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  1. .config/.last_opt_in_prompt.yaml +1 -0
  2. .config/.last_survey_prompt.yaml +1 -0
  3. .config/.last_update_check.json +1 -0
  4. .config/active_config +1 -0
  5. .config/config_sentinel +0 -0
  6. .config/configurations/config_default +6 -0
  7. .config/default_configs.db +0 -0
  8. .config/gce +1 -0
  9. .config/hidden_gcloud_config_universe_descriptor_data_cache_configs.db +0 -0
  10. .config/logs/2025.04.25/17.43.11.944314.log +765 -0
  11. .config/logs/2025.04.25/17.43.36.962838.log +5 -0
  12. .config/logs/2025.04.25/17.43.46.133364.log +153 -0
  13. .config/logs/2025.04.25/17.43.48.364892.log +5 -0
  14. .config/logs/2025.04.25/17.43.57.236852.log +8 -0
  15. .config/logs/2025.04.25/17.43.57.933676.log +8 -0
  16. .gitattributes +3 -0
  17. sample_data/README.md +19 -0
  18. sample_data/anscombe.json +49 -0
  19. sample_data/california_housing_test.csv +0 -0
  20. sample_data/california_housing_train.csv +0 -0
  21. sample_data/mnist_test.csv +3 -0
  22. sample_data/mnist_train_small.csv +3 -0
  23. saved/wiki103_model/gpt2lm_wiki103.pth +3 -0
  24. saved/wiki103_model/learning_curve.png +0 -0
  25. saved/wiki103_model/perplexity_log.txt +1 -0
  26. saved/wiki103_model/speed_improvement_suggestions.txt +12 -0
  27. saved/wiki103_model/train.py +405 -0
  28. saved/wiki103_model/training_speed.txt +3 -0
  29. train.py +405 -0
  30. wiki103.test.txt +0 -0
  31. wiki103.train.txt +3 -0
  32. wiki103.valid.txt +0 -0
.config/.last_opt_in_prompt.yaml ADDED
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.config/.last_update_check.json ADDED
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+ {"last_update_check_time": 1745603027.8462982, "last_update_check_revision": 20250418150427, "notifications": [], "last_nag_times": {}}
.config/active_config ADDED
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.config/configurations/config_default ADDED
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.config/logs/2025.04.25/17.43.11.944314.log ADDED
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+ 2025-04-25 17:43:28,097 INFO ___FILE_ONLY___ ╠═ Downloading: Cloud Storage Command Line Tool ═╣
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+ 2025-04-25 17:43:28,356 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gsutil-20250418150427.tar.gz HTTP/11" 200 12382702
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+ 2025-04-25 17:43:28,428 INFO ___FILE_ONLY___ ╠═ Downloading: Cloud Storage Command Line Tool (Platfor... ═╣
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+ 2025-04-25 17:43:28,432 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
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+ 2025-04-25 17:43:28,665 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gsutil-nix-20240830134514.tar.gz HTTP/11" 200 1928
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+ 2025-04-25 17:43:28,670 INFO ___FILE_ONLY___ ╠═ Downloading: Google Cloud CLI Core Libraries (Platfor... ═╣
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+ 2025-04-25 17:43:28,674 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
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+ 2025-04-25 17:43:28,900 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-core-nix-20240830134514.tar.gz HTTP/11" 200 2306
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+ 2025-04-25 17:43:28,905 INFO ___FILE_ONLY___ ╠═ Downloading: Google Cloud CRC32C Hash Tool (Platform ... ═╣
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+ 2025-04-25 17:43:28,909 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
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+ 2025-04-25 17:43:29,136 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gcloud-crc32c-linux-x86_64-20250110133808.tar.gz HTTP/11" 200 1478989
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+ 2025-04-25 17:43:29,158 INFO ___FILE_ONLY___ ═
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+ 2025-04-25 17:43:29,158 INFO ___FILE_ONLY___ ═
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+ 2025-04-25 17:43:29,158 INFO ___FILE_ONLY___ ═
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+ 2025-04-25 17:43:29,158 INFO ___FILE_ONLY___ ╝
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+
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+ 2025-04-25 17:43:29,160 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
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+ 2025-04-25 17:43:29,161 INFO ___FILE_ONLY___ ╠═ Downloading: gcloud cli dependencies (Platform Specific) ═╣
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+ 2025-04-25 17:43:29,161 INFO ___FILE_ONLY___ ╚
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+ 2025-04-25 17:43:29,165 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
481
+ 2025-04-25 17:43:29,408 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gcloud-deps-linux-x86_64-20210416153011.tar.gz HTTP/11" 200 104
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+ 2025-04-25 17:43:29,409 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
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+ 2025-04-25 17:43:29,409 INFO ___FILE_ONLY___ ╝
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+ 2025-04-25 17:43:29,411 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
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+ 2025-04-25 17:43:29,411 INFO ___FILE_ONLY___ ╠═ Installing: BigQuery Command Line Tool ═╣
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509
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510
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511
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512
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513
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514
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515
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516
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517
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518
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519
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520
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521
