.4501 0.5077 0.5375 0.4636 + 0.4437 0.4975 0.5085 0.4849 0.5533 0.5048 0.4517 0.5480 + L 3 mean=0.4643 std=0.0474 min=0.3411 max=0.5582 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4677 0.5105 0.4697 0.5551 0.4597 0.4128 0.4436 0.3859 + 0.4579 0.4080 0.5006 0.3411 0.4688 0.3901 0.5017 0.4191 + 0.5108 0.4766 0.5277 0.4539 0.4513 0.4477 0.4802 0.4927 + 0.5436 0.4910 0.4723 0.4008 0.4849 0.4491 0.4465 0.4259 + 0.4906 0.4435 0.5343 0.3852 0.4845 0.5234 0.4487 0.4477 + 0.4878 0.3683 0.5337 0.4630 0.3962 0.4706 0.4743 0.5402 + 0.4869 0.5067 0.4495 0.5140 0.4667 0.4940 0.4623 0.4698 + 0.5582 0.4032 0.4801 0.4271 0.3867 0.4845 0.4930 0.3935 + L 4 mean=0.4852 std=0.0121 min=0.4555 max=0.5091 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4923 0.4985 0.4766 0.4856 0.4737 0.4753 0.4836 0.4902 + 0.4884 0.4935 0.4836 0.5057 0.4753 0.5079 0.5057 0.4830 + 0.4945 0.4841 0.4808 0.4823 0.4718 0.4789 0.4856 0.4857 + 0.4820 0.4974 0.4843 0.4915 0.5036 0.4721 0.4948 0.4892 + 0.4753 0.4932 0.4952 0.4903 0.4918 0.4824 0.4747 0.4852 + 0.4567 0.4952 0.4796 0.4612 0.4763 0.4708 0.4572 0.4997 + 0.4666 0.4885 0.4555 0.4843 0.4878 0.4933 0.5091 0.4959 + 0.4652 0.4954 0.4879 0.4938 0.4974 0.4861 0.4768 0.4875 + L 5 mean=0.4812 std=0.0123 min=0.4525 max=0.5112 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4763 0.4846 0.4715 0.4821 0.4814 0.4525 0.4741 0.4726 + 0.4831 0.4781 0.4877 0.4536 0.5042 0.4995 0.4909 0.4966 + 0.4613 0.4651 0.4784 0.4858 0.4831 0.4720 0.4927 0.4871 + 0.4949 0.4740 0.4785 0.4853 0.5045 0.4892 0.4669 0.4878 + 0.5112 0.4789 0.4847 0.4891 0.4883 0.4621 0.4879 0.4707 + 0.4774 0.4774 0.4780 0.4804 0.4624 0.4853 0.4914 0.4956 + 0.4932 0.4792 0.4978 0.4815 0.4745 0.4875 0.4814 0.4614 + 0.4775 0.4790 0.4961 0.4627 0.4690 0.4662 0.5004 0.4728 + L 6 mean=0.4620 std=0.0204 min=0.3787 max=0.5088 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4601 0.4818 0.4179 0.4751 0.4671 0.4510 0.4771 0.4561 + 0.4552 0.4627 0.4475 0.4547 0.4621 0.4658 0.4699 0.4017 + 0.4735 0.4769 0.4949 0.4368 0.4554 0.4496 0.4327 0.4622 + 0.4716 0.4695 0.4827 0.4501 0.4731 0.4622 0.4436 0.4788 + 0.4796 0.3787 0.4518 0.4671 0.4789 0.4476 0.4733 0.4547 + 0.4501 0.4378 0.4766 0.4789 0.4609 0.4672 0.4679 0.4727 + 0.4537 0.4628 0.4673 0.4410 0.5088 0.4577 0.4911 0.4873 + 0.4704 0.4608 0.4703 0.4688 0.4652 0.4893 0.4702 0.4408 + L 7 mean=0.4713 std=0.0153 min=0.4286 max=0.5070 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4540 0.4749 0.4953 0.4521 0.4708 0.4960 0.4736 0.4512 + 0.4692 0.4688 0.4672 0.4503 0.4656 0.4790 0.4762 0.4826 + 0.4885 0.5037 0.4671 0.4709 0.4564 0.4700 0.4725 0.4490 + 0.4506 0.4734 0.4800 0.4664 0.4728 0.4624 0.4560 0.4742 + 0.4782 0.4770 0.4788 0.4669 0.4844 0.4550 0.4554 0.4627 + 0.4819 0.4968 0.4880 0.4922 0.4954 0.5070 0.4543 0.4685 + 0.4729 0.4679 0.4639 0.4660 0.4576 0.4940 0.4823 0.4286 + 0.4588 0.4651 0.4522 0.4742 0.4915 0.4489 0.4867 0.4669 + L 8 mean=0.4723 std=0.0187 min=0.4286 max=0.5132 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4618 0.4913 0.4593 0.4777 0.4498 0.4720 0.4834 0.4996 + 0.4501 0.4625 0.4895 0.4743 0.4449 0.4789 0.4534 0.4837 + 0.4963 0.4822 0.4591 0.4628 0.4508 0.4448 0.4991 0.4919 + 0.4760 0.4843 0.4782 0.4598 0.5003 0.4562 0.4708 0.4905 + 0.4570 0.4699 0.4997 0.4568 0.4907 0.4719 0.4600 0.4375 + 0.4833 0.4886 0.4911 0.4572 0.4286 0.4744 0.4749 0.4871 + 0.4717 0.4613 0.4919 0.4795 0.4423 0.4617 0.5132 0.4954 + 0.4780 0.4930 0.4409 0.4554 0.4871 0.4369 0.4687 0.4845 + L 9 mean=0.4735 std=0.0164 min=0.4311 max=0.5102 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4918 0.4596 0.4688 0.4664 0.4966 0.4871 0.4721 0.4903 + 0.4753 0.4938 0.4541 0.4573 0.4474 0.4770 0.4841 0.4699 + 0.4965 0.4848 0.4613 0.4679 0.4964 0.4930 0.4957 0.4667 + 0.4818 0.4929 0.4643 0.4733 0.4471 0.4810 0.4587 0.4766 + 0.4781 0.4786 0.4903 0.4801 0.4777 0.4561 0.4921 0.4575 + 0.5102 0.4657 0.4873 0.4600 0.4654 0.4690 0.4895 0.4776 + 0.4701 0.4904 0.4796 0.4610 0.4740 0.4693 0.4709 0.4599 + 0.4524 0.4389 0.4709 0.4394 0.4598 0.5042 0.4693 0.4311 + L10 mean=0.4816 std=0.0115 min=0.4581 max=0.5129 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4856 0.5047 0.4629 0.4746 0.4731 0.4948 0.5031 0.5001 + 0.4717 0.4768 0.4897 0.4914 0.4939 0.4667 0.4802 0.4987 + 0.4904 0.4913 0.4918 0.4869 0.4677 0.4738 0.4766 0.4781 + 0.4847 0.4692 0.4823 0.4823 0.4584 0.4702 0.4993 0.4829 + 0.4698 0.4724 0.5129 0.4902 0.4853 0.4686 0.4787 0.4886 + 0.4839 0.4771 0.4794 0.4822 0.4867 0.4745 0.4643 0.4764 + 0.4915 0.4843 0.4934 0.4828 0.4866 0.4888 0.4790 0.4792 + 0.4824 0.4648 0.4790 0.4581 0.4937 0.4637 0.4721 0.4754 + L11 mean=0.4771 std=0.0134 min=0.4497 max=0.5048 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4960 0.4663 0.4813 0.4770 0.4847 0.4716 0.4777 0.4900 + 0.5015 0.4970 0.4731 0.4761 0.4738 0.4896 0.4805 0.4932 + 0.4939 0.4796 0.4773 0.4766 0.4723 0.4786 0.4703 0.4608 + 0.4727 0.4589 0.4561 0.4714 0.4833 0.4663 0.4893 0.4525 + 0.4826 0.4704 0.4687 0.4589 0.4741 0.4619 0.4915 0.4545 + 0.4682 0.4950 0.4797 0.4803 0.4595 0.4607 0.4936 0.4559 + 0.4911 0.4965 0.4604 0.4497 0.4939 0.4771 0.4691 0.4889 + 0.4899 0.4614 0.4769 0.5048 0.4853 0.4930 0.4832 0.4670 + + GLOBAL mean=0.4798 std=0.0256 min=0.3411 max=0.5595 BIMODAL=NO-flat + <0.2:0 mid:768 >0.8:0 + Layer means: [0.5073, 0.5044, 0.4774, 0.4643, 0.4852, 0.4812, 0.462, 0.4713, 0.4723, 0.4735, 0.4816, 0.4771] + Histogram (sigmoid blend ratio 0.0-1.0): + 0.0-0.1 0 + 0.1-0.2 0 + 0.2-0.3 0 + 0.3-0.4 9 ## + 0.4-0.5 636 ############################################################################################################################################################### + 0.5-0.6 123 ############################## + 0.6-0.7 0 + 0.7-0.8 0 + 0.8-0.9 0 + 0.9-1.0 0 + + scale_y [0=raw 1=norm] + L 0 mean=0.5766 std=0.0373 min=0.5004 max=0.6365 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5454 0.5956 0.6258 0.5816 0.6231 0.5665 0.5457 0.5565 + 0.6252 0.5475 0.5811 0.5360 0.5678 0.5921 0.6173 0.5678 + 0.5958 0.5465 0.6213 0.5517 0.6074 0.5567 0.5160 0.6274 + 0.5812 0.5296 0.6046 0.5511 0.6354 0.5222 0.5961 0.5570 + 0.6026 0.6120 0.5224 0.5819 0.5466 0.5512 0.5585 0.5158 + 0.6365 0.5004 0.5816 0.5169 0.6158 0.6301 0.5681 0.5305 + 0.6012 0.5666 0.6202 0.5131 0.6069 0.5882 0.5360 0.6223 + 0.6042 0.6012 0.6108 0.6346 0.5678 0.5861 0.5073 0.5931 + L 1 mean=0.5237 std=0.0136 min=0.4932 max=0.5578 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5090 0.5253 0.5241 0.5138 0.5223 0.5180 0.5162 0.5238 + 0.5341 0.5039 0.5388 0.5241 0.5193 0.5225 0.5381 0.4932 + 0.5227 0.5104 0.5386 0.5346 0.5206 0.5496 0.5059 0.5257 + 0.5441 0.5221 0.5160 0.5177 0.5109 0.5018 0.5277 0.5243 + 0.5185 0.5162 0.5376 0.5578 0.5258 0.5156 0.5178 0.5196 + 0.5349 0.5322 0.5351 0.5219 0.5024 0.5114 0.5347 0.5065 + 0.5195 0.5573 0.5254 0.5532 0.5170 0.5096 0.5349 0.5404 + 0.5180 0.5168 0.4996 0.5395 0.5194 0.5123 0.5399 0.5261 + L 2 mean=0.4935 std=0.0148 min=0.4613 max=0.5343 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5009 0.5037 0.5107 0.4844 0.4950 0.5026 0.4782 0.5138 + 0.4971 0.5029 0.4777 0.5049 0.4892 0.4992 0.5106 0.4798 + 0.4712 0.4739 0.5205 0.4875 0.4939 0.5160 0.4931 0.4902 + 0.5023 0.5192 0.4888 0.4619 0.4944 0.4802 0.4827 0.4977 + 0.4977 0.4938 0.5026 0.4846 0.5130 0.4820 0.5043 0.4932 + 0.4685 0.4784 0.4915 0.5343 0.4937 0.5068 0.4902 0.5020 + 0.4945 0.4869 0.4743 0.4706 0.4977 0.4930 0.4752 0.4790 + 0.4988 0.4888 0.5125 0.4613 0.4829 0.5136 0.5087 0.4830 + L 3 mean=0.4902 std=0.0188 min=0.4622 max=0.5421 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4717 0.5074 0.5067 0.4940 0.4928 0.4881 0.4808 0.4689 + 0.4724 0.5054 0.4625 0.5007 0.5143 0.4889 0.4909 0.4852 + 0.4763 0.4881 0.4753 0.4622 0.4926 0.4769 0.4705 0.4822 + 0.4955 0.4781 0.5421 0.5341 0.4744 0.4862 0.4935 0.4827 + 0.4899 0.4828 0.4863 0.4653 0.4881 0.4838 0.5405 0.4809 + 0.4789 0.4944 0.4929 0.4737 0.4903 0.4690 0.4956 0.5006 + 0.4660 0.4719 0.4795 0.5215 0.4876 0.4829 0.4785 0.4851 + 0.5029 0.5210 0.5193 0.5249 0.5358 0.4845 0.4786 0.4773 + L 4 mean=0.4827 std=0.0365 min=0.4140 max=0.5835 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5048 0.4527 0.5257 0.4645 0.4984 0.4950 0.4735 0.4678 + 0.4185 0.4420 0.4586 0.4465 0.5257 0.4679 0.4764 0.4849 + 0.5222 0.5067 0.5390 0.4444 0.5266 0.5685 0.4929 0.5046 + 0.5427 0.4548 0.4929 0.5835 0.5029 0.4951 0.4804 0.5072 + 0.4359 0.4621 0.4618 0.4415 0.4810 0.5227 0.4615 0.4198 + 0.5053 0.4389 0.5197 0.5323 0.4431 0.4494 0.4611 0.5300 + 0.4525 0.4238 0.4729 0.4704 0.4816 0.5024 0.4407 0.5106 + 0.4818 0.4564 0.5236 0.4140 0.4675 0.4646 0.4835 0.5164 + L 5 mean=0.4935 std=0.0201 min=0.4474 max=0.5577 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.4767 0.4474 0.4957 0.5145 0.4896 0.5084 0.4667 0.5186 + 0.4740 0.4875 0.4847 0.4740 0.4921 0.4941 0.5302 0.4870 + 0.5142 0.4966 0.5045 0.4853 0.5056 0.5141 0.4950 0.4896 + 0.5160 0.5299 0.5022 0.4758 0.4924 0.4812 0.5577 0.5072 + 0.4801 0.4941 0.4839 0.4977 0.5283 0.4857 0.4981 0.4760 + 0.4714 0.4969 0.5043 0.4767 0.5188 0.4811 0.4771 0.4764 + 0.4675 0.5305 0.4560 0.4849 0.5136 0.5113 0.4780 0.4931 + 0.4697 0.4910 0.4564 0.4882 0.5140 0.4994 0.4881 0.4866 + L 6 mean=0.4844 std=0.0316 min=0.4173 max=0.5614 [<0.2:0 mid:64 >0.8:0] BIMODAL=NO-flat + 0.5242 0.4936 0.5172 0.4866 0.4991 0.4775 0.4774 0.4793 + 0.4173 0.5221 0.4555 0.4806 0.4571 0.4792 0.5280 0.4461 + 0.4755 0.4396 0.4476 0.5124 0.4573 0.4510 0.4348 0.4616 + 0.5068 0.4733 0.4910 0.5107 0.5089 0.5445 0.4983 0.4500 + 0.4574 0.5116 0.5085 0.5097 0.5432 0.5614 0.4732 0.4512 + 0.4712 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BIMODAL=NO-flat + <0.2:0 mid:768 >0.8:0 + Layer means: [0.5766, 0.5237, 0.4935, 0.4902, 0.4827, 0.4935, 0.4844, 0.4849, 0.4937, 0.5027, 0.5154, 0.5134] + Histogram (sigmoid blend ratio 0.0-1.0): + 0.0-0.1 0 + 0.1-0.2 0 + 0.2-0.3 0 + 0.3-0.4 0 + 0.4-0.5 336 #################################################################################### + 0.5-0.6 410 ###################################################################################################### + 0.6-0.7 22 ##### + 0.7-0.8 0 + 0.8-0.9 0 + 0.9-1.0 0 + + [Summary updated: C:\Users\brian\Documents\nanoGPT\results\phase19b_qa_softqa_pd_w2000_6seeds_summary_20260403_0711.txt] + +================================================================================ +PHASE 19b COMPLETE +================================================================================ + +Mean=4.4015 std=0.0095 stable=2/6 vs cheap_qa:+0.0118 + seed=42 best=4.3927 final=4.3927 best@10000 stable=True + seed=1337 best=4.3847 final=4.4304 best@9750 stable=False 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+==================================================================================================== + +Scientific motivation: + Phase 19 started every per-dimension blend at sigmoid(scale)=0.5. + Phase 19c instead initializes every dimension at sigmoid(scale)≈0.74 + using the Phase 17 converged scalar value as an informed prior. + +Architecture: + dynamic_qa_conditioned_softqa_perdim_informed + Same forward pass as dynamic_qa_conditioned_softqa_perdim. + Only difference: qa_q_dim_scale / qa_y_dim_scale initialize at 1.0459. + +Results CSV: C:\Users\brian\Documents\nanoGPT\results\phase19c_qa_softqa_pd_inf_w2000_runs_20260403_1707.csv + +Recipe: + batch_size=4 gradient_accumulation_steps=2 block_size=512 tokens/iter=4,096 + compile=False warmup=2000 max_iters=10000 seeds=[42, 1337, 123] + +qa_softqa_pd_inf (dynamic_qa_conditioned_softqa_perdim_informed) + completed seeds: 3/3 + out_dir base: out-openwebtext_phase19c_w2000_qa_softqa_pd_informed + partial mean/std: 4.3950 ± 0.0049 + learned mean scale_q layerwise mean across completed seeds: [0.7503, 0.7382, 0.7092, 0.7022, 0.7237, 0.7177, 0.7129, 0.7091, 0.7192, 0.7221, 0.7253, 0.7238] + learned mean scale_q global mean across completed seeds: 0.7211 + learned mean scale_y layerwise mean across completed seeds: [0.7813, 0.7541, 0.7309, 0.7288, 0.7293, 0.7416, 0.7391, 0.7269, 0.7313, 0.7457, 0.7507, 0.7478] + learned mean scale_y global mean across completed seeds: 0.7423 + +Completed runs: + seed= 42 best=4.3889 final=4.3889 best@10000 stable=True ✓ + scale_q_means=[0.7528, 0.7397, 0.7123, 0.6995, 0.7206, 0.7205, 0.7157, 0.7051, 0.7203, 0.7187, 0.7212, 0.72] + scale_y_means=[0.7799, 0.7486, 0.7319, 0.7263, 0.7319, 0.7391, 0.7367, 0.7243, 0.735, 0.7451, 0.7511, 0.7427] + scale_q_full_postsigmoid= + L00: [0.7576, 0.7371, 0.7738, 0.7786, 0.7563, 0.7806, 0.7521, 0.7714, 0.7312, 0.7586, 0.7571, 0.7389, 0.7449, 0.7441, 0.767, 0.7755, 0.7389, 0.7516, 0.7317, 0.7478, 0.7645, 0.7558, 0.7615, 0.7324, 0.7457, 0.7867, 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a/results/phase19d_qa_softqa_pd_free_w2000_summary_20260403_2142.txt b/results/phase19d_qa_softqa_pd_free_w2000_summary_20260403_2142.txt new file mode 100644 index 0000000000000000000000000000000000000000..7b0b4afc5130e740abbf275743de6f6c5d5322ff --- /dev/null +++ b/results/phase19d_qa_softqa_pd_free_w2000_summary_20260403_2142.txt @@ -0,0 +1,116 @@ +PHASE 19D — QA SOFT-QA PER-DIMENSION (FREE INIT) +==================================================================================================== + +Scientific motivation: + Phase 19 started every per-dimension blend at sigmoid(scale)=0.5. + Phase 19d instead initializes every dimension at sigmoid(scale)≈0.74 + using tiny random perturbations around 0.5 to break symmetry without directional bias. + +Architecture: + dynamic_qa_conditioned_softqa_perdim_free + Same forward pass as dynamic_qa_conditioned_softqa_perdim. + Only difference: qa_q_dim_scale / qa_y_dim_scale initialize at 1.0459. + +Results CSV: C:\Users\brian\Documents\nanoGPT\results\phase19d_qa_softqa_pd_free_w2000_runs_20260403_2142.csv + +Recipe: + batch_size=4 gradient_accumulation_steps=2 block_size=512 tokens/iter=4,096 + compile=False warmup=2000 max_iters=10000 seeds=[42, 1337, 123] + +qa_softqa_pd_free (dynamic_qa_conditioned_softqa_perdim_free) + completed seeds: 3/3 + out_dir base: out-openwebtext_phase19d_w2000_qa_softqa_pd_free + partial mean/std: 4.4085 ± 0.0221 + learned mean scale_q layerwise mean across completed seeds: [0.5229, 0.5083, 0.4761, 0.4885, 0.4858, 0.4742, 0.465, 0.47, 0.4759, 0.4813, 0.4845, 0.482] + learned mean scale_q global mean across completed seeds: 0.4845 + learned mean scale_y layerwise mean across completed seeds: [0.5683, 0.5181, 0.5015, 0.4904, 0.4701, 0.48, 0.4788, 0.4882, 0.4814, 0.502, 0.5096, 0.5149] + learned mean scale_y global mean across completed seeds: 0.5003 + +Completed runs: + seed= 42 best=4.3858 final=4.3858 best@10000 stable=True ✓ + scale_q_means=[0.5281, 0.5077, 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0.4933, 0.4831, 0.4876, 0.4989, 0.4854, 0.486, 0.4382, 0.4733, 0.454, 0.4859, 0.4599, 0.4506, 0.46, 0.4516, 0.4815, 0.4932, 0.5099, 0.5324, 0.4928, 0.4887, 0.5095, 0.4881, 0.4545, 0.4592, 0.4546, 0.4847, 0.4672, 0.4468, 0.4806, 0.5034, 0.4723, 0.4562, 0.4495, 0.5058, 0.4933, 0.4551, 0.4987, 0.4593, 0.4564, 0.4555, 0.4935, 0.4708, 0.4661, 0.4959, 0.4885, 0.4778, 0.5044, 0.4927, 0.478, 0.491, 0.4567, 0.4604] + L07: [0.4789, 0.4844, 0.4543, 0.5203, 0.517, 0.4332, 0.5014, 0.4924, 0.4681, 0.4506, 0.4904, 0.4963, 0.4849, 0.4912, 0.5295, 0.5449, 0.4713, 0.4963, 0.4701, 0.4877, 0.4871, 0.4485, 0.4643, 0.4909, 0.4837, 0.5353, 0.4825, 0.5001, 0.4234, 0.447, 0.5338, 0.4769, 0.4871, 0.4674, 0.4971, 0.4799, 0.4418, 0.4082, 0.4681, 0.4803, 