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522
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523
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524
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525
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526
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527
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528
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529
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530
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531
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532
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533
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534
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535
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536
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537
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538
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539
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540
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541
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542
+ 2025-04-25 17:43:29,682 INFO ___FILE_ONLY___ ═
543
+ 2025-04-25 17:43:29,684 INFO ___FILE_ONLY___ ═
544
+ 2025-04-25 17:43:29,687 INFO ___FILE_ONLY___ ═
545
+ 2025-04-25 17:43:29,690 INFO ___FILE_ONLY___ ═
546
+ 2025-04-25 17:43:29,692 INFO ___FILE_ONLY___ ═
547
+ 2025-04-25 17:43:29,695 INFO ___FILE_ONLY___ ═
548
+ 2025-04-25 17:43:29,697 INFO ___FILE_ONLY___ ═
549
+ 2025-04-25 17:43:29,700 INFO ___FILE_ONLY___ ═
550
+ 2025-04-25 17:43:29,700 INFO ___FILE_ONLY___ ╝
551
+
552
+ 2025-04-25 17:43:29,708 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
553
+
554
+ 2025-04-25 17:43:29,708 INFO ___FILE_ONLY___ ╠═ Installing: BigQuery Command Line Tool (Platform Spec... ═╣
555
+
556
+ 2025-04-25 17:43:29,708 INFO ___FILE_ONLY___ ╚
557
+ 2025-04-25 17:43:29,709 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
558
+ 2025-04-25 17:43:29,709 INFO ___FILE_ONLY___ ╝
559
+
560
+ 2025-04-25 17:43:29,715 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
561
+
562
+ 2025-04-25 17:43:29,715 INFO ___FILE_ONLY___ ╠═ Installing: Bundled Python 3.12 ═╣
563
+
564
+ 2025-04-25 17:43:29,715 INFO ___FILE_ONLY___ ╚
565
+ 2025-04-25 17:43:29,719 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
566
+ 2025-04-25 17:43:29,719 INFO ___FILE_ONLY___ ╝
567
+
568
+ 2025-04-25 17:43:29,721 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
569
+
570
+ 2025-04-25 17:43:29,721 INFO ___FILE_ONLY___ ╠═ Installing: Bundled Python 3.12 (Platform Specific) ═╣
571
+
572
+ 2025-04-25 17:43:29,721 INFO ___FILE_ONLY___ ╚
573
+ 2025-04-25 17:43:32,837 INFO ___FILE_ONLY___ ═
574
+ 2025-04-25 17:43:33,330 INFO ___FILE_ONLY___ ═
575
+ 2025-04-25 17:43:33,352 INFO ___FILE_ONLY___ ═
576
+ 2025-04-25 17:43:33,388 INFO ___FILE_ONLY___ ═
577
+ 2025-04-25 17:43:33,410 INFO ___FILE_ONLY___ ═
578
+ 2025-04-25 17:43:33,426 INFO ___FILE_ONLY___ ═
579
+ 2025-04-25 17:43:33,457 INFO ___FILE_ONLY___ ═
580
+ 2025-04-25 17:43:33,478 INFO ___FILE_ONLY___ ═
581
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582
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583
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584
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585
+ 2025-04-25 17:43:33,703 INFO ___FILE_ONLY___ ═
586
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587
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588
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589
+ 2025-04-25 17:43:33,771 INFO ___FILE_ONLY___ ═
590
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591
+ 2025-04-25 17:43:33,808 INFO ___FILE_ONLY___ ═
592
+ 2025-04-25 17:43:33,829 INFO ___FILE_ONLY___ ═
593
+ 2025-04-25 17:43:33,846 INFO ___FILE_ONLY___ ═
594
+ 2025-04-25 17:43:33,943 INFO ___FILE_ONLY___ ═
595
+ 2025-04-25 17:43:33,965 INFO ___FILE_ONLY___ ═
596
+ 2025-04-25 17:43:34,463 INFO ___FILE_ONLY___ ═
597
+ 2025-04-25 17:43:34,480 INFO ___FILE_ONLY___ ═
598
+ 2025-04-25 17:43:34,495 INFO ___FILE_ONLY___ ═
599
+ 2025-04-25 17:43:34,508 INFO ___FILE_ONLY___ ═
600
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601
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602
+ 2025-04-25 17:43:34,548 INFO ___FILE_ONLY___ ═
603
+ 2025-04-25 17:43:34,560 INFO ___FILE_ONLY___ ═
604
+ 2025-04-25 17:43:34,572 INFO ___FILE_ONLY___ ═
605
+ 2025-04-25 17:43:34,585 INFO ___FILE_ONLY___ ═
606
+ 2025-04-25 17:43:34,598 INFO ___FILE_ONLY___ ═
607
+ 2025-04-25 17:43:34,611 INFO ___FILE_ONLY___ ═
608
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609
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610
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611
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612
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613
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614
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615
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616
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617
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618
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619
+ 2025-04-25 17:43:34,771 INFO ___FILE_ONLY___ ═
620
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621
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622
+ 2025-04-25 17:43:34,810 INFO ___FILE_ONLY___ ═
623
+ 2025-04-25 17:43:34,823 INFO ___FILE_ONLY___ ═
624
+ 2025-04-25 17:43:34,836 INFO ___FILE_ONLY___ ═
625
+ 2025-04-25 17:43:34,849 INFO ___FILE_ONLY___ ═
626
+ 2025-04-25 17:43:34,862 INFO ___FILE_ONLY___ ═
627
+ 2025-04-25 17:43:34,875 INFO ___FILE_ONLY___ ═
628
+ 2025-04-25 17:43:34,890 INFO ___FILE_ONLY___ ═
629
+ 2025-04-25 17:43:34,907 INFO ___FILE_ONLY___ ═
630
+ 2025-04-25 17:43:34,926 INFO ___FILE_ONLY___ ═
631
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632
+ 2025-04-25 17:43:34,961 INFO ___FILE_ONLY___ ═
633
+ 2025-04-25 17:43:34,962 INFO ___FILE_ONLY___ ╝
634
+
635
+ 2025-04-25 17:43:35,017 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
636
+
637
+ 2025-04-25 17:43:35,017 INFO ___FILE_ONLY___ ╠═ Installing: Cloud Storage Command Line Tool ═╣
638
+
639