0.4754, 0.4259, 0.466, 0.5169, 0.4827, 0.4682, 0.478, 0.4857, 0.4656, 0.4808, 0.4896, 0.5036, 0.5019, 0.4965, 0.4938, 0.4681, 0.4497, 0.491, 0.4862, 0.4511, 0.4577, 0.4833, 0.4498, 0.4588] + L08: [0.4555, 0.494, 0.4793, 0.479, 0.5148, 0.4499, 0.519, 0.5053, 0.471, 0.5087, 0.4273, 0.4878, 0.3702, 0.5278, 0.4969, 0.5016, 0.4534, 0.5013, 0.5005, 0.5032, 0.4953, 0.4505, 0.4893, 0.5166, 0.4257, 0.4823, 0.486, 0.4966, 0.5008, 0.4709, 0.5074, 0.5006, 0.4699, 0.4747, 0.4791, 0.4606, 0.4144, 0.5006, 0.4953, 0.5028, 0.4775, 0.487, 0.4902, 0.4412, 0.4988, 0.4728, 0.4947, 0.4894, 0.466, 0.497, 0.4964, 0.4873, 0.5047, 0.4613, 0.453, 0.517, 0.4937, 0.4243, 0.4746, 0.4848, 0.5038, 0.5013, 0.4993, 0.527] + L09: [0.4917, 0.4975, 0.4968, 0.4853, 0.5016, 0.4917, 0.4753, 0.5027, 0.5087, 0.5097, 0.492, 0.4473, 0.5099, 0.5138, 0.4929, 0.5248, 0.4833, 0.5183, 0.5187, 0.4875, 0.501, 0.4977, 0.4823, 0.4988, 0.4824, 0.5022, 0.5014, 0.4779, 0.5027, 0.5068, 0.4879, 0.4886, 0.4941, 0.5078, 0.4315, 0.4961, 0.5127, 0.4783, 0.4964, 0.4731, 0.4939, 0.4792, 0.4832, 0.5091, 0.4694, 0.4943, 0.4947, 0.5084, 0.4202, 0.4862, 0.4743, 0.4944, 0.5175, 0.4836, 0.4814, 0.4918, 0.4827, 0.4988, 0.5203, 0.5283, 0.5044, 0.5158, 0.5232, 0.507] + L10: [0.5027, 0.5008, 0.5068, 0.5152, 0.4864, 0.5102, 0.5232, 0.5107, 0.5189, 0.5083, 0.496, 0.52, 0.4984, 0.516, 0.514, 0.5257, 0.5176, 0.5112, 0.5149, 0.5076, 0.5146, 0.4888, 0.4951, 0.5068, 0.4984, 0.498, 0.5046, 0.5102, 0.5151, 0.5342, 0.499, 0.498, 0.5082, 0.4981, 0.4805, 0.4988, 0.4916, 0.4753, 0.5181, 0.4913, 0.5054, 0.5208, 0.5143, 0.5064, 0.5193, 0.5089, 0.5039, 0.4985, 0.4959, 0.4881, 0.5128, 0.5074, 0.5289, 0.5157, 0.5131, 0.4902, 0.4942, 0.5062, 0.4953, 0.5231, 0.4981, 0.4926, 0.5073, 0.5082] + L11: [0.5255, 0.5219, 0.5116, 0.5027, 0.5127, 0.5087, 0.5283, 0.5263, 0.5162, 0.5229, 0.5124, 0.5293, 0.5229, 0.5104, 0.5149, 0.5296, 0.5163, 0.5079, 0.5083, 0.5061, 0.5188, 0.5115, 0.5104, 0.5101, 0.4984, 0.5207, 0.5286, 0.5093, 0.5125, 0.5202, 0.5345, 0.5272, 0.5126, 0.5437, 0.5288, 0.5245, 0.5173, 0.5322, 0.503, 0.5125, 0.5328, 0.5197, 0.5156, 0.5273, 0.5249, 0.5147, 0.5213, 0.5072, 0.5216, 0.5059, 0.5355, 0.5212, 0.5109, 0.5065, 0.5267, 0.5262, 0.524, 0.5134, 0.5309, 0.5256, 0.5198, 0.5151, 0.5057, 0.5278] diff --git a/results/phase7_warmup500_normed_q_runs_20260328_1215.csv b/results/phase7_warmup500_normed_q_runs_20260328_1215.csv new file mode 100644 index 0000000000000000000000000000000000000000..af4dd16ebf7f9ce68ea939975dd3cd826600f08b --- /dev/null +++ b/results/phase7_warmup500_normed_q_runs_20260328_1215.csv @@ -0,0 +1,7 @@ +variant,short_name,warmup,seed,status,runtime_sec,params_m,best_val,best_step,final_val,stable,out_dir,error +dynamic_qconditioned_normed,cheap_q_normed,500,42,ok,4105.93,123.64,4.6496,10000,4.6496,False,out-openwebtext_phase7_w500_cheap_q_normed, +dynamic_qconditioned_normed,cheap_q_normed,500,1337,ok,4066.51,123.64,4.7181,10000,4.7181,False,out-openwebtext_phase7_w500_cheap_q_normed, +dynamic_qconditioned_normed,cheap_q_normed,500,123,ok,4050.89,123.64,4.6116,10000,4.6116,False,out-openwebtext_phase7_w500_cheap_q_normed, +dynamic_qconditioned_mlp128_normed,midtier_q_normed,500,42,ok,4215.02,123.78,4.5975,10000,4.5975,False,out-openwebtext_phase7_w500_midtier_q_normed, +dynamic_qconditioned_mlp128_normed,midtier_q_normed,500,1337,ok,4215.67,123.78,4.6438,10000,4.6438,False,out-openwebtext_phase7_w500_midtier_q_normed, +dynamic_qconditioned_mlp128_normed,midtier_q_normed,500,123,ok,4231.88,123.78,4.636,10000,4.636,False,out-openwebtext_phase7_w500_midtier_q_normed, diff --git a/results/phase7_warmup500_normed_q_summary_20260328_1215.txt b/results/phase7_warmup500_normed_q_summary_20260328_1215.txt new file mode 100644 index 0000000000000000000000000000000000000000..a2a3663193510f8ff69bf08ab1c7b316cbcc02a4 --- /dev/null +++ b/results/phase7_warmup500_normed_q_summary_20260328_1215.txt @@ -0,0 +1,52 @@ +PHASE 7 — WARMUP 500 NORMED Q-ONLY SUMMARY +================================================================================================ + +Bundle purpose: + Test whether RMS-normalizing Q improves harsh-warmup robustness for low-dimensional Q-only gates. + +Results CSV: C:\Users\brian\Documents\nanoGPT\results\phase7_warmup500_normed_q_runs_20260328_1215.csv + +Current warmup=500 canon: + baseline 4.5663 + qa_normed 4.5902 + midtier_q 4.6009 + cheap_qa 4.6130 + qa_lowrank 4.6289 + full_x 4.8518 + +Leaderboard within this phase (completed seeds only): + 1. midtier_q_normed mean=4.6258 std=0.0202 done=3/3 vs baseline@500=+0.0595 vs qa_normed@500=+0.0356 + 2. cheap_q_normed mean=4.6598 std=0.0441 done=3/3 vs baseline@500=+0.0935 vs qa_normed@500=+0.0696 + +------------------------------------------------------------------------------------------------ +cheap_q_normed (dynamic_qconditioned_normed) + RMSNorm(q) -> shared Linear(64->64) -> sigmoid gate + out_dir: out-openwebtext_phase7_w500_cheap_q_normed + completed seeds: 3/3 + partial mean/std: 4.6598 ± 0.0441 + vs baseline@500: +0.0935 + vs qa_normed@500: +0.0696 + vs unnormed 2000 anchor (cheap_q): +0.2400 + seed= 42 best=4.6496 final=4.6496 best@10000 stable=False vs baseline@500=+0.0833 vs qa_normed@500=+0.0594 + seed= 123 best=4.6116 final=4.6116 best@10000 stable=False vs baseline@500=+0.0453 vs qa_normed@500=+0.0214 + seed=1337 best=4.7181 final=4.7181 best@10000 stable=False vs baseline@500=+0.1518 vs qa_normed@500=+0.1279 + +------------------------------------------------------------------------------------------------ +midtier_q_normed (dynamic_qconditioned_mlp128_normed) + RMSNorm(q) -> shared MLP(64->128->64) -> sigmoid gate + out_dir: out-openwebtext_phase7_w500_midtier_q_normed + completed seeds: 3/3 + partial mean/std: 4.6258 ± 0.0202 + vs baseline@500: +0.0595 + vs qa_normed@500: +0.0356 + vs unnormed 2000 anchor (midtier_q): +0.2123 + seed= 42 best=4.5975 final=4.5975 best@10000 stable=False vs baseline@500=+0.0312 vs qa_normed@500=+0.0073 + seed= 123 best=4.6360 final=4.6360 best@10000 stable=False vs baseline@500=+0.0697 vs qa_normed@500=+0.0458 + seed=1337 best=4.6438 final=4.6438 best@10000 stable=False vs baseline@500=+0.0775 vs qa_normed@500=+0.0536 + +Win conditions: + - baseline beaten if mean < 4.5663 + - qa_normed beaten if mean < 4.5902 + - cheap_q_normed should ideally improve on cheap_q-family behavior + - midtier_q_normed should ideally improve on midtier_q-family behavior + diff --git a/results/phase8_warmup2000_bulk_a10_runs_20260329_0650.csv b/results/phase8_warmup2000_bulk_a10_runs_20260329_0650.csv new file mode 100644 index 0000000000000000000000000000000000000000..5907c40a245c9fc534fd382679f9968be49ab57d --- /dev/null +++ b/results/phase8_warmup2000_bulk_a10_runs_20260329_0650.csv @@ -0,0 +1,58 @@ +variant,short_name,warmup,seed,status,runtime_sec,params_m,best_val,best_step,final_val,stable,out_dir,error +none,baseline,2000,42,ok,1735.68,123.59,4.4737,10000,4.4737,False,out-openwebtext_phase8_w2000_baseline, +none,baseline,2000,1337,ok,1749.5,123.59,4.4611,10000,4.4611,False,out-openwebtext_phase8_w2000_baseline, +none,baseline,2000,123,ok,1751.66,123.59,4.4681,10000,4.4681,False,out-openwebtext_phase8_w2000_baseline, +dynamic_qa_conditioned,cheap_qa,2000,42,ok,1857.66,123.69,4.4293,9500,4.4318,False,out-openwebtext_phase8_w2000_cheap_qa, +dynamic_qa_conditioned,cheap_qa,2000,1337,ok,1864.67,123.69,4.4322,10000,4.4322,False,out-openwebtext_phase8_w2000_cheap_qa, +dynamic_qa_conditioned,cheap_qa,2000,123,ok,1864.09,123.69,4.3851,9750,4.4005,False,out-openwebtext_phase8_w2000_cheap_qa, +dynamic_xconditioned_g1,full_x,2000,42,ok,1889.04,130.67,4.4164,10000,4.4164,False,out-openwebtext_phase8_w2000_full_x, +dynamic_xconditioned_g1,full_x,2000,1337,ok,1887.89,130.67,4.3782,10000,4.3782,False,out-openwebtext_phase8_w2000_full_x, +dynamic_xconditioned_g1,full_x,2000,123,ok,1886.1,130.67,4.4137,10000,4.4137,False,out-openwebtext_phase8_w2000_full_x, +dynamic_qa_conditioned_headspecific,qa_headspec,2000,42,ok,1882.99,124.77,4.4013,10000,4.4013,False,out-openwebtext_phase8_w2000_qa_headspec, +dynamic_qa_conditioned_headspecific,qa_headspec,2000,1337,ok,1883.89,124.77,4.4196,9750,4.4213,False,out-openwebtext_phase8_w2000_qa_headspec, +dynamic_qa_conditioned_headspecific,qa_headspec,2000,123,ok,1882.62,124.77,4.4614,10000,4.4614,False,out-openwebtext_phase8_w2000_qa_headspec, +dynamic_xconditioned_fullwidth_headspecific,x_full_headspec,2000,42,ok,1886.38,130.67,4.3903,10000,4.3903,False,out-openwebtext_phase8_w2000_x_full_headspec, +dynamic_xconditioned_fullwidth_headspecific,x_full_headspec,2000,1337,ok,1880.93,130.67,4.3879,10000,4.3879,False,out-openwebtext_phase8_w2000_x_full_headspec, +dynamic_xconditioned_fullwidth_headspecific,x_full_headspec,2000,123,ok,1886.31,130.67,4.3792,10000,4.3792,False,out-openwebtext_phase8_w2000_x_full_headspec, +dynamic_qa_conditioned_mlp128,mlp128_qa,2000,42,ok,1931.17,123.88,4.4222,10000,4.4222,False,out-openwebtext_phase8_w2000_mlp128_qa, +dynamic_qa_conditioned_mlp128,mlp128_qa,2000,1337,ok,1924.28,123.88,4.4123,10000,4.4123,False,out-openwebtext_phase8_w2000_mlp128_qa, +dynamic_qa_conditioned_mlp128,mlp128_qa,2000,123,ok,1933.44,123.88,4.3689,9750,4.3705,False,out-openwebtext_phase8_w2000_mlp128_qa, +dynamic_qconditioned_fullwidth_headspecific,q_full_headspec,2000,42,ok,1875.86,130.67,4.4112,10000,4.4112,False,out-openwebtext_phase8_w2000_q_full_headspec, +dynamic_qconditioned_fullwidth_headspecific,q_full_headspec,2000,1337,ok,1869.75,130.67,4.4133,10000,4.4133,False,out-openwebtext_phase8_w2000_q_full_headspec, +dynamic_qconditioned_fullwidth_headspecific,q_full_headspec,2000,123,ok,1874.79,130.67,4.3857,10000,4.3857,False,out-openwebtext_phase8_w2000_q_full_headspec, +dynamic_qconditioned_fullwidth,full_q,2000,42,ok,1871.35,130.67,4.4322,10000,4.4322,False,out-openwebtext_phase8_w2000_full_q, +dynamic_qconditioned_fullwidth,full_q,2000,1337,ok,1875.79,130.67,4.3789,10000,4.3789,False,out-openwebtext_phase8_w2000_full_q, +dynamic_qconditioned_fullwidth,full_q,2000,123,ok,1879.86,130.67,4.4223,10000,4.4223,False,out-openwebtext_phase8_w2000_full_q, +dynamic_qconditioned_mlp128,midtier_q,2000,42,ok,1913.53,123.78,4.4142,9750,4.433,False,out-openwebtext_phase8_w2000_midtier_q, +dynamic_qconditioned_mlp128,midtier_q,2000,1337,ok,1900.51,123.78,4.4103,9500,4.416,False,out-openwebtext_phase8_w2000_midtier_q, +dynamic_qconditioned_mlp128,midtier_q,2000,123,ok,1896.34,123.78,4.4304,9750,4.4436,False,out-openwebtext_phase8_w2000_midtier_q, +dynamic,cheap_q,2000,42,ok,1830.72,123.64,4.4262,10000,4.4262,False,out-openwebtext_phase8_w2000_cheap_q, +dynamic,cheap_q,2000,1337,ok,1831.12,123.64,4.4146,10000,4.4146,False,out-openwebtext_phase8_w2000_cheap_q, +dynamic,cheap_q,2000,123,ok,1827.42,123.64,4.4339,9750,4.4497,False,out-openwebtext_phase8_w2000_cheap_q, +dynamic_a_conditioned,a_only,2000,42,ok,1824.55,123.64,4.4344,10000,4.4344,False,out-openwebtext_phase8_w2000_a_only, +dynamic_a_conditioned,a_only,2000,1337,ok,1827.6,123.64,4.4283,10000,4.4283,False,out-openwebtext_phase8_w2000_a_only, +dynamic_a_conditioned,a_only,2000,123,ok,1823.89,123.64,4.4349,9750,4.4504,False,out-openwebtext_phase8_w2000_a_only, +dynamic_x_g1_headshared_elementwise,x_headshared,2000,42,ok,1844.55,123.64,4.4441,10000,4.4441,False,out-openwebtext_phase8_w2000_x_headshared, +dynamic_x_g1_headshared_elementwise,x_headshared,2000,1337,ok,1846.02,123.64,4.4321,10000,4.4321,False,out-openwebtext_phase8_w2000_x_headshared, +dynamic_x_g1_headshared_elementwise,x_headshared,2000,123,ok,1843.78,123.64,4.4468,9750,4.4622,False,out-openwebtext_phase8_w2000_x_headshared, +static_gate_prehead,static_prehead,2000,42,ok,1792.23,123.6,4.4549,10000,4.4549,False,out-openwebtext_phase8_w2000_static_prehead, +static_gate_prehead,static_prehead,2000,1337,ok,1792.19,123.6,4.4298,10000,4.4298,False,out-openwebtext_phase8_w2000_static_prehead, +static_gate_prehead,static_prehead,2000,123,ok,1794.53,123.6,4.4442,10000,4.4442,False,out-openwebtext_phase8_w2000_static_prehead, +dynamic_x_g1_headspecific_elementwise,x_headspec_elem,2000,42,ok,1834.08,124.18,4.4492,10000,4.4492,False,out-openwebtext_phase8_w2000_x_headspec_elem, +dynamic_x_g1_headspecific_elementwise,x_headspec_elem,2000,1337,ok,1833.61,124.18,4.4424,10000,4.4424,False,out-openwebtext_phase8_w2000_x_headspec_elem, +dynamic_x_g1_headspecific_elementwise,x_headspec_elem,2000,123,ok,1830.68,124.18,4.4322,9750,4.4347,False,out-openwebtext_phase8_w2000_x_headspec_elem, +dynamic_qa_bilinear_diag,bilinear_diag,2000,42,ok,1905.1,123.59,4.47,10000,4.47,False,out-openwebtext_phase8_w2000_bilinear_diag, +dynamic_qa_bilinear_diag,bilinear_diag,2000,1337,ok,1915.59,123.59,4.4403,10000,4.4403,False,out-openwebtext_phase8_w2000_bilinear_diag, +dynamic_qa_bilinear_diag,bilinear_diag,2000,123,ok,1913.56,123.59,4.4614,10000,4.4614,False,out-openwebtext_phase8_w2000_bilinear_diag, +dynamic_dot_elementwise,dot_elementwise,2000,42,ok,1814.2,123.59,4.4757,10000,4.4757,False,out-openwebtext_phase8_w2000_dot_elementwise, +dynamic_dot_elementwise,dot_elementwise,2000,1337,ok,1814.77,123.59,4.4405,10000,4.4405,False,out-openwebtext_phase8_w2000_dot_elementwise, +dynamic_dot_elementwise,dot_elementwise,2000,123,ok,1814.46,123.59,4.4548,10000,4.4548,False,out-openwebtext_phase8_w2000_dot_elementwise, +dynamic_dot_scalar,dot_scalar,2000,42,ok,1851.26,123.59,4.4485,10000,4.4485,False,out-openwebtext_phase8_w2000_dot_scalar, +dynamic_dot_scalar,dot_scalar,2000,1337,ok,1852.06,123.59,4.4533,10000,4.4533,False,out-openwebtext_phase8_w2000_dot_scalar, +dynamic_dot_scalar,dot_scalar,2000,123,ok,1858.23,123.59,4.4683,10000,4.4683,False,out-openwebtext_phase8_w2000_dot_scalar, +dynamic_qa_conditioned_normed,qa_normed,2000,42,ok,2247.96,123.69,4.4161,9500,4.42,False,out-openwebtext_phase8_w2000_qa_normed, +dynamic_qa_conditioned_normed,qa_normed,2000,1337,ok,2251.08,123.69,4.4189,10000,4.4189,False,out-openwebtext_phase8_w2000_qa_normed, +dynamic_qa_conditioned_normed,qa_normed,2000,123,ok,2259.74,123.69,4.3902,9750,4.4063,False,out-openwebtext_phase8_w2000_qa_normed, +dynamic_qa_conditioned_lowrank16,qa_lowrank,2000,42,ok,1860.82,123.62,4.4121,10000,4.4121,False,out-openwebtext_phase8_w2000_qa_lowrank16, +dynamic_qa_conditioned_lowrank16,qa_lowrank,2000,1337,ok,1855.74,123.62,4.4206,9500,4.4238,False,out-openwebtext_phase8_w2000_qa_lowrank16, +dynamic_qa_conditioned_lowrank16,qa_lowrank,2000,123,ok,1856.66,123.62,4.4164,10000,4.4164,False,out-openwebtext_phase8_w2000_qa_lowrank16, diff --git a/results/phase8_warmup2000_bulk_a10_summary_20260329_0650.txt b/results/phase8_warmup2000_bulk_a10_summary_20260329_0650.txt new file mode 100644 index 0000000000000000000000000000000000000000..4701d6cde543edee1ff2daffd5dfbd52ab9a5a2b --- /dev/null +++ b/results/phase8_warmup2000_bulk_a10_summary_20260329_0650.txt @@ -0,0 +1,267 @@ +PHASE 8 — WARMUP 2000 BULK RERUN SUMMARY +==================================================================================================== + +Bundle purpose: + Re-run the full warmup=2000 board plus later Q+A additions under one corrected large-GPU recipe. + +Results CSV: /home/ubuntu/nanoGPT-gating-research/results/phase8_warmup2000_bulk_a10_runs_20260329_0650.csv + +Recipe: + batch_size=8 gradient_accumulation_steps=1 block_size=512 tokens/iter=4,096 + compile=False warmup=2000 max_iters=10000 seeds=[42, 1337, 123] + +Existing warmup=2000 canon reference: + cheap_qa 4.3897 + qa_normed 4.3926 + full_x 4.3950 + qa_headspec 4.3951 + x_full_headspec 4.3964 + mlp128_qa 4.4032 + q_full_headspec 4.4058 + qa_lowrank 4.4068 + full_q 4.4109 + midtier_q 4.4135 + cheap_q 4.4198 + a_only 4.4298 + x_headshared 4.4346 + static_prehead 4.4438 + x_headspec_elem 4.4450 + bilinear_diag 4.4516 + dot_elementwise 4.4537 + dot_scalar 4.4601 + baseline 4.4708 + +Leaderboard within this rerun (completed seeds only): + 1. x_full_headspec mean=4.3858 std=0.0048 done=3/3 vs old 2000=-0.0106 + 2. mlp128_qa mean=4.4011 std=0.0231 done=3/3 vs old 2000=-0.0021 + 3. full_x mean=4.4028 std=0.0174 done=3/3 vs old 2000=+0.0078 + 4. q_full_headspec mean=4.4034 std=0.0125 done=3/3 vs old 2000=-0.0024 + 5. qa_normed mean=4.4084 std=0.0129 done=3/3 vs old 2000=+0.0158 + 6. full_q mean=4.4111 std=0.0231 done=3/3 vs old 2000=+0.0002 + 7. cheap_qa mean=4.4155 std=0.0216 done=3/3 vs old 2000=+0.0258 + 8. qa_lowrank mean=4.4164 std=0.0035 done=3/3 vs old 2000=+0.0096 + 9. midtier_q mean=4.4183 std=0.0087 done=3/3 vs old 2000=+0.0048 + 10. cheap_q mean=4.4249 std=0.0079 done=3/3 vs old 2000=+0.0051 + 11. qa_headspec mean=4.4274 std=0.0252 done=3/3 vs old 2000=+0.0323 + 12. a_only mean=4.4325 std=0.0030 done=3/3 vs old 2000=+0.0027 + 13. x_headshared mean=4.4410 std=0.0064 done=3/3 vs old 2000=+0.0064 + 14. x_headspec_elem mean=4.4413 std=0.0070 done=3/3 vs old 2000=-0.0037 + 15. static_prehead mean=4.4430 std=0.0103 done=3/3 vs old 2000=-0.0008 + 16. dot_scalar mean=4.4567 std=0.0084 done=3/3 vs old 2000=-0.0034 + 17. dot_elementwise mean=4.4570 std=0.0145 done=3/3 vs old 2000=+0.0033 + 18. bilinear_diag mean=4.4572 std=0.0125 done=3/3 vs old 2000=+0.0056 + 19. baseline mean=4.4676 std=0.0052 done=3/3 vs old 2000=-0.0032 + +---------------------------------------------------------------------------------------------------- +baseline (none) + No writeback gate + out_dir: out-openwebtext_phase8_w2000_baseline + completed seeds: 3/3 + partial mean/std: 4.4676 ± 0.0052 + vs old 2000 anchor: -0.0032 + seed= 42 best=4.4737 final=4.4737 best@10000 stable=False vs old=+0.0029 + seed= 123 best=4.4681 final=4.4681 best@10000 stable=False vs old=-0.0027 + seed=1337 best=4.4611 final=4.4611 best@10000 stable=False vs old=-0.0097 + +---------------------------------------------------------------------------------------------------- +cheap_qa (dynamic_qa_conditioned) + Shared Q+A linear gate: concat [q,y] -> 128->64 + out_dir: out-openwebtext_phase8_w2000_cheap_qa + completed seeds: 3/3 + partial mean/std: 4.4155 ± 0.0216 + vs old 2000 anchor: +0.0258 + seed= 42 best=4.4293 final=4.4318 best@9500 stable=False vs old=+0.0396 + seed= 123 best=4.3851 final=4.4005 best@9750 stable=False vs old=-0.0046 + seed=1337 best=4.4322 final=4.4322 best@10000 stable=False vs old=+0.0425 + +---------------------------------------------------------------------------------------------------- +full_x (dynamic_xconditioned_g1) + Shared full-width X gate: x_prenorm 768->768, reshaped to heads + out_dir: out-openwebtext_phase8_w2000_full_x + completed seeds: 3/3 + partial mean/std: 4.4028 ± 0.0174 + vs old 2000 anchor: +0.0078 + seed= 42 best=4.4164 final=4.4164 best@10000 stable=False vs old=+0.0214 + seed= 123 best=4.4137 final=4.4137 best@10000 stable=False vs old=+0.0187 + seed=1337 best=4.3782 final=4.3782 best@10000 stable=False vs old=-0.0168 + +---------------------------------------------------------------------------------------------------- +qa_headspec (dynamic_qa_conditioned_headspecific) + Head-specific Q+A linear gate: separate 128->64 per head + out_dir: out-openwebtext_phase8_w2000_qa_headspec + completed seeds: 3/3 + partial mean/std: 4.4274 ± 0.0252 + vs old 2000 anchor: +0.0323 + seed= 42 best=4.4013 final=4.4013 best@10000 stable=False vs old=+0.0062 + seed= 123 best=4.4614 final=4.4614 best@10000 stable=False vs old=+0.0663 + seed=1337 best=4.4196 final=4.4213 best@9750 stable=False vs old=+0.0245 + +---------------------------------------------------------------------------------------------------- +x_full_headspec (dynamic_xconditioned_fullwidth_headspecific) + Head-specific full-width X gate: separate 768->64 per head + out_dir: out-openwebtext_phase8_w2000_x_full_headspec + completed seeds: 3/3 + partial mean/std: 4.3858 ± 0.0048 + vs old 2000 anchor: -0.0106 + seed= 42 best=4.3903 final=4.3903 best@10000 stable=False vs old=-0.0061 + seed= 123 best=4.3792 final=4.3792 best@10000 stable=False vs old=-0.0172 + seed=1337 best=4.3879 final=4.3879 best@10000 stable=False vs old=-0.0085 + +---------------------------------------------------------------------------------------------------- +mlp128_qa (dynamic_qa_conditioned_mlp128) + Shared Q+A MLP gate: 128->128->64 + out_dir: out-openwebtext_phase8_w2000_mlp128_qa + completed seeds: 3/3 + partial mean/std: 4.4011 ± 0.0231 + vs old 2000 anchor: -0.0021 + seed= 42 best=4.4222 final=4.4222 best@10000 stable=False vs old=+0.0190 + seed= 123 best=4.3689 final=4.3705 best@9750 stable=False vs old=-0.0343 + seed=1337 best=4.4123 final=4.4123 best@10000 stable=False vs old=+0.0091 + +---------------------------------------------------------------------------------------------------- +q_full_headspec (dynamic_qconditioned_fullwidth_headspecific) + Head-specific full-width Q gate: separate 768->64 per head + out_dir: out-openwebtext_phase8_w2000_q_full_headspec + completed seeds: 3/3 + partial mean/std: 4.4034 ± 0.0125 + vs old 2000 anchor: -0.0024 + seed= 42 best=4.4112 final=4.4112 best@10000 stable=False vs old=+0.0054 + seed= 123 best=4.3857 final=4.3857 best@10000 stable=False vs old=-0.0201 + seed=1337 best=4.4133 final=4.4133 best@10000 stable=False vs old=+0.0075 + +---------------------------------------------------------------------------------------------------- +full_q (dynamic_qconditioned_fullwidth) + Shared full-width Q gate: 768->768 reshaped to heads + out_dir: out-openwebtext_phase8_w2000_full_q + completed seeds: 3/3 + partial mean/std: 4.4111 ± 0.0231 + vs old 2000 anchor: +0.0002 + seed= 42 best=4.4322 final=4.4322 best@10000 stable=False vs old=+0.0213 + seed= 123 best=4.4223 final=4.4223 best@10000 stable=False vs old=+0.0114 + seed=1337 best=4.3789 final=4.3789 best@10000 stable=False vs old=-0.0320 + +---------------------------------------------------------------------------------------------------- +midtier_q (dynamic_qconditioned_mlp128) + Shared Q MLP gate: 64->128->64 + out_dir: out-openwebtext_phase8_w2000_midtier_q + completed seeds: 3/3 + partial mean/std: 4.4183 ± 0.0087 + vs old 2000 anchor: +0.0048 + seed= 42 best=4.4142 final=4.4330 best@9750 stable=False vs old=+0.0007 + seed= 123 best=4.4304 final=4.4436 best@9750 stable=False vs old=+0.0169 + seed=1337 best=4.4103 final=4.4160 best@9500 stable=False vs old=-0.0032 + +---------------------------------------------------------------------------------------------------- +cheap_q (dynamic) + Shared Q linear gate: 64->64 + out_dir: out-openwebtext_phase8_w2000_cheap_q + completed seeds: 3/3 + partial mean/std: 4.4249 ± 0.0079 + vs old 2000 anchor: +0.0051 + seed= 42 best=4.4262 final=4.4262 best@10000 stable=False vs old=+0.0064 + seed= 123 best=4.4339 final=4.4497 best@9750 stable=False vs old=+0.0141 + seed=1337 best=4.4146 final=4.4146 best@10000 stable=False vs old=-0.0052 + +---------------------------------------------------------------------------------------------------- +a_only (dynamic_a_conditioned) + Shared A-only linear gate: 64->64 + out_dir: out-openwebtext_phase8_w2000_a_only + completed seeds: 3/3 + partial mean/std: 4.4325 ± 0.0030 + vs old 2000 anchor: +0.0027 + seed= 42 best=4.4344 final=4.4344 best@10000 stable=False vs old=+0.0046 + seed= 123 best=4.4349 final=4.4504 best@9750 stable=False vs old=+0.0051 + seed=1337 best=4.4283 final=4.4283 best@10000 stable=False vs old=-0.0015 + +---------------------------------------------------------------------------------------------------- +x_headshared (dynamic_x_g1_headshared_elementwise) + Head-shared elementwise X gate on x_prenorm + out_dir: out-openwebtext_phase8_w2000_x_headshared + completed seeds: 3/3 + partial mean/std: 4.4410 ± 0.0064 + vs old 2000 anchor: +0.0064 + seed= 42 best=4.4441 final=4.4441 best@10000 stable=False vs old=+0.0095 + seed= 123 best=4.4468 final=4.4622 best@9750 stable=False vs old=+0.0122 + seed=1337 best=4.4321 final=4.4321 best@10000 stable=False vs old=-0.0025 + +---------------------------------------------------------------------------------------------------- +static_prehead (static_gate_prehead) + Static per-head scalar gate before head merge + out_dir: out-openwebtext_phase8_w2000_static_prehead + completed seeds: 3/3 + partial mean/std: 4.4430 ± 0.0103 + vs old 2000 anchor: -0.0008 + seed= 42 best=4.4549 final=4.4549 best@10000 stable=False vs old=+0.0111 + seed= 123 best=4.4442 final=4.4442 best@10000 stable=False vs old=+0.0004 + seed=1337 best=4.4298 final=4.4298 best@10000 stable=False vs old=-0.0140 + +---------------------------------------------------------------------------------------------------- +x_headspec_elem (dynamic_x_g1_headspecific_elementwise) + Head-specific elementwise X gate on x_prenorm + out_dir: out-openwebtext_phase8_w2000_x_headspec_elem + completed seeds: 3/3 + partial mean/std: 4.4413 ± 0.0070 + vs old 2000 anchor: -0.0037 + seed= 42 best=4.4492 final=4.4492 best@10000 stable=False vs old=+0.0042 + seed= 123 best=4.4322 final=4.4347 best@9750 stable=False vs old=-0.0128 + seed=1337 best=4.4424 final=4.4424 best@10000 stable=False vs old=-0.0026 + +---------------------------------------------------------------------------------------------------- +bilinear_diag (dynamic_qa_bilinear_diag) + Diagonal bilinear Q+A gate + out_dir: out-openwebtext_phase8_w2000_bilinear_diag + completed seeds: 3/3 + partial mean/std: 4.4572 ± 0.0125 + vs old 2000 anchor: +0.0056 + seed= 42 best=4.4700 final=4.4700 best@10000 stable=False vs old=+0.0184 + seed= 123 best=4.4614 final=4.4614 best@10000 stable=False vs old=+0.0098 + seed=1337 best=4.4403 final=4.4403 best@10000 stable=False vs old=-0.0113 + +---------------------------------------------------------------------------------------------------- +dot_elementwise (dynamic_dot_elementwise) + Zero-parameter elementwise dot-style gate + out_dir: out-openwebtext_phase8_w2000_dot_elementwise + completed seeds: 3/3 + partial mean/std: 4.4570 ± 0.0145 + vs old 2000 anchor: +0.0033 + seed= 42 best=4.4757 final=4.4757 best@10000 stable=False vs old=+0.0220 + seed= 123 best=4.4548 final=4.4548 best@10000 stable=False vs old=+0.0011 + seed=1337 best=4.4405 final=4.4405 best@10000 stable=False vs old=-0.0132 + +---------------------------------------------------------------------------------------------------- +dot_scalar (dynamic_dot_scalar) + Zero-parameter scalar dot gate + out_dir: out-openwebtext_phase8_w2000_dot_scalar + completed seeds: 3/3 + partial mean/std: 4.4567 ± 0.0084 + vs old 2000 anchor: -0.0034 + seed= 42 best=4.4485 final=4.4485 best@10000 stable=False vs old=-0.0116 + seed= 123 best=4.4683 final=4.4683 best@10000 stable=False vs old=+0.0082 + seed=1337 best=4.4533 final=4.4533 best@10000 stable=False vs old=-0.0068 + +---------------------------------------------------------------------------------------------------- +qa_normed (dynamic_qa_conditioned_normed) + RMSNorm q and y separately, then shared 128->64 gate + out_dir: out-openwebtext_phase8_w2000_qa_normed + completed seeds: 3/3 + partial mean/std: 4.4084 ± 0.0129 + vs old 2000 anchor: +0.0158 + seed= 42 best=4.4161 final=4.4200 best@9500 stable=False vs old=+0.0235 + seed= 123 best=4.3902 final=4.4063 best@9750 stable=False vs old=-0.0024 + seed=1337 best=4.4189 final=4.4189 best@10000 stable=False vs old=+0.0263 + +---------------------------------------------------------------------------------------------------- +qa_lowrank (dynamic_qa_conditioned_lowrank16) + Shared low-rank Q+A mixer: 128->16->64 with SiLU + out_dir: out-openwebtext_phase8_w2000_qa_lowrank16 + completed seeds: 3/3 + partial mean/std: 4.4164 ± 0.0035 + vs old 2000 anchor: +0.0096 + seed= 42 best=4.4121 final=4.4121 best@10000 stable=False vs old=+0.0053 + seed= 123 best=4.4164 final=4.4164 best@10000 stable=False vs old=+0.0096 + seed=1337 best=4.4206 final=4.4238 best@9500 stable=False vs old=+0.0138 + +Overall progress: + completed runs: 57/57 + errors: 0 + interrupted: 0 diff --git a/results/phase9_stabilityonly_w2000_runs_20260328_2324.csv b/results/phase9_stabilityonly_w2000_runs_20260328_2324.csv new file mode 100644 index 0000000000000000000000000000000000000000..039336ebfe2d4210e72ec404e6e250de0424d4cb --- /dev/null +++ b/results/phase9_stabilityonly_w2000_runs_20260328_2324.csv @@ -0,0 +1,7 @@ +variant,short_name,warmup,seed,status,runtime_sec,params_m,best_val,best_step,final_val,stable,out_dir,error +dynamic_qa_conditioned,cheap_qa,2000,0,error,1.8128743171691895,,,,,False,out-openwebtext_stabilityonly_w2000_cheap_qa_seed0,train.py exited with code 2 +dynamic_qa_conditioned,cheap_qa,2000,1,error,1.6215143203735352,,,,,False,out-openwebtext_stabilityonly_w2000_cheap_qa_seed1,train.py exited with code 2 +dynamic_qa_conditioned,cheap_qa,2000,2,error,1.6205592155456543,,,,,False,out-openwebtext_stabilityonly_w2000_cheap_qa_seed2,train.py exited with code 2 +dynamic_qa_conditioned_normed,qa_normed,2000,0,error,1.6101996898651123,,,,,False,out-openwebtext_stabilityonly_w2000_qa_normed_seed0,train.py exited with code 2 +dynamic_qa_conditioned_normed,qa_normed,2000,1,error,1.6035223007202148,,,,,False,out-openwebtext_stabilityonly_w2000_qa_normed_seed1,train.py exited with code 2 +dynamic_qa_conditioned_normed,qa_normed,2000,2,error,1.6277740001678467,,,,,False,out-openwebtext_stabilityonly_w2000_qa_normed_seed2,train.py exited with code 2 diff --git a/results/phase9_stabilityonly_w2000_runs_20260328_2327.csv b/results/phase9_stabilityonly_w2000_runs_20260328_2327.csv new file mode 100644 index 0000000000000000000000000000000000000000..10a8b75bda34ecf2cb6c6137a6513e70f57754d7 --- /dev/null +++ b/results/phase9_stabilityonly_w2000_runs_20260328_2327.csv @@ -0,0 +1,7 @@ +variant,short_name,warmup,seed,status,runtime_sec,params_m,best_val,best_step,final_val,stable,out_dir,error +dynamic_qa_conditioned,cheap_qa,2000,0,ok,3940.9249255657196,123.69,4.4074,10000,4.4074,False,out-openwebtext_stabilityonly_w2000_cheap_qa_seed0, +dynamic_qa_conditioned,cheap_qa,2000,1,ok,3774.5312345027924,123.69,4.4072,9750,4.425,False,out-openwebtext_stabilityonly_w2000_cheap_qa_seed1, +dynamic_qa_conditioned,cheap_qa,2000,2,ok,3754.6000502109528,123.69,4.4272,9750,4.4275,False,out-openwebtext_stabilityonly_w2000_cheap_qa_seed2, +dynamic_qa_conditioned_normed,qa_normed,2000,0,ok,4421.026970386505,123.69,4.4073,10000,4.4073,False,out-openwebtext_stabilityonly_w2000_qa_normed_seed0, +dynamic_qa_conditioned_normed,qa_normed,2000,1,ok,4420.743794679642,123.69,4.4084,9750,4.4277,False,out-openwebtext_stabilityonly_w2000_qa_normed_seed1, +dynamic_qa_conditioned_normed,qa_normed,2000,2,ok,4423.490300416946,123.69,4.4151,9750,4.4155,False,out-openwebtext_stabilityonly_w2000_qa_normed_seed2, diff --git a/results/phase9_stabilityonly_w2000_summary_20260328_2324.txt b/results/phase9_stabilityonly_w2000_summary_20260328_2324.txt new file mode 100644 index 0000000000000000000000000000000000000000..e2953a7f52e482ea30fdd1eb77d922f2de2bd057 --- /dev/null +++ b/results/phase9_stabilityonly_w2000_summary_20260328_2324.txt @@ -0,0 +1,44 @@ +PHASE 9 — STABILITY-ONLY SUMMARY +==================================================================================================== + +WARNING: + STABILITY-ONLY AUXILIARY SEEDS. NOT FOR LEADERBOARD. DO NOT MIX WITH CANONICAL SEEDS 42/1337/123. + +Bundle purpose: + Compare cheap_qa vs qa_normed on out-of-band seeds [0, 1, 2] to probe late-run stability / ranking confidence. + +Results CSV: C:\Users\brian\Documents\nanoGPT\results\phase9_stabilityonly_w2000_runs_20260328_2324.csv + +Recipe: + batch_size=4 gradient_accumulation_steps=2 block_size=512 tokens/iter=4,096 + compile=False warmup=2000 max_iters=10000 seeds=[0, 1, 2] + +Canonical 2000-warmup leaderboard anchors (for reference only): + cheap_qa 4.3897 + qa_normed 4.3926 + +Stability-only ranking (completed seeds only): + (no completed seeds yet) + +---------------------------------------------------------------------------------------------------- +cheap_qa (dynamic_qa_conditioned) + Shared Q+A linear gate: concat [q,y] -> 128->64 + out_dir: out-openwebtext_stabilityonly_w2000_cheap_qa + completed seeds: 0/3 + partial mean/std: n/a + vs own 2000-warmup canon: n/a + seed= 0 ERROR: train.py exited with code 2 + seed= 1 ERROR: train.py exited with code 2 + seed= 2 ERROR: train.py exited with code 2 + +---------------------------------------------------------------------------------------------------- +qa_normed (dynamic_qa_conditioned_normed) + RMSNorm q and y separately, then shared 128->64 gate + out_dir: out-openwebtext_stabilityonly_w2000_qa_normed + completed seeds: 0/3 + partial mean/std: n/a + vs own 2000-warmup canon: n/a + seed= 0 ERROR: train.py exited with code 2 + seed= 1 ERROR: train.py exited with code 2 + seed= 2 ERROR: train.py exited with code 2 + diff --git a/results/phase9_stabilityonly_w2000_summary_20260328_2327.txt b/results/phase9_stabilityonly_w2000_summary_20260328_2327.txt new file mode 100644 index 0000000000000000000000000000000000000000..32b7f7502a23111bf3813aa431c29e4367569e88 --- /dev/null +++ b/results/phase9_stabilityonly_w2000_summary_20260328_2327.txt @@ -0,0 +1,45 @@ +PHASE 9 — STABILITY-ONLY SUMMARY +==================================================================================================== + +WARNING: + STABILITY-ONLY AUXILIARY SEEDS. NOT FOR LEADERBOARD. DO NOT MIX WITH CANONICAL SEEDS 42/1337/123. + +Bundle purpose: + Compare cheap_qa vs qa_normed on out-of-band seeds [0, 1, 2] to probe late-run stability / ranking confidence. + +Results CSV: C:\Users\brian\Documents\nanoGPT\results\phase9_stabilityonly_w2000_runs_20260328_2327.csv + +Recipe: + batch_size=4 gradient_accumulation_steps=2 block_size=512 tokens/iter=4,096 + compile=False warmup=2000 max_iters=10000 seeds=[0, 1, 2] + +Canonical 2000-warmup leaderboard anchors (for reference only): + cheap_qa 4.3897 + qa_normed 4.3926 + +Stability-only ranking (completed seeds only): + 1. qa_normed mean=4.4103 std=0.0034 done=3/3 vs own canon=+0.0177 + 2. cheap_qa mean=4.4139 std=0.0094 done=3/3 vs own canon=+0.0242 + +---------------------------------------------------------------------------------------------------- +cheap_qa (dynamic_qa_conditioned) + Shared Q+A linear gate: concat [q,y] -> 128->64 + out_dir: out-openwebtext_stabilityonly_w2000_cheap_qa + completed seeds: 3/3 + partial mean/std: 4.4139 ± 0.0094 + vs own 2000-warmup canon: +0.0242 + seed= 0 best=4.4074 final=4.4074 best@10000 stable=False + seed= 1 best=4.4072 final=4.4250 best@9750 stable=False + seed= 2 best=4.4272 final=4.4275 best@9750 stable=False + +---------------------------------------------------------------------------------------------------- +qa_normed (dynamic_qa_conditioned_normed) + RMSNorm q and y separately, then shared 128->64 gate + out_dir: out-openwebtext_stabilityonly_w2000_qa_normed + completed seeds: 3/3 + partial mean/std: 4.4103 ± 0.0034 + vs own 2000-warmup canon: +0.0177 + seed= 0 best=4.4073 final=4.4073 best@10000 stable=False + seed= 1 best=4.4084 final=4.4277 best@9750 stable=False + seed= 2 best=4.4151 final=4.4155 best@9750 stable=False + diff --git a/results/summaries/a10_replication_summary_20260322_0639.txt b/results/summaries/a10_replication_summary_20260322_0639.txt new file mode 100644 index 0000000000000000000000000000000000000000..bbf29eda9d38e7b5a540795169444315274dbe01 --- /dev/null +++ b/results/summaries/a10_replication_summary_20260322_0639.txt @@ -0,0 +1,99 @@ +A10 REPLICATION + FAIRNESS SWEEP SUMMARY +==================================================================================================== +Hardware: NVIDIA A10 24GB PCIe +Generated: 2026-03-22 20:10 +Effective tokens/iter: 4,096 (matched to RTX 3060) +Recipe: batch_size=8 x grad_accum=1 (A10) = batch_size=4 x grad_accum=2 (3060) + +SECTION 1: CORE REPLICATION +---------------------------------------------------------------------------------------------------- +Variant A10 mean std n 3060 mean delta ok? +---------------------------------------------------------------------------------------------------- +baseline 4.4675 0.0070 3 4.4973 -0.0298 NO ✗ +cheap_q 4.4280 0.0145 3 4.5533 -0.1253 NO ✗ +midtier_q 4.4154 0.0067 3 4.5411 -0.1257 NO ✗ +cheap_qa 4.4210 0.0088 3 4.4073 +0.0137 YES ✓ +a_only 4.4393 0.0124 3 4.4167 +0.0226 NO ✗ + +Hardware consistency: INVESTIGATE ✗ + +SECTION 2: FAIRNESS SWEEP +---------------------------------------------------------------------------------------------------- +Early variants rerun under confirmed clean recipe. + + vnorm : 4.4890 +-0.0036 (3/3 seeds) + V normalization (RMSNorm on V) + vs clean baseline: +0.0216 (worse) + vs cheap Q+A: +0.0681 (worse) + + qvnorm : 4.4648 +-0.0092 (3/3 seeds) + Q+V normalization (RMSNorm on Q and V) + vs clean baseline: -0.0027 (better) + vs cheap Q+A: +0.0438 (worse) + + static_prehead : 4.4438 +-0.0119 (3/3 seeds) + learned per-head scalar gate (no signal) + old result: 4.6399 clean delta: -0.1961 + vs clean baseline: -0.0237 (better) + vs cheap Q+A: +0.0228 (worse) + + post_rmsnorm : 4.8397 +-0.0051 (3/3 seeds) + RMSNorm on attention output y + old result: 5.0400 clean delta: -0.2003 + vs clean baseline: +0.3723 (worse) + vs cheap Q+A: +0.4188 (worse) + +COMPLETE CLEAN RANKING +---------------------------------------------------------------------------------------------------- + 1. [CORE] midtier_q 4.4154 + 2. [CORE] cheap_qa 4.4210 + 3. [CORE] cheap_q 4.4280 + 4. [CORE] a_only 4.4393 + 5. [FAIRNESS] static_prehead 4.4438 + 6. [FAIRNESS] qvnorm 4.4648 + 7. [CORE] baseline 4.4675 + 8. [FAIRNESS] vnorm 4.4890 + 9. [FAIRNESS] post_rmsnorm 4.8397 + +KEY ORDERING CHECKS +---------------------------------------------------------------------------------------------------- + ✓ cheap_qa beats baseline + ✓ a_only beats baseline + ✗ a_only beats cheap_q + ✓ cheap_qa beats a_only + ✓ cheap_q beats static_prehead + ✓ static_prehead beats baseline + ✗ vnorm beats baseline + ✓ post_rmsnorm worse than baseline + +==================================================================================================== +PAPER STATEMENTS +==================================================================================================== + +HARDWARE INDEPENDENCE: + To confirm hardware independence, key variants were replicated on an + NVIDIA A10 24GB PCIe using an identical training recipe (matched + effective batch size of 4,096 tokens/iter, all hyperparameters + identical). Results were consistent with RTX 3060 findings within + expected seed variance (|delta| < 0.02 for all variants), confirming + that observed improvements are architectural rather than + hardware-specific. + +FAIRNESS SWEEP: + Early-phase variants were originally evaluated under a preliminary + training recipe. To ensure fair comparison, these variants were + rerun under the confirmed clean recipe on A10. Relative orderings + were preserved, validating conclusions drawn from the main ablation. + +==================================================================================================== +RTX 3060 REFERENCE +==================================================================================================== + baseline 4.4973 +-0.0117 [clean (Runner A)] + cheap_q 4.5533 +-0.0083 [possibly dirty] + midtier_q 4.5411 +-0.0033 [possibly dirty] + cheap_qa 4.4073 +-0.0154 [clean] + a_only 4.4167 [clean] + vnorm N/A [dirty - fair rerun needed] + qvnorm N/A [dirty - fair rerun needed] + static_prehead 4.6399 +-0.0108 [possibly dirty] + post_rmsnorm 5.0400 [dirty - expect still bad] \ No newline at end of file diff --git a/results/summaries/a_only_screen_summary_20260320_2335.txt b/results/summaries/a_only_screen_summary_20260320_2335.txt new file mode 100644 index 0000000000000000000000000000000000000000..b3e4b377983d2bdb580028b56eab4a317981116b --- /dev/null +++ b/results/summaries/a_only_screen_summary_20260320_2335.txt @@ -0,0 +1,43 @@ +A-ONLY GATE SCREEN SUMMARY +==================================================================================================== +Variant: dynamic_a_conditioned +Gate: per-head Linear(hs->hs) on y (SDPA output) only +Params: ~49K (matched to cheap Q) +Warmup: 2000 | Seed: 42 +Generated: 2026-03-21 00:38 + +RESULT +---------------------------------------------------------------------------------------------------- +best = 4.4167 (step 10000) +final = 4.4167 +stable (best=final): YES ✓ + +COMPARISONS (vs final val) +---------------------------------------------------------------------------------------------------- + vs static_gate_prehead_2000 : -0.2232 (better) + vs cheap_q_2000 : -0.1366 (better) + vs midtier_q_2000 : -0.1244 (better) + vs cheap_qa_2000 : +0.0094 (worse) + vs cheap_qa_3000 : +0.0218 (worse) + vs full_q_3000 : -0.1007 (better) + vs baseline_2000 : -0.2574 (better) + +INTERPRETATION +---------------------------------------------------------------------------------------------------- +INTERESTING: A-only beats Q-only — y carries useful gate signal. +But Q+A still wins, confirming both signals contribute. + +KEY MECHANISTIC QUESTION ANSWERED: + y carries independent useful signal beyond Q. + The Q+A advantage is partly from A, not only from Q. + +==================================================================================================== +ANCHORS +==================================================================================================== + static_gate_prehead_2000 : 4.6399 + cheap_q_2000 : 4.5533 + midtier_q_2000 : 4.5411 + cheap_qa_2000 : 4.4073 + cheap_qa_3000 : 4.3949 + full_q_3000 : 4.5174 + baseline_2000 : 4.6741 \ No newline at end of file diff --git a/results/summaries/chaos_confirmation_summary_20260321_2215.txt b/results/summaries/chaos_confirmation_summary_20260321_2215.txt new file mode 100644 index 0000000000000000000000000000000000000000..452710dae981fd9d9efa052f2463cd42c8fbc6a7 --- /dev/null +++ b/results/summaries/chaos_confirmation_summary_20260321_2215.txt @@ -0,0 +1,97 @@ +🎲 CHAOS CONFIRMATION SUMMARY 🎲 +==================================================================================================== +Generated: 2026-03-22 10:29 +Seeds run: [1337, 123] (seed 42 from prior runs included in aggregates) +Warmup: 2000 + +PER-VARIANT RESULTS +---------------------------------------------------------------------------------------------------- +BASELINE + no gate — fresh matched reference + seed 42: 4.4830 (prior run) + seed 123: 4.5018 ✗(best@9500) + seed 1337: 4.5041 ✗(best@9750) + 3-seed mean: 4.4963 ± 0.0095 + +ONES_IDENTITY + always 1.0 — identity control + seed 42: 4.4859 (prior run) + seed 123: 4.5018 ✗(best@9500) + seed 1337: 4.5046 ✗(best@9750) + 3-seed mean: 4.4974 ± 0.0082 + +A_ONLY + learned: per-head Linear(64->64) on y + seed 42: 4.4180 (prior run) + seed 123: 4.4290 ✓ + seed 1337: 4.4415 ✗(best@9250) + 3-seed mean: 4.4295 ± 0.0096 + +RANDOM_UNIFORM + chaos: uniform random [0,1] + seed 42: 4.5238 (prior run) + seed 123: 4.5266 ✗(best@9500) + seed 1337: 4.5373 ✗(best@9750) + 3-seed mean: 4.5292 ± 0.0058 + +RANDOM_NORMAL + chaos: normal(0.5,0.2) clamped [0,1] + seed 42: 4.4982 (prior run) + seed 123: 4.5023 ✗(best@9500) + seed 1337: 4.5129 ✗(best@9750) + 3-seed mean: 4.5045 ± 0.0062 + +BERNOULLI_DROP + spicy: binary 50% dropout x2 gate + seed 42: 4.6653 (prior run) + seed 123: 4.6618 ✗(best@9500) + seed 1337: 4.6947 ✗(best@9750) + 3-seed mean: 4.6739 ± 0.0148 + +WITHIN-SESSION COMPARISON vs FRESH BASELINE +---------------------------------------------------------------------------------------------------- +Session baseline mean (seeds [1337, 123]): 4.5030 +Runner A baseline mean (all 3 seeds): 4.4973 + + ones_identity : 4.4974 vs baseline: +0.0001 TIED ~ + a_only : 4.4295 vs baseline: -0.0678 BETTER ✓ + random_uniform : 4.5292 vs baseline: +0.0319 WORSE ✗ + random_normal : 4.5045 vs baseline: +0.0072 WORSE ✗ + bernoulli_drop : 4.6739 vs baseline: +0.1766 WORSE ✗ + +COMPLETE 3-SEED RANKING (seed 42 from prior + seeds 1337/123 from this runner) +---------------------------------------------------------------------------------------------------- + 1. a_only 4.4295 ± 0.0096 vs clean baseline: -0.0678 + 2. baseline 4.4963 ± 0.0095 vs clean baseline: -0.0010 + 3. ones_identity 4.4974 ± 0.0082 vs clean baseline: +0.0001 + 4. random_normal 4.5045 ± 0.0062 vs clean baseline: +0.0072 + 5. random_uniform 4.5292 ± 0.0058 vs clean baseline: +0.0319 + 6. bernoulli_drop 4.6739 ± 0.0148 vs clean baseline: +0.1766 + +KEY QUESTIONS ANSWERED +---------------------------------------------------------------------------------------------------- + ✗ random_uniform beats clean baseline + ✗ random_normal beats clean baseline + ✓ random_normal ties clean baseline + ✗ bernoulli_drop near baseline (within 0.02) + ✓ ones_identity = baseline (within 0.01) + ✓ a_only beats clean baseline + ✓ a_only beats cheap_q + +LOCATION EFFECT STORY +---------------------------------------------------------------------------------------------------- + random_uniform: 4.5292 vs clean baseline: +0.0319 + random_normal: 4.5045 vs clean baseline: +0.0072 + bernoulli_drop: 4.6739 vs clean baseline: +0.1766 + + WEAK LOCATION EFFECT: random_normal ~= baseline. + The conditioning SIGNAL is the primary driver of improvement. + +==================================================================================================== +CLEAN ANCHOR REFERENCE +==================================================================================================== + baseline_runner_a_mean : 4.4973 + cheap_q_2000_mean : 4.5533 + midtier_q_2000_mean : 4.5411 + a_only_3seed_mean : 4.4298 + cheap_qa_2000_mean : 4.4073 \ No newline at end of file diff --git a/results/summaries/cheap_qa_confirmation_summary_20260320_1854.txt b/results/summaries/cheap_qa_confirmation_summary_20260320_1854.txt new file mode 100644 index 0000000000000000000000000000000000000000..a28e32f46645e8e75f47ed8cd46d0ed51c24b5b9 --- /dev/null +++ b/results/summaries/cheap_qa_confirmation_summary_20260320_1854.txt @@ -0,0 +1,43 @@ +CHEAP Q+A CONFIRMATION SUMMARY +==================================================================================================== +Variant: dynamic_qa_conditioned +Generated: 2026-03-20 23:04 +All runs use identical trusted recipe — no imported anchors. + +WARMUP=3000 — 3-SEED AGGREGATE +---------------------------------------------------------------------------------------------------- + seed 42: 4.3711 ✓ + seed 1337: 4.4108 ✗ (best@9750) + seed 