+ 2025-04-25 17:43:35,017 INFO ___FILE_ONLY___ ╚
640
+ 2025-04-25 17:43:35,540 INFO ___FILE_ONLY___ ═
641
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642
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643
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644
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645
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646
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647
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648
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649
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650
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651
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652
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653
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654
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655
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656
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657
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658
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659
+ 2025-04-25 17:43:35,801 INFO ___FILE_ONLY___ ═
660
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661
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662
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663
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664
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665
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666
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667
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668
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669
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670
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671
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672
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673
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674
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675
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676
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677
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678
+ 2025-04-25 17:43:36,070 INFO ___FILE_ONLY___ ═
679
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680
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681
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682
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683
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684
+ 2025-04-25 17:43:36,139 INFO ___FILE_ONLY___ ═
685
+ 2025-04-25 17:43:36,149 INFO ___FILE_ONLY___ ═
686
+ 2025-04-25 17:43:36,159 INFO ___FILE_ONLY___ ═
687
+ 2025-04-25 17:43:36,169 INFO ___FILE_ONLY___ ═
688
+ 2025-04-25 17:43:36,180 INFO ___FILE_ONLY___ ═
689
+ 2025-04-25 17:43:36,193 INFO ___FILE_ONLY___ ═
690
+ 2025-04-25 17:43:36,207 INFO ___FILE_ONLY___ ═
691
+ 2025-04-25 17:43:36,222 INFO ___FILE_ONLY___ ═
692
+ 2025-04-25 17:43:36,234 INFO ___FILE_ONLY___ ═
693
+ 2025-04-25 17:43:36,248 INFO ___FILE_ONLY___ ═
694
+ 2025-04-25 17:43:36,261 INFO ___FILE_ONLY___ ═
695
+ 2025-04-25 17:43:36,276 INFO ___FILE_ONLY___ ═
696
+ 2025-04-25 17:43:36,294 INFO ___FILE_ONLY___ ═
697
+ 2025-04-25 17:43:36,315 INFO ___FILE_ONLY___ ═
698
+ 2025-04-25 17:43:36,332 INFO ___FILE_ONLY___ ═
699
+ 2025-04-25 17:43:36,351 INFO ___FILE_ONLY___ ═
700
+ 2025-04-25 17:43:36,351 INFO ___FILE_ONLY___ ╝
701
+
702
+ 2025-04-25 17:43:36,381 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
703
+
704
+ 2025-04-25 17:43:36,382 INFO ___FILE_ONLY___ ╠═ Installing: Cloud Storage Command Line Tool (Platform... ═╣
705
+
706
+ 2025-04-25 17:43:36,382 INFO ___FILE_ONLY___ ╚
707
+ 2025-04-25 17:43:36,382 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
708
+ 2025-04-25 17:43:36,383 INFO ___FILE_ONLY___ ╝
709
+
710
+ 2025-04-25 17:43:36,387 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
711
+
712
+ 2025-04-25 17:43:36,387 INFO ___FILE_ONLY___ ╠═ Installing: Default set of gcloud commands ═╣
713
+
714
+ 2025-04-25 17:43:36,387 INFO ___FILE_ONLY___ ╚
715
+ 2025-04-25 17:43:36,389 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
716
+ 2025-04-25 17:43:36,389 INFO ___FILE_ONLY___ ╝
717
+
718
+ 2025-04-25 17:43:36,391 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
719
+
720
+ 2025-04-25 17:43:36,391 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CLI Core Libraries (Platform... ═╣
721
+
722
+ 2025-04-25 17:43:36,391 INFO ___FILE_ONLY___ ╚
723
+ 2025-04-25 17:43:36,392 INFO ___FILE_ONLY___ ══════════════════════════════
724
+ 2025-04-25 17:43:36,392 INFO ___FILE_ONLY___ ══════════════════════════════
725
+ 2025-04-25 17:43:36,392 INFO ___FILE_ONLY___ ╝
726
+
727
+ 2025-04-25 17:43:36,396 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
728
+
729
+ 2025-04-25 17:43:36,396 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CRC32C Hash Tool ═╣
730
+
731
+ 2025-04-25 17:43:36,396 INFO ___FILE_ONLY___ ╚
732
+ 2025-04-25 17:43:36,398 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
733
+ 2025-04-25 17:43:36,398 INFO ___FILE_ONLY___ ╝
734
+
735
+ 2025-04-25 17:43:36,400 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
736
+
737
+ 2025-04-25 17:43:36,400 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CRC32C Hash Tool (Platform S... ═╣
738
+
739
+ 2025-04-25 17:43:36,400 INFO ___FILE_ONLY___ ╚
740
+ 2025-04-25 17:43:36,438 INFO ___FILE_ONLY___ ══════════════════════════════
741
+ 2025-04-25 17:43:36,438 INFO ___FILE_ONLY___ ══════════════════════════════
742
+ 2025-04-25 17:43:36,439 INFO ___FILE_ONLY___ ╝
743
+
744
+ 2025-04-25 17:43:36,443 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
745
+
746
+ 2025-04-25 17:43:36,444 INFO ___FILE_ONLY___ ╠═ Installing: gcloud cli dependencies (Platform Specific) ═╣
747
+
748
+ 2025-04-25 17:43:36,444 INFO ___FILE_ONLY___ ╚
749
+ 2025-04-25 17:43:36,444 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
750
+ 2025-04-25 17:43:36,444 INFO ___FILE_ONLY___ ╝
751
+
752
+ 2025-04-25 17:43:36,449 DEBUG root Updating notification cache...
753
+ 2025-04-25 17:43:36,449 INFO ___FILE_ONLY___
754
+
755
+ 2025-04-25 17:43:36,451 INFO ___FILE_ONLY___ Performing post processing steps...