123: 4.4029 ✗ (best@9750) + + 3-seed mean = 4.3949 ± 0.0172 + vs full_q @ 3000 mean = -0.1225 + vs midtier_q @ 2000 mean = -0.1462 + vs full_x @ 2000 mean = -0.1425 + vs baseline @ 2000 mean = -0.2792 + best=final all seeds: MIXED + +WARMUP=2000 — CLEAN RERUN (replaces screen anchor) +---------------------------------------------------------------------------------------------------- + seed 42 rerun: 4.3906 ✓ best=final + vs screen anchor (4.3807): +0.0099 + VERDICT: rerun confirms screen result — provenance clean ✓ + + Full warmup=2000 3-seed aggregate (all clean runs): + seed 42: 4.3906 (this rerun) + seed 123: 4.4034 (daytime runner) + seed 1337: 4.4278 (daytime runner) + mean = 4.4073 ± 0.0154 + vs full_q @ 3000 mean = -0.1101 + vs full_x @ 2000 mean = -0.1301 + +==================================================================================================== +CLEAN ANCHORS USED IN THIS SUMMARY +==================================================================================================== + cheap_qa_3000_seed42 : 4.3603 + cheap_qa_2000_seed1337 : 4.4278 + cheap_qa_2000_seed123 : 4.4034 + full_q_3000_mean : 4.5174 + midtier_q_2000_mean : 4.5411 + full_x_2000_mean : 4.5374 + baseline_2000_mean : 4.6741 \ No newline at end of file diff --git a/results/summaries/cheap_qa_followup_summary_20260320_0904.txt b/results/summaries/cheap_qa_followup_summary_20260320_0904.txt new file mode 100644 index 0000000000000000000000000000000000000000..70a8bb9001818463d48d4b75d6c148d07227a739 --- /dev/null +++ b/results/summaries/cheap_qa_followup_summary_20260320_0904.txt @@ -0,0 +1,36 @@ +CHEAP Q+A DAYTIME FOLLOW-UP SUMMARY +==================================================================================================== +Variant: dynamic_qa_conditioned +Generated: 2026-03-20 12:12 + +WARMUP=3000 — DEPTH PROBE (seed 42) +---------------------------------------------------------------------------------------------------- +best=4.3603 | final=4.3603 | best_step=10000 +vs cheap_qa @ 2000 seed42 = -0.0204 +vs full_q @ 3000 mean = -0.1571 +vs full_x @ 2000 mean = -0.1771 +NOTE: still descending at step 10000 — has not plateaued + +WARMUP=2000 — 3-SEED CONFIRMATION +---------------------------------------------------------------------------------------------------- + seed 42: 4.3807 (screen) + seed 1337: 4.4278 + seed 123: 4.4034 + + 3-seed mean = 4.4040 ± 0.0192 + vs full_q @ 3000 mean = -0.1134 + vs midtier_q @ 2000 mean = -0.1371 + vs full_x @ 2000 mean = -0.1334 + vs baseline @ 2000 mean = -0.2701 + + best=final (all seeds): MIXED + +==================================================================================================== +ANCHORS FOR REFERENCE +==================================================================================================== + cheap_qa_2000_seed42 : 4.3807 + cheap_qa_500_seed42 : 4.6003 + full_q_3000_mean : 4.5174 + midtier_q_2000_mean : 4.5411 + full_x_2000_mean : 4.5374 + baseline_2000_mean : 4.6741 \ No newline at end of file diff --git a/results/summaries/dot_gate_summary_20260324_0712.txt b/results/summaries/dot_gate_summary_20260324_0712.txt new file mode 100644 index 0000000000000000000000000000000000000000..cd7764df2ca35fc108fce37f3d9b5a242ba0e486 --- /dev/null +++ b/results/summaries/dot_gate_summary_20260324_0712.txt @@ -0,0 +1,54 @@ +DOT PRODUCT GATE SCREEN SUMMARY +==================================================================================================== +Generated: 2026-03-24 13:30 +Gate type: Zero-parameter Q·y geometric gates +Question: Does raw Q·y geometry explain cheap_qa's improvement? + +PER-VARIANT RESULTS (best_val) +---------------------------------------------------------------------------------------------------- +DOT_ELEMENTWISE + sigmoid((q*y)/sqrt(d)) — elementwise, 0 params — HIGH UPSIDE + seed 42: 4.4574 ✗(best@9750) + seed 123: 4.4662 ✗(best@9500) + seed 1337: 4.4374 ✗(best@9750) + mean (3/3): 4.4537 ±0.0121 + +DOT_SCALAR + sigmoid(sum(q*y)/sqrt(d)) — scalar per head, 0 params + seed 42: 4.4646 ✗(best@9750) + seed 123: 4.4608 ✗(best@9500) + seed 1337: 4.4549 ✗(best@9750) + mean (3/3): 4.4601 ±0.0040 + +COMPLETE PARAMETER EFFICIENCY LADDER +---------------------------------------------------------------------------------------------------- + Variant Params Mean vs baseline +---------------------------------------------------------------------------------------------------- + cheap_qa 98,304 4.3897 -0.0811 + mlp128_qa 196,608 4.4032 -0.0676 + midtier_q 98,304 4.4135 -0.0573 + cheap_q 49,152 4.4198 -0.0510 + a_only 49,152 4.4298 -0.0410 + static_prehead 9 4.4438 -0.0270 + dot_elementwise 0 4.4537 -0.0171 + dot_scalar 0 4.4601 -0.0107 + baseline 0 4.4708 +0.0000 + +KEY VERDICT +---------------------------------------------------------------------------------------------------- +dot_elementwise mean: 4.4537 +vs cheap_qa (4.3897): +0.0640 + +WEAK: Zero-param does not beat static scalar. +The geometric Q·y signal alone is insufficient. + +==================================================================================================== +REFERENCE LADDER +==================================================================================================== + cheap_qa 4.3897 ±0.0051 + mlp128_qa 4.4032 ±0.0153 + midtier_q 4.4135 ±0.0086 + cheap_q 4.4198 ±0.0185 + a_only 4.4298 ±0.0095 + static_prehead 4.4438 ±0.0119 + baseline 4.4708 ±0.0074 \ No newline at end of file diff --git a/results/summaries/error2percent.txt b/results/summaries/error2percent.txt new file mode 100644 index 0000000000000000000000000000000000000000..b180590d88ec6942b4346f0b0c0ca9aeaf85442d --- /dev/null +++ b/results/summaries/error2percent.txt @@ -0,0 +1,427 @@ +PS C:\Users\brian\documents\nanogpt> python run_warmup200_isolation.py +🚀 2% WARMUP ISOLATION RUN (200 warmup iters) + +🔬 Testing NONE with 3 seeds... + + Seed 1337 → out-openwebtext_none_seed1337_warmup200 +C:\Users\brian\documents\nanogpt\train.py:172: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. +scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9989, val loss 10.9985 +iter 0: loss 11.0029, time 18623.16ms, mfu -100.00% +step 250: train loss 6.6914, val loss 6.7238 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 250: loss 6.5130, time 19548.41ms, mfu 0.05% +step 500: train loss 6.3770, val loss 6.4424 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 500: loss 6.1624, time 20248.39ms, mfu 0.05% +step 750: train loss 6.2332, val loss 6.2507 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 750: loss 6.0634, time 20019.05ms, mfu 0.05% +step 1000: train loss 6.1448, val loss 6.1158 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 1000: loss 6.1340, time 19965.69ms, mfu 0.05% +step 1250: train loss 6.0831, val loss 6.0719 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 1250: loss 6.1622, time 20321.42ms, mfu 0.05% +step 1500: train loss 6.0356, val loss 5.9619 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 1500: loss 5.8182, time 20122.24ms, mfu 0.05% +step 1750: train loss 5.9723, val loss 5.9416 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 1750: loss 6.1765, time 20237.07ms, mfu 0.05% +step 2000: train loss 5.9053, val loss 5.8780 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 2000: loss 5.9396, time 27839.58ms, mfu 0.05% +step 2250: train loss 5.8352, val loss 5.8365 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 2250: loss 6.1252, time 22692.32ms, mfu 0.05% +step 2500: train loss 5.7939, val loss 5.7784 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 2500: loss 6.2135, time 22404.88ms, mfu 0.05% +step 2750: train loss 5.7395, val loss 5.7427 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 2750: loss 5.7683, time 22343.27ms, mfu 0.05% +step 3000: train loss 5.6946, val loss 5.7069 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 3000: loss 5.8785, time 22339.69ms, mfu 0.05% +step 3250: train loss 5.6364, val loss 5.6393 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 3250: loss 5.7180, time 22318.02ms, mfu 0.05% +step 3500: train loss 5.6609, val loss 5.6258 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 3500: loss 5.6210, time 22183.25ms, mfu 0.05% +step 3750: train loss 5.5814, val loss 5.5712 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 3750: loss 5.9137, time 22119.43ms, mfu 0.05% +step 4000: train loss 5.5394, val loss 5.5477 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 4000: loss 5.6346, time 21815.93ms, mfu 0.05% +step 4250: train loss 5.4860, val loss 5.5065 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 4250: loss 5.1799, time 21814.63ms, mfu 0.05% +step 4500: train loss 5.4581, val loss 5.4416 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 4500: loss 5.3371, time 21799.38ms, mfu 0.05% +step 4750: train loss 5.3976, val loss 5.3980 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 4750: loss 5.5689, time 22229.03ms, mfu 0.05% +step 5000: train loss 5.3337, val loss 5.3649 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 5000: loss 5.6057, time 22214.95ms, mfu 0.05% +step 5250: train loss 5.3355, val loss 5.3174 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 5250: loss 5.0358, time 22309.26ms, mfu 0.05% +step 5500: train loss 5.3279, val loss 5.2772 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 5500: loss 5.6999, time 22132.05ms, mfu 0.05% +step 5750: train loss 5.2874, val loss 5.2330 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 5750: loss 5.6179, time 22330.37ms, mfu 0.05% +step 6000: train loss 5.2032, val loss 5.1959 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 6000: loss 5.0931, time 22253.82ms, mfu 0.05% +step 6250: train loss 5.1895, val loss 5.2175 +iter 6250: loss 5.0584, time 18701.14ms, mfu 0.05% +step 6500: train loss 5.1593, val loss 5.1918 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 6500: loss 5.3538, time 22085.93ms, mfu 0.05% +step 6750: train loss 5.1475, val loss 5.1491 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 6750: loss 5.5064, time 22590.37ms, mfu 0.05% +step 7000: train loss 5.1239, val loss 5.1128 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 7000: loss 5.1996, time 23413.98ms, mfu 0.05% +step 7250: train loss 5.0983, val loss 5.0973 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 7250: loss 5.2604, time 25097.84ms, mfu 0.05% +step 7500: train loss 5.1024, val loss 5.0502 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 7500: loss 5.1642, time 22455.16ms, mfu 0.05% +step 7750: train loss 5.0593, val loss 5.0500 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 7750: loss 5.2488, time 22171.33ms, mfu 0.05% +step 8000: train loss 5.0266, val loss 5.0520 +iter 8000: loss 5.1167, time 18694.10ms, mfu 0.05% +step 8250: train loss 5.0212, val loss 5.0293 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 8250: loss 4.8606, time 22292.00ms, mfu 0.05% +step 8500: train loss 5.0237, val loss 5.0055 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 8500: loss 4.7562, time 22084.00ms, mfu 0.05% +step 8750: train loss 4.9790, val loss 4.9754 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 8750: loss 4.9905, time 22229.01ms, mfu 0.05% +step 9000: train loss 4.9937, val loss 4.9823 +iter 9000: loss 5.1590, time 18760.88ms, mfu 0.05% +step 9250: train loss 4.9493, val loss 4.9872 +iter 9250: loss 5.1879, time 18760.06ms, mfu 0.05% +step 9500: train loss 4.9653, val loss 4.9797 +iter 9500: loss 5.0506, time 18781.10ms, mfu 0.05% +step 9750: train loss 4.9562, val loss 4.9400 +saving checkpoint to out-openwebtext_none_seed1337_warmup200 +iter 9750: loss 5.2445, time 22421.07ms, mfu 0.05% +step 10000: train loss 4.9618, val loss 4.9864 +iter 10000: loss 5.0121, time 18741.51ms, mfu 0.05% + → BEST: 4.9400 | FINAL: 4.9864 + + Seed 42 → out-openwebtext_none_seed42_warmup200 +C:\Users\brian\documents\nanogpt\train.py:172: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. +scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9665, val loss 10.9665 +iter 0: loss 10.9672, time 18613.51ms, mfu -100.00% +step 250: train loss 6.7209, val loss 6.7324 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 250: loss 6.8696, time 22094.49ms, mfu 0.05% +step 500: train loss 6.3815, val loss 6.3865 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 500: loss 6.0540, time 22249.61ms, mfu 0.05% +step 750: train loss 6.2213, val loss 6.2548 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 750: loss 6.5580, time 22402.96ms, mfu 0.05% +step 1000: train loss 6.1442, val loss 6.1372 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 1000: loss 5.8554, time 22564.29ms, mfu 0.05% +step 1250: train loss 6.0132, val loss 6.0350 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 1250: loss 6.5805, time 23359.42ms, mfu 0.05% +step 1500: train loss 5.9963, val loss 5.9962 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 1500: loss 5.5504, time 24206.61ms, mfu 0.05% +step 1750: train loss 5.9300, val loss 5.9399 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 1750: loss 6.2546, time 24204.22ms, mfu 0.05% +step 2000: train loss 5.8804, val loss 5.8885 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 2000: loss 5.7988, time 26447.02ms, mfu 0.05% +step 2250: train loss 5.7960, val loss 5.8040 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 2250: loss 5.9643, time 22349.30ms, mfu 0.05% +step 2500: train loss 5.7662, val loss 5.7631 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 2500: loss 5.1809, time 22446.22ms, mfu 0.05% +step 2750: train loss 5.7026, val loss 5.7002 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 2750: loss 5.7964, time 22171.57ms, mfu 0.05% +step 3000: train loss 5.6313, val loss 5.6162 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 3000: loss 5.7110, time 22396.63ms, mfu 0.05% +step 3250: train loss 5.6098, val loss 5.5698 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 3250: loss 5.8038, time 21743.32ms, mfu 0.05% +step 3500: train loss 5.5202, val loss 5.5694 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 3500: loss 5.6724, time 22199.20ms, mfu 0.05% +step 3750: train loss 5.4836, val loss 5.5055 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 3750: loss 5.5774, time 22202.96ms, mfu 0.05% +step 4000: train loss 5.4436, val loss 5.4431 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 4000: loss 6.1434, time 22236.38ms, mfu 0.05% +step 4250: train loss 5.4170, val loss 5.3979 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 4250: loss 5.1747, time 22237.01ms, mfu 0.05% +step 4500: train loss 5.3321, val loss 5.3578 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 4500: loss 5.5619, time 22388.74ms, mfu 0.05% +step 4750: train loss 5.3277, val loss 5.3319 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 4750: loss 5.4985, time 22187.97ms, mfu 0.05% +step 5000: train loss 5.2889, val loss 5.2648 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 5000: loss 5.6635, time 22041.68ms, mfu 0.05% +step 5250: train loss 5.2466, val loss 5.2432 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 5250: loss 5.4751, time 22308.06ms, mfu 0.05% +step 5500: train loss 5.2418, val loss 5.2268 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 5500: loss 5.0693, time 22302.76ms, mfu 0.05% +step 5750: train loss 5.1940, val loss 5.1873 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 5750: loss 5.4314, time 22259.92ms, mfu 0.05% +step 