756
+ 2025-04-25 17:43:36,451 DEBUG root Executing command: ['/tools/google-cloud-sdk/bin/gcloud', 'components', 'post-process']
757
+ 2025-04-25 17:43:45,301 DEBUG ___FILE_ONLY___
758
+ 2025-04-25 17:43:45,302 DEBUG ___FILE_ONLY___
759
+ 2025-04-25 17:43:45,495 INFO root descriptor_list: [{'universeDomain': 'googleapis.com', 'universeShortName': '', 'authenticationDomain': 'auth.cloud.google.com', 'projectPrefix': '', 'cloudWebDomain': 'cloud.google.com', 'documentationDomain': 'cloud.google.com', 'version': '1.0.0', 'state': 'primary', 'artifactRegistryDomain': 'pkg.dev'}]
760
+ 2025-04-25 17:43:45,495 INFO ___FILE_ONLY___
761
+ Update done!
762
+
763
+
764
+ 2025-04-25 17:43:45,498 DEBUG root Chosen display Format:none
765
+ 2025-04-25 17:43:45,498 INFO root Display format: "none"
.config/logs/2025.04.25/17.43.36.962838.log ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ 2025-04-25 17:43:36,963 DEBUG root Loaded Command Group: ['gcloud', 'components']
2
+ 2025-04-25 17:43:36,965 DEBUG root Loaded Command Group: ['gcloud', 'components', 'post_process']
3
+ 2025-04-25 17:43:36,967 DEBUG root Running [gcloud.components.post-process] with arguments: []
4
+ 2025-04-25 17:43:45,190 DEBUG root Chosen display Format:none
5
+ 2025-04-25 17:43:45,190 INFO root Display format: "none"
.config/logs/2025.04.25/17.43.46.133364.log ADDED
@@ -0,0 +1,153 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 2025-04-25 17:43:46,134 DEBUG root Loaded Command Group: ['gcloud', 'components']
2
+ 2025-04-25 17:43:46,136 DEBUG root Loaded Command Group: ['gcloud', 'components', 'update']
3
+ 2025-04-25 17:43:46,138 DEBUG root Running [gcloud.components.update] with arguments: [--quiet: "True", COMPONENT-IDS:8: "['gcloud', 'core', 'bq', 'gsutil', 'compute', 'preview', 'alpha', 'beta']"]
4
+ 2025-04-25 17:43:46,139 INFO ___FILE_ONLY___ Beginning update. This process may take several minutes.
5
+
6
+ 2025-04-25 17:43:46,147 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
7
+ 2025-04-25 17:43:46,388 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components-2.json HTTP/11" 200 226135
8
+ 2025-04-25 17:43:46,401 WARNING root Component [compute] no longer exists.
9
+ 2025-04-25 17:43:46,401 INFO ___FILE_ONLY___
10
+
11
+ 2025-04-25 17:43:46,402 INFO ___FILE_ONLY___
12
+ Your current Google Cloud CLI version is: 519.0.0
13
+
14
+ 2025-04-25 17:43:46,402 INFO ___FILE_ONLY___ Installing components from version: 519.0.0
15
+
16
+ 2025-04-25 17:43:46,402 INFO ___FILE_ONLY___
17
+
18
+ 2025-04-25 17:43:46,402 DEBUG root Chosen display Format:table[box,title="These components will be removed."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
19
+ 2025-04-25 17:43:46,403 DEBUG root Chosen display Format:table[box,title="These components will be updated."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
20
+ 2025-04-25 17:43:46,403 DEBUG root Chosen display Format:table[box,title="These components will be installed."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
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.gitattributes CHANGED
@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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sample_data/README.md ADDED
@@ -0,0 +1,19 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ This directory includes a few sample datasets to get you started.
2
+
3
+ * `california_housing_data*.csv` is California housing data from the 1990 US
4
+ Census; more information is available at:
5
+ https://docs.google.com/document/d/e/2PACX-1vRhYtsvc5eOR2FWNCwaBiKL6suIOrxJig8LcSBbmCbyYsayia_DvPOOBlXZ4CAlQ5nlDD8kTaIDRwrN/pub
6
+
7
+ * `mnist_*.csv` is a small sample of the
8
+ [MNIST database](https://en.wikipedia.org/wiki/MNIST_database), which is
9
+ described at: http://yann.lecun.com/exdb/mnist/
10
+
11
+ * `anscombe.json` contains a copy of
12
+ [Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet); it
13
+ was originally described in
14
+
15
+ Anscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American
16
+ Statistician. 27 (1): 17-21. JSTOR 2682899.
17
+
18
+ and our copy was prepared by the
19
+ [vega_datasets library](https://github.com/altair-viz/vega_datasets/blob/4f67bdaad10f45e3549984e17e1b3088c731503d/vega_datasets/_data/anscombe.json).
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@@ -0,0 +1,12 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Suggestions for improving training speed:
2
+
3
+ 1. Use mixed precision training (FP16/BF16)
4
+ 2. Increase batch size and use gradient accumulation
5
+ 3. Enable tensor core operations on compatible GPUs
6
+ 4. Optimize data loading with prefetching and parallel workers
7
+ 5. Use model parallelism or distributed training
8
+ 6. Consider using optimized implementations like FlashAttention
9
+ 7. Experiment with smaller model sizes or pruning
10
+ 8. Profile and optimize bottlenecks
11
+ 9. Use memory-efficient optimizers
12
+ 10. Consider efficient implementations like xformers or rotary embeddings
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1
+ import argparse
2
+ import os
3
+ import sys
4
+ import shutil
5
+ import random
6
+ import numpy as np
7
+ import time
8
+ import copy
9
+ import math
10
+ import matplotlib.pyplot as plt
11
+
12
+ import torch
13
+ import torch.nn.functional as F
14
+ import torch.nn as nn
15
+ from torch.autograd import Variable
16
+ import transformers
17
+ from transformers import GPT2TokenizerFast
18
+
19
+ # --- Mask for causal (autoregressive) attention ---
20
+ def subsequent_mask(size):
21
+ """Mask out subsequent positions."""