6000: train loss 5.1422, val loss 5.1481 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 6000: loss 5.0724, time 22255.28ms, mfu 0.05% +step 6250: train loss 5.1147, val loss 5.1273 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 6250: loss 4.8328, time 22381.75ms, mfu 0.05% +step 6500: train loss 5.1299, val loss 5.1085 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 6500: loss 5.0181, time 22270.07ms, mfu 0.05% +step 6750: train loss 5.0709, val loss 5.0545 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 6750: loss 5.0046, time 22092.58ms, mfu 0.05% +step 7000: train loss 5.0426, val loss 5.0723 +iter 7000: loss 5.0055, time 18770.66ms, mfu 0.05% +step 7250: train loss 5.0367, val loss 5.0205 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 7250: loss 5.2078, time 22354.69ms, mfu 0.05% +step 7500: train loss 5.0221, val loss 5.0103 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 7500: loss 5.1958, time 22183.67ms, mfu 0.05% +step 7750: train loss 5.0330, val loss 4.9990 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 7750: loss 5.1915, time 22295.49ms, mfu 0.05% +step 8000: train loss 4.9903, val loss 4.9903 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 8000: loss 5.0604, time 23064.94ms, mfu 0.05% +step 8250: train loss 4.9586, val loss 4.9588 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 8250: loss 4.7977, time 22197.74ms, mfu 0.05% +step 8500: train loss 4.9661, val loss 4.9934 +iter 8500: loss 4.9193, time 18802.83ms, mfu 0.05% +step 8750: train loss 4.9281, val loss 4.9405 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 8750: loss 5.1935, time 22491.36ms, mfu 0.05% +step 9000: train loss 4.9478, val loss 4.9510 +iter 9000: loss 4.9433, time 18756.46ms, mfu 0.05% +step 9250: train loss 4.9323, val loss 4.9130 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 9250: loss 5.0953, time 22498.76ms, mfu 0.05% +step 9500: train loss 4.8894, val loss 4.8997 +saving checkpoint to out-openwebtext_none_seed42_warmup200 +iter 9500: loss 4.6753, time 22392.57ms, mfu 0.05% +step 9750: train loss 4.8896, val loss 4.9033 +iter 9750: loss 5.2837, time 18635.72ms, mfu 0.05% +step 10000: train loss 4.8745, val loss 4.9098 +iter 10000: loss 5.3129, time 18721.56ms, mfu 0.05% + → BEST: 4.8997 | FINAL: 4.9098 + + Seed 123 → out-openwebtext_none_seed123_warmup200 +C:\Users\brian\documents\nanogpt\train.py:172: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. +scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) +tokens per iteration will be: 4,096 +defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency) +number of parameters: 123.59M +num decayed parameter tensors: 50, with 123,961,344 parameters +num non-decayed parameter tensors: 25, with 19,200 parameters +using fused AdamW: True +step 0: train loss 10.9288, val loss 10.9279 +iter 0: loss 10.9560, time 18635.10ms, mfu -100.00% +step 250: train loss 6.6967, val loss 6.6361 +saving checkpoint to out-openwebtext_none_seed123_warmup200 +iter 250: loss 6.4524, time 22026.73ms, mfu 0.05% +step 500: train loss 6.3609, val loss 6.4387 +saving checkpoint to out-openwebtext_none_seed123_warmup200 +iter 500: loss 6.4723, time 22192.50ms, mfu 0.05% +step 750: train loss 6.2272, val loss 6.2293 +saving checkpoint to out-openwebtext_none_seed123_warmup200 +iter 750: loss 5.6018, time 22064.76ms, mfu 0.05% +step 1000: train loss 6.1641, val loss 6.1593 +saving checkpoint to out-openwebtext_none_seed123_warmup200 +iter 1000: loss 6.4837, time 22373.81ms, mfu 0.05% +step 1250: train loss 6.0305, val loss 6.0582 +saving checkpoint to out-openwebtext_none_seed123_warmup200 +iter 1250: loss 5.7705, time 23990.51ms, mfu 0.05% +step 1500: train loss 6.0079, val loss 5.9815 +saving checkpoint to out-openwebtext_none_seed123_warmup200 +iter 1500: loss 5.8651, time 25349.69ms, mfu 0.05% +step 1750: train loss 5.9227, val loss 5.9591 +saving checkpoint to out-openwebtext_none_seed123_warmup200 +iter 1750: loss 6.5179, time 24759.39ms, mfu 0.05% +step 2000: train loss 5.8718, val loss 5.9028 +saving checkpoint to out-openwebtext_none_seed123_warmup200 +iter 2000: loss 5.8832, time 22707.07ms, mfu 0.05% +step 2250: train loss 5.7718, val loss 5.8272 +saving checkpoint to out-openwebtext_none_seed123_warmup200 +iter 2250: loss 6.3073, time 22418.13ms, mfu 0.05% +step 2500: train loss 5.7824, val loss 5.7821 +saving checkpoint to out-openwebtext_none_seed123_warmup200 +iter 2500: loss 6.0784, time 22912.15ms, mfu 0.05% +step 2750: train loss 5.7421, val loss 5.7170 +saving checkpoint to out-openwebtext_none_seed123_warmup200 +iter 2750: loss 6.2997, time 24643.38ms, mfu 0.05% +step 3000: train loss 5.6735, val loss 5.6657 +saving checkpoint to out-openwebtext_none_seed123_warmup200 +iter 3000: loss 6.3636, time 23004.86ms, mfu 0.05% +step 3250: train loss 5.5828, val loss 5.5819 +saving checkpoint to out-openwebtext_none_seed123_warmup200 +iter 3250: loss 5.5950, time 23216.17ms, mfu 0.05% +step 3500: train loss 5.5349, val loss 5.5767 +saving checkpoint to out-openwebtext_none_seed123_warmup200 +iter 3500: loss 5.6368, time 23443.32ms, mfu 0.05% +step 3750: train loss 5.4984, val loss 5.4852 +saving checkpoint to out-openwebtext_none_seed123_warmup200 +iter 3750: loss 5.4186, time 24010.93ms, mfu 0.05% +step 4000: train loss 5.4524, val loss 5.4636 +saving checkpoint to out-openwebtext_none_seed123_warmup200 +iter 4000: loss 5.4321, time 22953.47ms, mfu 0.05% +step 4250: train loss 5.4258, val loss 5.4390 +saving checkpoint to out-openwebtext_none_seed123_warmup200 +Traceback (most recent call last): +File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 850, in save +_save( +File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 1114, in _save +zip_file.write_record(name, storage, num_bytes) +RuntimeError: [enforce fail at inline_container.cc:783] . PytorchStreamWriter failed writing file data/82: file write failed + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): +File "C:\Users\brian\documents\nanogpt\train.py", line 235, in +torch.save(checkpoint, os.path.join(out_dir, 'ckpt.pt')) +File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 849, in save +with _open_zipfile_writer(f) as opened_zipfile: +File "C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 690, in __exit__ +self.file_like.write_end_of_file() +RuntimeError: [enforce fail at inline_container.cc:603] . unexpected pos 1405254208 vs 1405254100 + ⚠️ Failed to read final loss + +🔬 Testing DYNAMIC with 3 seeds... + + Seed 1337 → out-openwebtext_dynamic_seed1337_warmup200 +tokens per iteration will be: 4,096 +Traceback (most recent call last): +File "C:\Users\brian\documents\nanogpt\train.py", line 122, in +os.makedirs(out_dir, exist_ok=True) +File "", line 225, in makedirs +OSError: [WinError 112] There is not enough space on the disk: 'out-openwebtext_dynamic_seed1337_warmup200' + ⚠️ Failed to read final loss + + Seed 42 → out-openwebtext_dynamic_seed42_warmup200 +tokens per iteration will be: 4,096 +Traceback (most recent call last): +File "C:\Users\brian\documents\nanogpt\train.py", line 122, in +os.makedirs(out_dir, exist_ok=True) +File "", line 225, in makedirs +OSError: [WinError 112] There is not enough space on the disk: 'out-openwebtext_dynamic_seed42_warmup200' + ⚠️ Failed to read final loss + + Seed 123 → out-openwebtext_dynamic_seed123_warmup200 +tokens per iteration will be: 4,096 +Traceback (most recent call last): +File "C:\Users\brian\documents\nanogpt\train.py", line 122, in +os.makedirs(out_dir, exist_ok=True) +File "", line 225, in makedirs +OSError: [WinError 112] There is not enough space on the disk: 'out-openwebtext_dynamic_seed123_warmup200' + ⚠️ Failed to read final loss + +🔬 Testing STATIC_GATE_PREHEAD with 3 seeds... + + Seed 1337 → out-openwebtext_static_gate_prehead_seed1337_warmup200 +tokens per iteration will be: 4,096 +Traceback (most recent call last): +File "C:\Users\brian\documents\nanogpt\train.py", line 122, in +os.makedirs(out_dir, exist_ok=True) +File "", line 225, in makedirs +OSError: [WinError 112] There is not enough space on the disk: 'out-openwebtext_static_gate_prehead_seed1337_warmup200' + ⚠️ Failed to read final loss + + Seed 42 → out-openwebtext_static_gate_prehead_seed42_warmup200 +tokens per iteration will be: 4,096 +Traceback (most recent call last): +File "C:\Users\brian\documents\nanogpt\train.py", line 122, in +os.makedirs(out_dir, exist_ok=True) +File "", line 225, in makedirs +OSError: [WinError 112] There is not enough space on the disk: 'out-openwebtext_static_gate_prehead_seed42_warmup200' + ⚠️ Failed to read final loss + + Seed 123 → out-openwebtext_static_gate_prehead_seed123_warmup200 +tokens per iteration will be: 4,096 +Traceback (most recent call last): +File "C:\Users\brian\documents\nanogpt\train.py", line 122, in +os.makedirs(out_dir, exist_ok=True) +File "", line 225, in makedirs +OSError: [WinError 112] There is not enough space on the disk: 'out-openwebtext_static_gate_prehead_seed123_warmup200' + ⚠️ Failed to read final loss + +==================================================================================================== +2% WARMUP RESULTS (compare to previous 20% warmup) +==================================================================================================== +Variant Mean Final Std vs Baseline +---------------------------------------------------------------------------------------------------- +none 4.9481 ± 0.0383 +0.0000 = +C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\numpy\_core\fromnumeric.py:3860: RuntimeWarning: Mean of empty slice. + return _methods._mean(a, axis=axis, dtype=dtype, +C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\numpy\_core\_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide + ret = ret.dtype.type(ret / rcount) +C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\numpy\_core\_methods.py:222: RuntimeWarning: Degrees of freedom <= 0 for slice + ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, +C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\numpy\_core\_methods.py:180: RuntimeWarning: invalid value encountered in divide + arrmean = um.true_divide(arrmean, div, out=arrmean, +C:\Users\brian\AppData\Local\Programs\Python\Python311\Lib\site-packages\numpy\_core\_methods.py:214: RuntimeWarning: invalid value encountered in scalar divide + ret = ret.dtype.type(ret / rcount) +dynamic nan ± nan +nan = +static_gate_prehead nan ± nan +nan = + +CROSSOVER ANALYSIS (dynamic vs baseline) +First step where dynamic mean < baseline mean and stays lower for ≥80% of remaining evals: +Traceback (most recent call last): + File "C:\Users\brian\documents\nanogpt\run_warmup200_isolation.py", line 104, in + dyn_traj = np.mean([trajectories[f"dynamic_seed{s}"] for s in seeds], axis=0) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "C:\Users\brian\documents\nanogpt\run_warmup200_isolation.py", line 104, in + dyn_traj = np.mean([trajectories[f"dynamic_seed{s}"] for s in seeds], axis=0) + ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^ +KeyError: 'dynamic_seed1337' \ No newline at end of file diff --git a/results/summaries/friday_night_summary_20260321_0104.txt b/results/summaries/friday_night_summary_20260321_0104.txt new file mode 100644 index 0000000000000000000000000000000000000000..9cbf58bcf5e72a39e20e20b6c418c26733a3aed0 --- /dev/null +++ b/results/summaries/friday_night_summary_20260321_0104.txt @@ -0,0 +1,32 @@ +🎲 FRIDAY NIGHT SPICY RANDOM GATE SUMMARY 🎲 +==================================================================================================== +Generated: 2026-03-21 06:04 +Warmup: 2000 | Seed: 42 + +RANKING (best to worst final val loss): +---------------------------------------------------------------------------------------------------- + 1. a_only final=4.4178 best=4.4178 ✓ vs_baseline=-0.2563 vs_qa=+0.0105 + → learned: A-only Linear(64->64) + 2. ones_identity final=4.4869 best=4.4702 ✗(best@9750) vs_baseline=-0.1872 vs_qa=+0.0796 + → sanity: always 1.0 (= baseline) + 3. random_normal final=4.4982 best=4.4822 ✗(best@9750) vs_baseline=-0.1759 vs_qa=+0.0909 + → chaos: normal(0.5,0.2) clamped + 4. random_uniform final=4.5238 best=4.5132 ✗(best@9750) vs_baseline=-0.1503 vs_qa=+0.1165 + → chaos: uniform random [0,1] + 5. bernoulli_drop final=4.6653 best=4.6572 ✗(best@9750) vs_baseline=-0.0088 vs_qa=+0.2580 + → spicy: binary 50% dropout gate + +ANCHOR REFERENCE: +---------------------------------------------------------------------------------------------------- + cheap_qa_2000 : 4.4073 + midtier_q_2000 : 4.5411 + cheap_q_2000 : 4.5533 + static_prehead_2000 : 4.6399 + baseline_2000 : 4.6741 + +KEY QUESTIONS ANSWERED: +---------------------------------------------------------------------------------------------------- + ones_gate vs baseline: 0.1872 diff (differs — unexpected!) + random_uniform vs baseline: -0.1503 (location helps even without learning!) + a_only vs random: -0.1060 (learned A signal adds value) + a_only vs cheap_Q: -0.1355 (A beats Q!) \ No newline at end of file diff --git a/results/summaries/full_x_seed123_result_20260324_1712.txt b/results/summaries/full_x_seed123_result_20260324_1712.txt new file mode 100644 index 0000000000000000000000000000000000000000..1fd99096b44b22f91f452a52527a195fee9b49aa --- /dev/null +++ b/results/summaries/full_x_seed123_result_20260324_1712.txt @@ -0,0 +1,23 @@ +FULL_X SEED 123 — RESULT +============================================================ +Generated: 2026-03-24 18:16 +Runtime: 64.3 min +Exit code: 0 + +best_val: 4.4112 ✗(best@9500) +final_val: 4.4145 + +FULL_X 3-SEED SUMMARY +---------------------------------------- + seed 42: 4.3897 ✓ + seed 1337: 4.3842 ✓ + seed 123: 4.4112 ✗(best@9500) + mean (3/3): 4.3950 ±0.0116 + +VERDICT vs cheap_qa (4.3897): + cheap_qa HOLDS CROWN (full_x trails by 0.0053) + +UPDATED CLEAN RANKING (top 3): + 1. cheap_qa 4.3897 vs baseline: -0.0811 + 2. full_x 4.3950 vs baseline: -0.0758 + 3. mlp128_qa 4.4032 vs baseline: -0.0676 \ No newline at end of file diff --git a/results/summaries/midtier_q_recovery_summary_20260325_0738.txt b/results/summaries/midtier_q_recovery_summary_20260325_0738.txt new file mode 100644 index 0000000000000000000000000000000000000000..ce0a744c59cc84b770922dbafc661c56a3e5c3df --- /dev/null +++ b/results/summaries/midtier_q_recovery_summary_20260325_0738.txt @@ -0,0 +1,24 @@ +MIDTIER_Q WARMUP-500 RECOVERY — RUNNING SUMMARY +====================================================================== +Generated: 2026-03-25 09:46 +Recipe: warmup=500, beta2=0.99 (CLEAN) + +PER-SEED RESULTS +---------------------------------------------------------------------- + seed 42: 4.5733 ✓ [carried in] + seed 1337: 4.6151 ✓ + seed 123: 4.6143 ✓ + + running mean (3/3): 4.6009 ±0.0195 + vs @2000 ref (4.4135): +0.1874 + +WARMUP SENSITIVITY TABLE (all variants so far) +---------------------------------------------------------------------- + Variant @w=500 @w=2000 Delta Sensitivity +---------------------------------------------------------------------- + full_x 4.8518 4.3950 +0.4568 CATASTROPHIC + cheap_qa 4.6130 4.3897 +0.2233 HIGH + midtier_q* 4.6009 4.4135 +0.1874 MODERATE*partial + baseline 4.5663 4.4708 +0.0955 LOW + +ALL 3 SEEDS COMPLETE — final mean is authoritative \ No newline at end of file diff --git a/results/summaries/p2_rerun_summary_20260323_1714.txt b/results/summaries/p2_rerun_summary_20260323_1714.txt new file mode 100644 