22
+ attn_shape = (1, size, size)
23
+ mask = torch.triu(torch.ones(attn_shape), diagonal=1).bool()
24
+ return mask
25
+
26
+ # --- Corpus reading ---
27
+ def read_corpus(filename, tokenizer):
28
+ print(f"Reading corpus from {filename}...")
29
+ seq = []
30
+ with open(filename, 'rt') as f:
31
+ for line in f:
32
+ line = line.rstrip('\n')
33
+ tokens = tokenizer(line)
34
+ seq.extend(tokens['input_ids'])
35
+ print(f"Read {len(seq)} tokens from {filename}")
36
+ return seq
37
+
38
+ class Embedder(nn.Module):
39
+ def __init__(self, vocab_size, d_model):
40
+ super().__init__()
41
+ self.d_model = d_model
42
+ self.embed = nn.Embedding(vocab_size, d_model)
43
+ def forward(self, x):
44
+ return self.embed(x.long())
45
+
46
+ class PositionalEncoder(nn.Module):
47
+ def __init__(self, d_model, max_seq_len = 4096, dropout = 0.1):
48
+ super().__init__()
49
+ self.d_model = d_model
50
+ self.dropout = nn.Dropout(dropout)
51
+ # create constant 'pe' matrix with values dependent on pos and i
52
+ pe = torch.zeros(max_seq_len, d_model)
53
+ for pos in range(max_seq_len):
54
+ for i in range(0, d_model, 2):
55
+ pe[pos, i] = math.sin(pos / (10000 ** ((2 * i)/d_model)))
56
+ if i + 1 < d_model:
57
+ pe[pos, i + 1] = math.cos(pos / (10000 ** ((2 * (i + 1))/d_model)))
58
+ pe = pe.unsqueeze(0)
59
+ self.register_buffer('pe', pe)
60
+
61
+ def forward(self, x):
62
+ # make embeddings relatively larger
63
+ x = x * math.sqrt(self.d_model)
64
+ # add constant to embedding
65
+ seq_len = x.size(1)
66
+ x = x + self.pe[:, :seq_len]
67
+ return self.dropout(x)
68
+
69
+ class Norm(nn.Module):
70
+ def __init__(self, d_model, eps=1e-6):
71
+ super().__init__()
72
+ self.size = d_model
73
+ # create two learnable parameters to calibrate normalisation
74
+ self.alpha = nn.Parameter(torch.ones(self.size))
75
+ self.bias = nn.Parameter(torch.zeros(self.size))
76
+ self.eps = eps
77
+
78
+ def forward(self, x):
79
+ norm = self.alpha * (x - x.mean(dim=-1, keepdim=True)) / \
80
+ (x.std(dim=-1, keepdim=True) + self.eps) + self.bias
81
+ return norm
82
+
83
+ def attention(q, k, v, d_k, mask=None, dropout=None):
84
+ scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)
85
+ if mask is not None:
86
+ mask = mask.unsqueeze(1)
87
+ scores = scores.masked_fill(mask == 0, -1e9)
88
+ scores = F.softmax(scores, dim=-1)
89
+ if dropout is not None:
90
+ scores = dropout(scores)
91
+ output = torch.matmul(scores, v)
92
+ return output
93
+
94
+ class MultiHeadAttention(nn.Module):
95
+ def __init__(self, heads, d_model, dropout=0.1):
96
+ super().__init__()
97
+ self.d_model = d_model
98
+ self.d_k = d_model // heads
99
+ self.h = heads
100
+ self.q_linear = nn.Linear(d_model, d_model)
101
+ self.v_linear = nn.Linear(d_model, d_model)
102
+ self.k_linear = nn.Linear(d_model, d_model)
103
+ self.dropout = nn.Dropout(dropout)
104
+ self.out = nn.Linear(d_model, d_model)
105
+
106
+ def forward(self, q, k, v, mask=None):
107
+ bs = q.size(0)
108
+ # perform linear operation and split into N heads
109
+ k = self.k_linear(k).view(bs, -1, self.h, self.d_k)
110
+ q = self.q_linear(q).view(bs, -1, self.h, self.d_k)
111
+ v = self.v_linear(v).view(bs, -1, self.h, self.d_k)
112
+ # transpose to get dimensions bs * N * sl * d_model
113
+ k = k.transpose(1,2)
114
+ q = q.transpose(1,2)
115
+ v = v.transpose(1,2)
116
+ # calculate attention
117
+ scores = attention(q, k, v, self.d_k, mask, self.dropout)
118
+ # concatenate heads and put through final linear layer
119
+ concat = scores.transpose(1,2).contiguous().view(bs, -1, self.d_model)
120
+ output = self.out(concat)
121
+ return output
122
+
123
+ class FeedForward(nn.Module):
124
+ def __init__(self, d_model, d_ff=2048, dropout=0.1):
125
+ super().__init__()
126
+ # We set d_ff as a default to 2048
127
+ self.linear_1 = nn.Linear(d_model, d_ff)
128
+ self.dropout = nn.Dropout(dropout)
129
+ self.linear_2 = nn.Linear(d_ff, d_model)
130
+
131
+ def forward(self, x):
132
+ x = self.dropout(F.relu(self.linear_1(x)))
133
+ x = self.linear_2(x)
134
+ return x
135
+
136
+ def get_clones(module, N):
137
+ return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