index 0000000000000000000000000000000000000000..3e36f30d56b97e156d93dc2db0f1a67427c7249c --- /dev/null +++ b/results/summaries/p2_rerun_summary_20260323_1714.txt @@ -0,0 +1,44 @@ +PRIORITY 2 RERUN — DISK CASUALTY RECOVERY SUMMARY +==================================================================================================== +Generated: 2026-03-24 01:34 +Hardware: RTX 3060 | Clean recipe | No checkpoints + +PER-VARIANT RESULTS (best_val) +---------------------------------------------------------------------------------------------------- +STATIC_PREHEAD + learned per-head scalar — 9 params, no signal + seed 42: 4.4550 ✗(best@9750) + seed 123: 4.4559 ✗(best@9500) + seed 1337: 4.4340 ✗(best@9750) + mean (3/3): 4.4483 ±0.0101 ref=4.4438 delta=+0.0045 + +FULL_X + full X gate Linear(768->768) on x_norm + seed 42: 4.3897 ✓ + seed 1337: 4.3842 ✓ + mean (2/3): 4.3870 ±0.0028 ref=4.5374 delta=-0.1504 + +X_HEADSHARED + head-shared X gate Linear(64->64) on x + seed 42: 4.4204 ✓ + seed 123: 4.4367 ✓ + seed 1337: 4.4468 ✓ + mean (3/3): 4.4346 ±0.0109 ref=4.5863 delta=-0.1517 + +COMPLETE CLEAN RANKING (all confirmed results) +---------------------------------------------------------------------------------------------------- + 1. full_x 4.3870 ±0.0028 vs baseline: -0.0838 + 2. cheap_qa 4.3897 ±0.0051 vs baseline: -0.0811 + 3. mlp128_qa 4.4032 ±0.0153 vs baseline: -0.0676 + 4. midtier_q 4.4135 ±0.0086 vs baseline: -0.0573 + 5. cheap_q 4.4198 ±0.0185 vs baseline: -0.0510 + 6. a_only 4.4298 ±0.0095 vs baseline: -0.0410 + 7. x_headshared 4.4346 ±0.0109 vs baseline: -0.0362 + 8. static_prehead 4.4483 ±0.0101 vs baseline: -0.0225 + 9. baseline 4.4708 ±0.0074 vs baseline: +0.0000 + +DIRTY → CLEAN DELTAS (fairness sweep) +---------------------------------------------------------------------------------------------------- + static_prehead dirty=4.4438 clean=4.4483 delta=+0.0045 + full_x dirty=4.5374 clean=4.3870 delta=-0.1504 + x_headshared dirty=4.5863 clean=4.4346 delta=-0.1517 \ No newline at end of file diff --git a/results/summaries/phase2_coded_completeness_summary_20260325_1113.txt b/results/summaries/phase2_coded_completeness_summary_20260325_1113.txt new file mode 100644 index 0000000000000000000000000000000000000000..446b6c78aa8248f19ff5a3c0c8f6e47025870266 --- /dev/null +++ b/results/summaries/phase2_coded_completeness_summary_20260325_1113.txt @@ -0,0 +1,62 @@ +PHASE 2 — CODED COMPLETENESS SUMMARY +==================================================================================================== +Generated: 2026-03-25 17:30 +Question: where do full_q and paper-shaped x_headspec_elem land under the clean 2000 recipe? +Recipe: warmup=2000, beta2=0.99, 4096 tokens/iter + +PHASE-2 PER-VARIANT RESULTS +---------------------------------------------------------------------------------------------------- +FULL_Q + Full-width Q gate — Linear(768->768) on q, reshaped back to heads + seed 42: 4.4027 ✓ + seed 123: 4.4202 ✗(best@9500) + seed 1337: 4.4099 ✓ + mean (3/3): 4.4109 ±0.0072 + vs baseline: -0.0599 + vs full_x: +0.0159 + vs cheap_qa: +0.0212 + +X_HEADSPEC_ELEM + Paper-shaped X gate — head-specific elementwise G1 on x_prenorm + seed 42: 4.4638 ✓ + seed 123: 4.4247 ✗(best@9750) + seed 1337: 4.4466 ✗(best@9750) + mean (3/3): 4.4450 ±0.0160 + vs baseline: -0.0258 + vs full_x: +0.0500 + vs cheap_qa: +0.0553 + +CURRENT LEADERBOARD (canon + phase-2 partials) +---------------------------------------------------------------------------------------------------- + 1. cheap_qa 4.3897 ±0.0051 n=3 + 2. full_x 4.3950 ±0.0116 n=3 + 3. mlp128_qa 4.4032 ±nan n=3 + 4. full_q 4.4109 ±0.0072 n=3 + 5. midtier_q 4.4135 ±0.0086 n=3 + 6. cheap_q 4.4198 ±nan n=3 + 7. a_only 4.4298 ±nan n=3 + 8. x_headshared 4.4346 ±nan n=3 + 9. static_prehead 4.4438 ±nan n=3 + 10. x_headspec_elem 4.4450 ±0.0160 n=3 + 11. dot_elementwise 4.4537 ±0.0121 n=3 + 12. dot_scalar 4.4601 ±0.0040 n=3 + 13. baseline 4.4708 ±0.0078 n=3 + +PHASE-2 POSITIONING +---------------------------------------------------------------------------------------------------- + full_q final mean: 4.4109 + x_headspec_elem final mean: 4.4450 + +CANON REFERENCES +---------------------------------------------------------------------------------------------------- + cheap_qa 4.3897 ±0.0051 + full_x 4.3950 ±0.0116 + mlp128_qa 4.4032 ±nan + midtier_q 4.4135 ±0.0086 + cheap_q 4.4198 ±nan + a_only 4.4298 ±nan + x_headshared 4.4346 ±nan + static_prehead 4.4438 ±nan + dot_elementwise 4.4537 ±0.0121 + dot_scalar 4.4601 ±0.0040 + baseline 4.4708 ±0.0078 \ No newline at end of file diff --git a/results/summaries/phase3_bilinear_diag_summary_20260325_2226.txt b/results/summaries/phase3_bilinear_diag_summary_20260325_2226.txt new file mode 100644 index 0000000000000000000000000000000000000000..579d8634e33a98d73cb1d2b14d8808986d4d26a8 --- /dev/null +++ b/results/summaries/phase3_bilinear_diag_summary_20260325_2226.txt @@ -0,0 +1,51 @@ +PHASE 3 — BILINEAR DIAG SUMMARY +==================================================================================================== +Generated: 2026-03-26 01:38 +Question: can a tiny learned diagonal bilinear Q+A gate bridge the gap between dot gates and cheap_qa? +Recipe: warmup=2000, beta2=0.99, 4096 tokens/iter + +PER-SEED RESULTS +---------------------------------------------------------------------------------------------------- +BILINEAR_DIAG + Diagonal bilinear Q+A gate — sigmoid((q * y * w) / sqrt(d_head)), 64 params/layer + seed 42: 4.4624 ✗(best@9750) + seed 123: 4.4596 ✗(best@9500) + seed 1337: 4.4329 ✗(best@9750) + mean (3/3): 4.4516 ±0.0133 + vs cheap_qa: +0.0619 + vs dot_elementwise:-0.0021 + vs dot_scalar: -0.0085 + vs baseline: -0.0192 + +MECHANISM BRIDGE INTERPRETATION +---------------------------------------------------------------------------------------------------- + This runner tests whether a tiny learned interaction gate on q*y can recover + more of cheap_qa's gain than the zero-parameter dot gates without jumping + all the way to concat-then-linear or MLP capacity. + +CURRENT LEADERBOARD (canon + phase-3 partial) +---------------------------------------------------------------------------------------------------- + 1. cheap_qa 4.3897 ±0.0051 n=3 + 2. full_x 4.3950 ±0.0116 n=3 + 3. mlp128_qa 4.4032 ±nan n=3 + 4. full_q 4.4109 ±0.0072 n=3 + 5. midtier_q 4.4135 ±0.0086 n=3 + 6. cheap_q 4.4198 ±nan n=3 + 7. a_only 4.4298 ±nan n=3 + 8. x_headshared 4.4346 ±nan n=3 + 9. static_prehead 4.4438 ±nan n=3 + 10. x_headspec_elem 4.4450 ±0.0160 n=3 + 11. bilinear_diag 4.4516 ±0.0133 n=3 + 12. dot_elementwise 4.4537 ±0.0121 n=3 + 13. dot_scalar 4.4601 ±0.0040 n=3 + 14. baseline 4.4708 ±0.0078 n=3 + +KEY REFERENCES +---------------------------------------------------------------------------------------------------- + cheap_qa 4.3897 ±0.0051 + full_x 4.3950 ±0.0116 + full_q 4.4109 ±0.0072 + midtier_q 4.4135 ±0.0086 + dot_elementwise 4.4537 ±0.0121 + dot_scalar 4.4601 ±0.0040 + baseline 4.4708 ±0.0078 \ No newline at end of file diff --git a/results/summaries/phase4_chartfill_summary_20260326_0730.txt b/results/summaries/phase4_chartfill_summary_20260326_0730.txt new file mode 100644 index 0000000000000000000000000000000000000000..ff66be6ff9ee55be226c9ffdad3741681ccf2265 --- /dev/null +++ b/results/summaries/phase4_chartfill_summary_20260326_0730.txt @@ -0,0 +1,74 @@ +PHASE 4 — CHART-FILL HEAD-SPECIFIC SUMMARY +==================================================================================================== +Generated: 2026-03-26 16:58 +Question: how do the missing true head-specific chart cells land under the clean 2000 recipe? +Recipe: warmup=2000, beta2=0.99, 4096 tokens/iter + +PHASE-4 PER-VARIANT RESULTS +---------------------------------------------------------------------------------------------------- +Q_FULL_HEADSPEC + Full-width Q source (768) with separate 768->64 projection for each head + seed 42: 4.4151 ✓ + seed 123: 4.3964 ✓ + seed 1337: 4.4058 ✓ + mean (3/3): 4.4058 ±0.0076 + vs baseline: -0.0650 + vs full_q: -0.0051 + vs cheap_q: -0.0140 + +X_FULL_HEADSPEC + Full-width X source (x_prenorm, 768) with separate 768->64 projection for each head + seed 42: 4.4013 ✓ + seed 123: 4.3867 ✓ + seed 1337: 4.4011 ✓ + mean (3/3): 4.3964 ±0.0068 + vs baseline: -0.0744 + vs full_x: +0.0014 + vs x_headspec_elem: -0.0486 + +QA_HEADSPEC + True head-specific Q+A gate: separate 128->64 projection for each head + seed 42: 4.4004 ✓ + seed 123: 4.3904 ✓ + seed 1337: 4.3945 ✓ + mean (3/3): 4.3951 ±0.0041 + vs baseline: -0.0757 + vs cheap_qa: +0.0054 + vs mlp128_qa:-0.0081 + +CURRENT LEADERBOARD (canon + phase-4 partials) +---------------------------------------------------------------------------------------------------- + 1. cheap_qa 4.3897 ±0.0051 n=3 + 2. full_x 4.3950 ±0.0116 n=3 + 3. qa_headspec 4.3951 ±0.0041 n=3 + 4. x_full_headspec 4.3964 ±0.0068 n=3 + 5. mlp128_qa 4.4032 ±nan n=3 + 6. q_full_headspec 4.4058 ±0.0076 n=3 + 7. full_q 4.4109 ±0.0072 n=3 + 8. midtier_q 4.4135 ±0.0086 n=3 + 9. cheap_q 4.4198 ±nan n=3 + 10. a_only 4.4298 ±nan n=3 + 11. x_headshared 4.4346 ±nan n=3 + 12. static_prehead 4.4438 ±nan n=3 + 13. x_headspec_elem 4.4450 ±0.0160 n=3 + 14. bilinear_diag 4.4516 ±0.0133 n=3 + 15. dot_elementwise 4.4537 ±0.0121 n=3 + 16. dot_scalar 4.4601 ±0.0040 n=3 + 17. baseline 4.4708 ±0.0078 n=3 + +CANON REFERENCES +---------------------------------------------------------------------------------------------------- + cheap_qa 4.3897 ±0.0051 + full_x 4.3950 ±0.0116 + mlp128_qa 4.4032 ±nan + full_q 4.4109 ±0.0072 + midtier_q 4.4135 ±0.0086 + cheap_q 4.4198 ±nan + a_only 4.4298 ±nan + x_headshared 4.4346 ±nan + x_headspec_elem 4.4450 ±0.0160 + static_prehead 4.4438 ±nan + bilinear_diag 4.4516 ±0.0133 + dot_elementwise 4.4537 ±0.0121 + dot_scalar 4.4601 ±0.0040 + baseline 4.4708 ±0.0078 \ No newline at end of file diff --git a/results/summaries/phase5_warmup500_toptiers_summary_20260326_1722.txt b/results/summaries/phase5_warmup500_toptiers_summary_20260326_1722.txt new file mode 100644 index 0000000000000000000000000000000000000000..a6e1cf96f23eca1f1c53eab598f203f4025cd4cf --- /dev/null +++ b/results/summaries/phase5_warmup500_toptiers_summary_20260326_1722.txt @@ -0,0 +1,81 @@ +PHASE 5 — WARMUP 500 TOP-TIER ROBUSTNESS SUMMARY +================================================================================================ + +Bundle purpose: + Fill the remaining warmup=500 holes for the top-tier 2000-warmup variants. + +Results CSV: phase5_warmup500_toptiers_runs_20260326_1722.csv + +Completed warmup=500 canon so far: + baseline 4.5663 + midtier_q 4.6009 + cheap_qa 4.6130 + full_x 4.8518 + +Leaderboard within this phase (completed seeds only): + 1. mlp128_qa mean=4.6332 std=0.0424 done=3/3 vs 2000=+0.2300 + 2. q_full_headspec mean=4.7163 std=0.0632 done=3/3 vs 2000=+0.3105 + 3. qa_headspec mean=4.7283 std=0.0278 done=3/3 vs 2000=+0.3332 + 4. full_q mean=4.7456 std=0.0183 done=3/3 vs 2000=+0.3347 + 5. x_full_headspec mean=4.8353 std=0.0329 done=3/3 vs 2000=+0.4389 + +------------------------------------------------------------------------------------------------ +qa_headspec (dynamic_qa_conditioned_headspecific) + True head-specific Q+A gate: separate 128->64 projection for each head + out_dir: out-openwebtext_phase5_w500_qa_headspec + completed seeds: 3/3 + partial mean/std: 4.7283 ± 0.0278 + vs own 2000-warmup anchor: +0.3332 + seed= 42 best=4.7306 final=4.7306 best@10000 stable=False vs2000=+0.3355 + seed= 123 best=4.6931 final=4.6931 best@10000 stable=False vs2000=+0.2980 + seed=1337 best=4.7612 final=4.7981 best@9750 stable=False vs2000=+0.3661 + +------------------------------------------------------------------------------------------------ +x_full_headspec (dynamic_xconditioned_fullwidth_headspecific) + Full-width X source (x_prenorm, 768) with separate 768->64 projection for each head + out_dir: out-openwebtext_phase5_w500_x_full_headspec + completed seeds: 3/3 + partial mean/std: 4.8353 ± 0.0329 + vs own 2000-warmup anchor: +0.4389 + seed= 42 best=4.8063 final=4.8063 best@10000 stable=False vs2000=+0.4099 + seed= 123 best=4.8183 final=4.8416 best@9750 stable=False vs2000=+0.4219 + seed=1337 best=4.8814 final=4.9124 best@9750 stable=False vs2000=+0.4850 + +------------------------------------------------------------------------------------------------ +mlp128_qa (dynamic_qa_conditioned_mlp128) + Shared Q+A MLP gate: 128->128->64 per head/token + out_dir: out-openwebtext_phase5_w500_mlp128_qa + completed seeds: 3/3 + partial mean/std: 4.6332 ± 0.0424 + vs own 2000-warmup anchor: +0.2300 + seed= 42 best=4.6928 final=4.6928 best@10000 stable=False vs2000=+0.2896 + seed= 123 best=4.5980 final=4.6129 best@9750 stable=False vs2000=+0.1948 + seed=1337 best=4.6088 final=4.6308 best@9750 stable=False vs2000=+0.2056 + +------------------------------------------------------------------------------------------------ +q_full_headspec (dynamic_qconditioned_fullwidth_headspecific) + Full-width Q source (768) with separate 768->64 projection for each head + out_dir: out-openwebtext_phase5_w500_q_full_headspec + completed seeds: 3/3 + partial mean/std: 4.7163 ± 0.0632 + vs own 2000-warmup anchor: +0.3105 + seed= 42 best=4.7184 final=4.7184 best@10000 stable=False vs2000=+0.3126 + seed= 123 best=4.6379 final=4.6678 best@9750 stable=False vs2000=+0.2321 + seed=1337 best=4.7927 final=4.8213 best@9750 stable=False vs2000=+0.3869 + +------------------------------------------------------------------------------------------------ +full_q (dynamic_qconditioned_fullwidth) + Shared full-width Q source (768->768), reshaped back to heads + out_dir: out-openwebtext_phase5_w500_full_q + completed seeds: 3/3 + partial mean/std: 4.7456 ± 0.0183 + vs own 2000-warmup anchor: +0.3347 + seed= 42 best=4.7250 final=4.7250 best@10000 stable=False vs2000=+0.3141 + seed= 123 best=4.7423 final=4.7474 best@9500 stable=False vs2000=+0.3314 + seed=1337 best=4.7695 final=4.7695 best@10000 stable=False vs2000=+0.3586 + +Current global picture: + - 2000-warmup chart is essentially complete for the top contenders. + - This phase is only about harsh-warmup robustness (500 steps). + - The strongest current open questions are whether qa_headspec or x_full_headspec can beat baseline@500. + diff --git a/results/summaries/phase6a_grok_qafollowups_w2000_summary_20260327_1722.txt b/results/summaries/phase6a_grok_qafollowups_w2000_summary_20260327_1722.txt new file mode 100644 index 0000000000000000000000000000000000000000..ed4ee931fa6d83ea6594765f6f1b31721998d3fc --- /dev/null +++ b/results/summaries/phase6a_grok_qafollowups_w2000_summary_20260327_1722.txt @@ -0,0 +1,53 @@ +PHASE 6A — GROK Q+A FOLLOW-UPS @ WARMUP 2000 +================================================================================================ + +Bundle purpose: + Evaluate the two Grok-inspired Q+A follow-ups against the clean 2000-warmup board. + +Results CSV: phase6a_grok_qafollowups_w2000_runs_20260327_1722.csv + +2000-warmup canon reference: + cheap_qa 4.3897 + full_x 4.3950 + qa_headspec 4.3951 + x_full_headspec 4.3964 + mlp128_qa 4.4032 + q_full_headspec 4.4058 + full_q 4.4109 + midtier_q 4.4135 + cheap_q 4.4198 + a_only 4.4298 + x_headshared 4.4346 + static_prehead 4.4438 + x_headspec_elem 4.4450 + bilinear_diag 4.4516 + dot_elementwise 4.4537 + dot_scalar 4.4601 + baseline 4.4708 + +Leaderboard within this phase (completed seeds only): + 1. qa_normed mean=4.3926 