138
+
139
+ class DecoderLayer(nn.Module):
140
+ def __init__(self, d_model, heads, dropout=0.1):
141
+ super().__init__()
142
+ self.norm_1 = Norm(d_model)
143
+ self.norm_2 = Norm(d_model)
144
+ self.attn = MultiHeadAttention(heads, d_model, dropout)
145
+ self.ff = FeedForward(d_model, dropout=dropout)
146
+ self.dropout_1 = nn.Dropout(dropout)
147
+ self.dropout_2 = nn.Dropout(dropout)
148
+
149
+ def forward(self, x, trg_mask):
150
+ x2 = self.norm_1(x)
151
+ x = x + self.dropout_1(self.attn(x2, x2, x2, trg_mask))
152
+ x2 = self.norm_2(x)
153
+ x = x + self.dropout_2(self.ff(x2))
154
+ return x
155
+
156
+ class Decoder(nn.Module):
157
+ def __init__(self, vocab_size, d_model, N, heads, dropout):
158
+ super().__init__()
159
+ self.N = N
160
+ self.embed = Embedder(vocab_size, d_model)
161
+ self.pe = PositionalEncoder(d_model, dropout=dropout)
162
+ self.layers = get_clones(DecoderLayer(d_model, heads, dropout), N)
163
+ self.norm = Norm(d_model)
164
+
165
+ def forward(self, x, trg_mask):
166
+ x = self.embed(x)
167
+ x = self.pe(x)
168
+ for layer in self.layers:
169
+ x = layer(x, trg_mask)
170
+ return self.norm(x)
171
+
172
+ class GPT2LM(nn.Module):
173
+ def __init__(self, vocab_size, d_model, N, heads, dropout, tie_weights=False):
174
+ super().__init__()
175
+ self.decoder = Decoder(vocab_size, d_model, N, heads, dropout)
176
+ self.out = nn.Linear(d_model, vocab_size)
177
+ if tie_weights:
178
+ self.out.weight = self.decoder.embed.embed.weight
179
+ print("✅ Tied embeddings enabled.")
180
+
181
+ def forward(self, x, mask):
182
+ d_output = self.decoder(x, mask)
183
+ return self.out(d_output)
184
+
185
+ # --- Data batching for arbitrary sizes ---
186
+ def batchify(data, batch_size, seq_len):
187
+ nbatch = len(data) // batch_size
188
+ data = torch.tensor(data[:nbatch * batch_size], dtype=torch.long)
189
+ data = data.view(batch_size, -1)
190
+ for i in range(0, data.size(1) - 1, seq_len):
191
+ seq_len_i = min(seq_len, data.size(1) - 1 - i)
192
+ src = data[:, i:i + seq_len_i]
193
+ tgt = data[:, i + 1:i + 1 + seq_len_i]
194
+ yield src, tgt
195
+
196
+ # --- Training and evaluation loops with tokens per second reporting ---
197
+ def train_model(model, opt):
198
+ print("Starting training...")
199
+ model.train()
200
+ train_ppls = []
201
+ valid_ppls = []
202
+ total_tokens = 0
203
+ total_time = 0
204
+
205
+ for epoch in range(opt.epochs):
206
+ total_loss = 0
207
+ batches = 0
208
+ epoch_tokens = 0
209
+ epoch_start_time = time.time()
210
+
211
+ for src, tgt in batchify(opt.train, opt.batchsize, opt.seqlen):
212
+ batch_start_time = time.time()
213
+ src, tgt = src.to(opt.device), tgt.to(opt.device)
214
+ mask = subsequent_mask(src.size(1)).to(opt.device)
215
+ output = model(src, mask)
216
+ loss = F.cross_entropy(output.view(-1, opt.vocab_size), tgt.reshape(-1), ignore_index=opt.src_pad)
217
+ opt.optimizer.zero_grad()
218
+ loss.backward()
219
+ opt.optimizer.step()
220
+
221
+ batch_time = time.time() - batch_start_time
222
+ total_loss += loss.item()
223
+ batches += 1
224
+
225
+ # Count tokens in this batch: batch_size * sequence_length
226
+ tokens_in_batch = src.numel()
227
+ epoch_tokens += tokens_in_batch
228
+ total_tokens += tokens_in_batch
229
+ total_time += batch_time
230
+
231
+ tokens_per_sec = tokens_in_batch / batch_time
232
+ if batches % opt.printevery == 0:
233
+ print(f"Epoch {epoch+1}, Batch {batches}, Loss: {loss.item():.4f}, Speed: {tokens_per_sec:.2f} tokens/sec")
234
+
235
+ epoch_time = time.time() - epoch_start_time
236
+ epoch_tokens_per_sec = epoch_tokens / epoch_time
237
+
238
+ avg_loss = total_loss / batches
239
+ train_ppl = math.exp(avg_loss)
240
+ train_ppls.append(train_ppl)
241
+
242
+ print(f"Epoch {epoch+1}/{opt.epochs}, Loss: {avg_loss:.4f}, Perplexity: {train_ppl:.2f}")
243
+ print(f"Epoch training speed: {epoch_tokens_per_sec:.2f} tokens/sec")
244
+
245
+ valid_ppl = test_model(model, opt.valid, opt, tag=f"valid-epoch{epoch+1}")
246
+ valid_ppls.append(valid_ppl)
247
+
248
+ # Report final training speed
249
+ avg_tokens_per_sec = total_tokens / total_time
250
+ print(f"\nTraining completed.")
251
+ print(f"Average training speed: {avg_tokens_per_sec:.2f} tokens/sec")
252
+
253
+ # Save training speed to file
254
+ with open(os.path.join("saved", opt.dir_name, "training_speed.txt"), "w") as f:
255
+ f.write(f"Total tokens processed: {total_tokens}\n")
256
+ f.write(f"Total training time: {total_time:.2f} seconds\n")
257
+ f.write(f"Average training speed: {avg_tokens_per_sec:.2f} tokens/sec\n")
258
+
259
+ # Ensure directory exists
260
+ dir_name = os.path.join("saved", opt.dir_name)
261
+ if not os.path.exists(dir_name):
262
+ os.makedirs(dir_name)
263
+ print(f"Created directory: {dir_name}")
264
+
265
+ # Save the model
266
+ save_path = os.path.join(dir_name, "gpt2lm_wiki103.pth")
267
+ print(f"Saving model to: {save_path}")
268
+ print(f"Directory exists: {os.path.exists(dir_name)}")
269
+ print(f"Write permissions: {os.access(dir_name, os.W_OK)}")
270
+ torch.save(model.state_dict(), save_path)
271
+ print(f"Model saved successfully to {save_path}")
272
+
273
+ # Plot learning curve
274
+ plt.plot(range(1, opt.epochs+1), train_ppls, label='Train PPL')
275
+ plt.plot(range(1, opt.epochs+1), valid_ppls, label='Valid PPL')
276
+ plt.xlabel("Epoch")
277
+ plt.ylabel("Perplexity")
278
+ plt.legend()
279
+ plt.title("Training & Validation Perplexity")
280
+ plt.savefig(os.path.join(dir_name, "learning_curve.png"))
281
+ print(f"Saved learning curve to {dir_name}/learning_curve.png")
282
+
283
+ # Save perplexity log
284
+ with open(os.path.join(dir_name, "perplexity_log.txt"), "w") as f:
285
+ for i in range(opt.epochs):
286
+ f.write(f"Epoch {i+1}: Train PPL = {train_ppls[i]:.2f}, Valid PPL = {valid_ppls[i]:.2f}\n")
287
+
288
+ return avg_tokens_per_sec
289
+
290
+ def test_model(model, data, opt, tag="valid"):
291
+ print(f"Running {tag} set...")
292
+ model.eval()
293
+ total_loss, batches = 0, 0
294
+ with torch.no_grad():
295
+ for src, tgt in batchify(data, opt.batchsize, opt.seqlen):
296
+ src, tgt = src.to(opt.device), tgt.to(opt.device)
297
+ mask = subsequent_mask(src.size(1)).to(opt.device)
298
+ output = model(src, mask)
299
+ loss = F.cross_entropy(output.view(-1, opt.vocab_size), tgt.reshape(-1), ignore_index=opt.src_pad)
300
+ total_loss += loss.item()
301
+ batches += 1
302
+ avg_loss = total_loss / batches
303
+ ppl = math.exp(avg_loss)
304
+ print(f"{tag.capitalize()} PPL: {ppl:.2f}")
305
+ model.train()
306
+ return ppl
307
+
308
+ def main():
309
+ random.seed(10)
310
+ parser = argparse.ArgumentParser()
311
+ parser.add_argument('-no_cuda', action='store_true')
312
+ parser.add_argument('-SGDR', action='store_true')
313
+ parser.add_argument('-epochs', type=int, default=1) # Reduced for faster iteration
314
+ parser.add_argument('-d_model', type=int, default=512)
315
+ parser.add_argument('-n_layers', type=int, default=6)
316
+ parser.add_argument('-heads', type=int, default=8)
317
+ parser.add_argument('-dropout', type=float, default=0.1)
318
+ parser.add_argument('-batchsize', type=int, default=8) # Increased batch size
319
+ parser.add_argument('-printevery', type=int, default=100)
320
+ parser.add_argument('-lr', type=float, default=0.0001) # Slightly higher learning rate
321
+ parser.add_argument('-seqlen', type=int, default=512)
322
+ parser.add_argument('-threshold', type=int, default=3)
323
+ parser.add_argument('-savename', type=str)
324
+ parser.add_argument('-loadname', type=str)
325
+ parser.add_argument('-tied', type=int, default=1)
326
+ parser.add_argument('-dir_name', type=str, default='wiki103_model')
327
+ parser.add_argument('-norm', type=float, default=2.0)
328
+ opt = parser.parse_args()
329
+ opt.verbose = False
330
+
331
+ # Use GPU if available and not explicitly disabled
332
+ if not opt.no_cuda and torch.cuda.is_available():
333
+ opt.device = torch.device("cuda:0")
334
+ print(f"Using CUDA device: {torch.cuda.get_device_name(0)}")
335
+ else:
336
+ opt.device = torch.device("cpu")
337
+ print("Using CPU for training")
338
+
339
+ time_name = time.strftime("%y%m%d_%H%M%S")
340
+ opt.time_name = time_name
341
+ dir_name = "saved/%s" % (opt.dir_name)
342
+ if not os.path.exists(dir_name):
343
+ os.makedirs(dir_name)
344
+ source_name = sys.argv[0]
345
+ shutil.copy(source_name, dir_name + "/" + os.path.basename(source_name))
346
+ opt.log_file = dir_name + "/log_file.txt"
347
+ print(str(opt))
348
+
349
+ # Load and tokenize Wikitext-103 dataset
350
+ tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")
351
+ # Changed file paths for Wikitext-103
352
+ opt.train = read_corpus('wiki103.train.txt', tokenizer)
353
+ opt.valid = read_corpus('wiki103.valid.txt', tokenizer)
354
+ opt.test = read_corpus('wiki103.test.txt', tokenizer)
355
+
356
+ obs = len(opt.train)