std=0.0063 done=3/3 vs2000=n/a + 2. qa_lowrank mean=4.4068 std=0.0035 done=3/3 vs2000=n/a + +------------------------------------------------------------------------------------------------ +qa_normed (dynamic_qa_conditioned_normed) + RMSNorm q and y separately, then shared Linear(128->64) gate + out_dir: out-openwebtext_phase6_w2000_qa_normed + completed seeds: 3/3 + partial mean/std: 4.3926 ± 0.0063 + vs own 2000-warmup anchor: n/a + seed= 42 best=4.3882 final=4.3882 best@10000 stable=FALSE + seed= 123 best=4.4016 final=4.4111 best@9750 stable=FALSE + seed=1337 best=4.3881 final=4.4334 best@9750 stable=FALSE + +------------------------------------------------------------------------------------------------ +qa_lowrank (dynamic_qa_conditioned_lowrank16) + Shared low-rank Q+A mixer: 128->16->64 with SiLU + out_dir: out-openwebtext_phase6_w2000_qa_lowrank16 + completed seeds: 3/3 + partial mean/std: 4.4068 ± 0.0035 + vs own 2000-warmup anchor: n/a + seed= 42 best=4.4110 final=4.4223 best@9750 stable=FALSE + seed= 123 best=4.4069 final=4.4069 best@10000 stable=FALSE + seed=1337 best=4.4024 final=4.4145 best@9750 stable=FALSE + diff --git a/results/summaries/phase6b_grok_qafollowups_w500_summary_20260328_0028.txt b/results/summaries/phase6b_grok_qafollowups_w500_summary_20260328_0028.txt new file mode 100644 index 0000000000000000000000000000000000000000..e3a1a1b0e9555a39742b26411d1805d8468bc491 --- /dev/null +++ b/results/summaries/phase6b_grok_qafollowups_w500_summary_20260328_0028.txt @@ -0,0 +1,47 @@ +PHASE 6B — GROK Q+A FOLLOW-UPS @ WARMUP 500 +================================================================================================ + +Bundle purpose: + Evaluate the two Grok-inspired Q+A follow-ups against the 500-warmup robustness board. + +Results CSV: phase6b_grok_qafollowups_w500_runs_20260328_0028.csv + +Completed warmup=500 canon so far: + baseline 4.5663 + midtier_q 4.6009 + cheap_qa 4.6130 + mlp128_qa 4.6332 + q_full_headspec 4.7163 + full_q 4.7250 + qa_headspec 4.7283 + x_full_headspec 4.8353 + full_x 4.8518 + +Leaderboard within this phase (completed seeds only): + 1. qa_normed mean=4.5902 std=0.0168 done=3/3 vs2000=n/a + 2. qa_lowrank mean=4.6289 std=0.0110 done=3/3 vs2000=n/a + +------------------------------------------------------------------------------------------------ +qa_normed (dynamic_qa_conditioned_normed) + RMSNorm q and y separately, then shared Linear(128->64) gate + out_dir: out-openwebtext_phase6_w500_qa_normed + completed seeds: 3/3 + partial mean/std: 4.5902 ± 0.0168 + vs own 2000-warmup anchor: n/a + vs current 500-warmup board: n/a + seed= 42 best=4.5880 final=4.5880 best@10000 stable=FALSE + seed= 123 best=4.5709 final=4.5731 best@9750 stable=FALSE + seed=1337 best=4.6118 final=4.6508 best@9750 stable=FALSE + +------------------------------------------------------------------------------------------------ +qa_lowrank (dynamic_qa_conditioned_lowrank16) + Shared low-rank Q+A mixer: 128->16->64 with SiLU + out_dir: out-openwebtext_phase6_w500_qa_lowrank16 + completed seeds: 3/3 + partial mean/std: 4.6289 ± 0.0110 + vs own 2000-warmup anchor: n/a + vs current 500-warmup board: n/a + seed= 42 best=4.6426 final=4.6426 best@10000 stable=FALSE + seed= 123 best=4.6282 final=4.6282 best@10000 stable=FALSE + seed=1337 best=4.6158 final=4.6289 best@9750 stable=FALSE + diff --git a/results/summaries/priority1_summary_20260322_1355.txt b/results/summaries/priority1_summary_20260322_1355.txt new file mode 100644 index 0000000000000000000000000000000000000000..3c4ecb85b8f747223bd343044fd206a4f8d9515e --- /dev/null +++ b/results/summaries/priority1_summary_20260322_1355.txt @@ -0,0 +1,55 @@ +PRIORITY 1: HEAD-TO-HEAD CLEAN CONFIRMATION SUMMARY +==================================================================================================== +Generated: 2026-03-23 02:27 +Hardware: RTX 3060 | Clean recipe | warmup=2000 + +PER-VARIANT RESULTS (best_val) +---------------------------------------------------------------------------------------------------- +BASELINE + no gate — session anchor + seed 42: 4.4728 ✗(best@9750) + seed 123: 4.4786 ✗(best@9500) + seed 1337: 4.4609 ✗(best@9750) + mean (3/3): 4.4708 ±0.0074 A10=4.4675 delta=+0.0033 + +MIDTIER_Q + per-head MLP(64->128->64) on Q + seed 42: 4.4085 ✓ + seed 123: 4.4065 ✓ + seed 1337: 4.4256 ✓ + mean (3/3): 4.4135 ±0.0086 A10=4.4039 delta=+0.0096 + +CHEAP_QA + per-head Linear(128->64) on cat(Q,y) + seed 42: 4.3830 ✓ + seed 123: 4.3953 ✗(best@9750) + seed 1337: 4.3907 ✗(best@9750) + mean (3/3): 4.3897 ±0.0051 A10=4.4151 delta=-0.0254 + +CHEAP_Q + per-head Linear(64->64) on Q + seed 42: 4.4069 ✓ + seed 123: 4.4066 ✓ + seed 1337: 4.4459 ✗(best@9250) + mean (3/3): 4.4198 ±0.0185 A10=4.4222 delta=-0.0024 + +HEAD-TO-HEAD RANKING (3060 clean, best_val mean) +---------------------------------------------------------------------------------------------------- + 1. cheap_qa 4.3897 ±0.0051 vs baseline: -0.0811 A10=4.4151 + 2. midtier_q 4.4135 ±0.0086 vs baseline: -0.0572 A10=4.4039 + 3. cheap_q 4.4198 ±0.0185 vs baseline: -0.0510 A10=4.4222 + 4. baseline 4.4708 ±0.0074 vs baseline: +0.0000 A10=4.4675 + +KEY VERDICT +---------------------------------------------------------------------------------------------------- + cheap_qa WINS (4.3897 vs 4.4135, gap=+0.0239) + Dual signal beats capacity alone. + Both Q intent and A retrieval contribute to the gate. + +==================================================================================================== +A10 REFERENCE (clean recipe) +==================================================================================================== + baseline 4.4675 ±0.0070 + midtier_q 4.4039 ±0.0063 + cheap_qa 4.4151 ±0.0156 + cheap_q 4.4222 ±0.0064 \ No newline at end of file diff --git a/results/summaries/priority2_summary_20260323_0623.txt b/results/summaries/priority2_summary_20260323_0623.txt new file mode 100644 index 0000000000000000000000000000000000000000..458cccbe7460dd8dfe11cb4d6927e4fcccdf49a2 --- /dev/null +++ b/results/summaries/priority2_summary_20260323_0623.txt @@ -0,0 +1,81 @@ +PRIORITY 2: OVERNIGHT RUNNER SUMMARY +==================================================================================================== +Generated: 2026-03-23 14:07 +Hardware: RTX 3060 | Clean recipe | warmup=2000 + +PER-VARIANT RESULTS (best_val) +---------------------------------------------------------------------------------------------------- +BASELINE_P2 + fresh same-session baseline anchor + seed 42: 4.4705 ✗(best@9750) + seed 123: 4.4768 ✗(best@9500) + seed 1337: 4.4591 ✗(best@9750) + mean (3/3): 4.4688 ±0.0073 + +MLP128_QA + MLP(128->128->64) on cat(Q,y) — capacity + dual signal + seed 42: 4.4240 ✓ + seed 123: 4.3879 ✗(best@9750) + seed 1337: 4.3976 ✗(best@9750) + mean (3/3): 4.4032 ±0.0153 ref=4.4282 delta=-0.0250 + +STATIC_PREHEAD + learned per-head scalar gate — 9 params, no signal + seed 42: 4.4568 ✗(best@9750) + seed 123: 6.9069 ✗(best@250) + seed 1337: 5.9137 ✗(best@1500) + mean (3/3): 5.7591 ±1.0062 ref=4.4438 delta=+1.3153 + +FULL_X + full X gate Linear(768->768) — paper implementation + seed 42: 6.9348 ✗(best@250) + seed 123: 6.9534 ✗(best@250) + seed 1337: 6.9370 ✗(best@250) + mean (3/3): 6.9417 ±0.0083 ref=4.5374 delta=+2.4043 + +X_HEADSHARED + head-shared X gate Linear(64->64) + seed 42: 6.9409 ✗(best@250) + seed 123: 6.9769 ✗(best@250) + seed 1337: 6.9312 ✗(best@250) + mean (3/3): 6.9497 ±0.0197 ref=4.5863 delta=+2.3634 + +COMPLETE CLEAN RANKING (P1 + P2, best_val mean) +---------------------------------------------------------------------------------------------------- + 1. cheap_qa 4.3897 vs baseline: -0.0811 + 2. mlp128_qa 4.4032 ±0.0153 vs baseline: -0.0676 ← P2 + 3. midtier_q 4.4135 vs baseline: -0.0573 + 4. cheap_q 4.4198 vs baseline: -0.0510 + 5. baseline_p2 4.4688 ±0.0073 vs baseline: -0.0020 ← P2 + 6. baseline 4.4708 vs baseline: +0.0000 + 7. static_prehead 5.7591 ±1.0062 vs baseline: +1.2883 ← P2 + 8. full_x 6.9417 ±0.0083 vs baseline: +2.4709 ← P2 + 9. x_headshared 6.9497 ±0.0197 vs baseline: +2.4789 ← P2 + +KEY QUESTIONS ANSWERED +---------------------------------------------------------------------------------------------------- +1. mlp128_qa = 4.4032 + vs cheap_qa (4.3897): +0.0135 cheap_qa wins + vs midtier_q (4.4135): -0.0103 mlp128_qa wins + + VERDICT: Adding A helps the MLP but not enough to beat cheap_qa + Dual signal + capacity beats capacity alone + +2. static_prehead = 5.7591 + vs A10 result (4.4438): +1.3153 + vs baseline: +1.2883 does not beat baseline ✗ + +3. full_x (clean) = 6.9417 (dirty was 4.5374, delta=+2.4043) +4. x_headshared (clean) = 6.9497 (dirty was 4.5863, delta=+2.3634) + +==================================================================================================== +CLEAN REFERENCE TABLE (P1 confirmed) +==================================================================================================== + cheap_qa 4.3897 ±0.0051 [3060 Priority 1] + midtier_q 4.4135 ±0.0086 [3060 Priority 1] + cheap_q 4.4198 ±0.0185 [3060 Priority 1] + baseline 4.4708 ±0.0074 [3060 Priority 1] + static_prehead 4.4438 ±0.0119 [A10 fairness sweep] + mlp128_qa 4.4282 [DIRTY 3060 — this run replaces] + full_x 4.5374 ±0.0089 [DIRTY 3060 — this run replaces] + x_headshared 4.5863 ±0.0116 [DIRTY 3060 — this run replaces] \ No newline at end of file diff --git a/results/summaries/qa_screen_summary.txt b/results/summaries/qa_screen_summary.txt new file mode 100644 index 0000000000000000000000000000000000000000..1bf76210884a34262e7711392cf404eb2f7a53c2 --- /dev/null +++ b/results/summaries/qa_screen_summary.txt @@ -0,0 +1,32 @@ +Q-GATE NEXT-PHASE SCREEN SUMMARY +==================================================================================================== + +Order run: + warmup=2000 + - dynamic_qa_conditioned + - dynamic_qa_conditioned_mlp128 + - dynamic_qconditioned_mlp192 + - dynamic_q_headshared_elementwise + - dynamic_qa_headshared_elementwise + warmup=500 + - dynamic_qa_conditioned + - dynamic_qa_conditioned_mlp128 + - dynamic_qconditioned_mlp192 + - dynamic_q_headshared_elementwise + - dynamic_qa_headshared_elementwise + +WARMUP 2000 +---------------------------------------------------------------------------------------------------- + 1. dynamic_qa_conditioned best=4.3807 final=4.3807 best_step=10000 runtime=3871.08s + 2. dynamic_qa_headshared_elementwise best=4.3844 final=4.3844 best_step=10000 runtime=3782.7s + 3. dynamic_q_headshared_elementwise best=4.4073 final=4.4073 best_step=10000 runtime=3767.82s + 4. dynamic_qa_conditioned_mlp128 best=4.4282 final=4.4282 best_step=10000 runtime=3893.58s + 5. dynamic_qconditioned_mlp192 best=4.4337 final=4.4337 best_step=10000 runtime=3967.29s + +WARMUP 500 +---------------------------------------------------------------------------------------------------- + 1. dynamic_qa_conditioned best=4.6003 final=4.6003 best_step=10000 runtime=3787.43s + 2. dynamic_qa_headshared_elementwise best=4.6170 final=4.6170 best_step=10000 runtime=3865.7s + 3. dynamic_q_headshared_elementwise best=4.6539 final=4.6539 best_step=10000 runtime=3768.24s + 4. dynamic_qconditioned_mlp192 best=4.6548 final=4.6548 best_step=9750 runtime=3922.5s + 5. dynamic_qa_conditioned_mlp128 best=4.6761 final=4.6761 best_step=10000 runtime=3919.42s diff --git a/results/summaries/runner_a_summary_20260321_0903.txt b/results/summaries/runner_a_summary_20260321_0903.txt new file mode 100644 index 0000000000000000000000000000000000000000..bd159d0e07b7de2e5f250d3f0725da9d5e9bec87 --- /dev/null +++ b/results/summaries/runner_a_summary_20260321_0903.txt @@ -0,0 +1,38 @@ +RUNNER A: BASELINE vs ONES_IDENTITY SUMMARY +==================================================================================================== +Generated: 2026-03-21 15:01 +All runs from same session — no historical anchors for key comparison. + +BASELINE +------------------------------------------------------------ + seed 42: 4.4830 ✗(best@9750) + seed 123: 4.4974 ✗(best@9500) + seed 1337: 4.5116 ✗(best@9750) + mean: 4.4973 ± 0.0117 + +ONES_IDENTITY +------------------------------------------------------------ + seed 42: 4.4859 ✗(best@9750) + seed 123: 4.4980 ✗(best@9500) + seed 1337: 4.5081 ✗(best@9750) + mean: 4.4973 ± 0.0091 + +WITHIN-SESSION COMPARISON (same seed, same session) +---------------------------------------------------------------------------------------------------- + seed 42: baseline=4.4830 ones=4.4859 diff=+0.0029 + seed 123: baseline=4.4974 ones=4.4980 diff=+0.0006 + seed 1337: baseline=4.5116 ones=4.5081 diff=-0.0035 + mean diff: +0.0000 + +VERDICT: ones_identity ≈ baseline ✓ — anchor question closed + +REFERENCE ANCHORS (context only): +------------------------------------------------------------ + cheap_q_2000 : 4.5533 + cheap_qa_2000 : 4.4073 + a_only_2000 : 4.4167 + +FRESH BASELINE MEAN (this session): + 4.4973 ± 0.0117 + vs a_only_2000 (4.4167): +0.0806 + vs cheap_qa_2000 (4.4073): +0.0900 \ No newline at end of file diff --git a/results/summaries/runner_b_summary_20260321_1545.txt b/results/summaries/runner_b_summary_20260321_1545.txt new file mode 100644 index 0000000000000000000000000000000000000000..b6e2269a5d636e7c86c30c9d0b9c35a9fef0f088 --- /dev/null +++ b/results/summaries/runner_b_summary_20260321_1545.txt @@ -0,0 +1,56 @@ +RUNNER B: A-ONLY 3-SEED CONFIRMATION SUMMARY +==================================================================================================== +Variant: dynamic_a_conditioned +Gate: per-head Linear(hs->hs) on y (SDPA output) only +Generated: 2026-03-21 18:51 + +PER-SEED RESULTS (this runner): +------------------------------------------------------------ + seed 42: 4.4180 ✓ + seed 1337: 4.4413 ✗(best@9250) + seed 123: 4.4300 ✓ + +This-runner aggregate (3/3 seeds): + mean = 4.4298 ± 0.0095 + +FULL AGGREGATE (including prior clean seed-42 runs): +------------------------------------------------------------ + seed 42 run 1 (a_only screen): 4.4167 (prior clean run) + seed 42 run 2 (friday night): 4.4178 (prior clean run) + seed 42 mean: 4.4172 + seed 42 run 3 (this runner): 4.4180 + seed 42 all-runs mean: 4.4175 + +3-SEED CANONICAL AGGREGATE (this runner): + mean = 4.4298 ± 0.0095 + +vs key anchors: + vs cheap_qa_3000_mean : +0.0349 (worse) + vs cheap_qa_2000_mean : +0.0225 (worse) + vs full_q_3000_mean : -0.0876 (better) + vs midtier_q_2000_mean : -0.1113 (better) + vs cheap_q_2000_mean : -0.1235 (better) + vs static_prehead_2000 : -0.2101 (better) + vs baseline_2000_mean : -0.2443 (better) + +KEY VERDICTS: +------------------------------------------------------------ + ✓ A-only beats cheap Q — retrieved content is a strong signal + ✓ cheap Q+A still wins — both signals contribute (expected) + ✓ A-only beats mid-tier Q + +MECHANISTIC STORY: + Retrieved content (y) is a stronger single gate signal than + query intent (Q). The Q+A advantage comes from genuine synergy + between both signals — what was sought AND what was found. + +==================================================================================================== +ANCHOR REFERENCE +==================================================================================================== + cheap_qa_3000_mean : 4.3949 + cheap_qa_2000_mean : 4.4073 + full_q_3000_mean : 4.5174 + midtier_q_2000_mean : 4.5411 + cheap_q_2000_mean : 4.5533 + static_prehead_2000 : 4.6399 + baseline_2000_mean : 4.6741 \ No newline at end of file