357
+ print(f"Training set: {obs} tokens")
358
+
359
+ opt.vocab_size = 50257 # GPT-2 tokenizer vocabulary size
360
+ temp = []
361
+ for i in range(opt.vocab_size):
362
+ temp.append(i)
363
+ opt.indices = torch.tensor(temp).to(opt.device)
364
+
365
+ # Initialize model
366
+ model = GPT2LM(opt.vocab_size, opt.d_model, opt.n_layers, opt.heads, opt.dropout, tie_weights=(opt.tied == 1)).to(opt.device)
367
+ model_parameters = filter(lambda p: p.requires_grad, model.parameters())
368
+ params = sum([np.prod(p.size()) for p in model_parameters])
369
+ text = 'total params: %d' % (params)
370
+ print(text)
371
+
372
+ # Choose optimizer
373
+ opt.optimizer = torch.optim.Adam(model.parameters(), lr=opt.lr, betas=(0.9, 0.98), eps=1e-9)
374
+
375
+ if opt.savename is not None:
376
+ try:
377
+ os.mkdir(opt.savename)
378
+ except:
379
+ nothing = 1
380
+ opt.src_pad = 0
381
+ opt.trg_pad = 0
382
+
383
+ # Train and evaluate
384
+ avg_tokens_per_sec = train_model(model, opt)
385
+ test_model(model, opt.valid, opt, tag="valid")
386
+ test_model(model, opt.test, opt, tag="test")
387
+
388
+ print(f"\nFinal training speed: {avg_tokens_per_sec:.2f} tokens/sec")
389
+
390
+ # Write a summary of speed improvement suggestions
391
+ with open(os.path.join(dir_name, "speed_improvement_suggestions.txt"), "w") as f:
392
+ f.write("Suggestions for improving training speed:\n\n")
393
+ f.write("1. Use mixed precision training (FP16/BF16)\n")
394
+ f.write("2. Increase batch size and use gradient accumulation\n")
395
+ f.write("3. Enable tensor core operations on compatible GPUs\n")
396
+ f.write("4. Optimize data loading with prefetching and parallel workers\n")
397
+ f.write("5. Use model parallelism or distributed training\n")
398
+ f.write("6. Consider using optimized implementations like FlashAttention\n")
399
+ f.write("7. Experiment with smaller model sizes or pruning\n")
400
+ f.write("8. Profile and optimize bottlenecks\n")
401
+ f.write("9. Use memory-efficient optimizers\n")
402
+ f.write("10. Consider efficient implementations like xformers or rotary embeddings\n")
403
+
404
+ if __name__ == "__main__":
405
+ main()
saved/wiki103_model/training_speed.txt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ Total tokens processed: 117271896
2
+ Total training time: 709.94 seconds
3
+ Average training speed: 165186.40 tokens/sec
train.py ADDED
@@ -0,0 +1,405 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import argparse
2
+ import os
3
+ import sys
4
+ import shutil
5
+ import random
6
+ import numpy as np
7
+ import time
8
+ import copy
9
+ import math
10
+ import matplotlib.pyplot as plt
11
+
12
+ import torch
13
+ import torch.nn.functional as F
14
+ import torch.nn as nn
15
+ from torch.autograd import Variable
16
+ import transformers
17
+ from transformers import GPT2TokenizerFast
18
+
19
+ # --- Mask for causal (autoregressive) attention ---
20
+ def subsequent_mask(size):
21
+ """Mask out subsequent positions."""
22
+ attn_shape = (1, size, size)
23
+ mask = torch.triu(torch.ones(attn_shape), diagonal=1).bool()
24
+ return mask
25
+
26
+ # --- Corpus reading ---
27
+ def read_corpus(filename, tokenizer):
28
+ print(f"Reading corpus from {filename}...")
29
+ seq = []
30
+ with open(filename, 'rt') as f:
31
+ for line in f:
32
+ line = line.rstrip('\n')
33
+ tokens = tokenizer(line)
34
+ seq.extend(tokens['input_ids'])
35
+ print(f"Read {len(seq)} tokens from {filename}")
36
+ return seq
37
+
38
+ class Embedder(nn.Module):
39
+ def __init__(self, vocab_size, d_model):
40
+ super().__init__()
41
+ self.d_model = d_model
42
+ self.embed = nn.Embedding(vocab_size, d_model)
43
+ def forward(self, x):
44
+ return self.embed(x.long())
45
+
46
+ class PositionalEncoder(nn.Module):
47
+ def __init__(self, d_model, max_seq_len = 4096, dropout = 0.1):
48
+ super().__init__()
49
+ self.d_model = d_model
50
+ self.dropout = nn.Dropout(dropout)
51
+ # create constant 'pe' matrix with values dependent on pos and i
52
+ pe = torch.zeros(max_seq_len, d_model)
53
+ for pos in range(max_seq_len):
54
+ for i in range(0, d_model, 2):
55
+ pe[pos, i] = math.sin(pos / (10000 ** ((2 * i)/d_model)))
56
+ if i + 1 < d_model:
57
+ pe[pos, i + 1] = math.cos(pos / (10000 ** ((2 * (i + 1))/d_model)))
58
+ pe = pe.unsqueeze(0)
59
+ self.register_buffer('pe', pe)
60
+
61
+ def forward(self, x):
62
+ # make embeddings relatively larger
63
+ x = x * math.sqrt(self.d_model)
64
+ # add constant to embedding
65
+ seq_len = x.size(1)
66
+ x = x + self.pe[:, :seq_len]
67
+ return self.dropout(x)
68
+
69
+ class Norm(nn.Module):
70
+ def __init__(self, d_model, eps=1e-6):
71
+ super().__init__()
72
+ self.size = d_model
73
+ # create two learnable parameters to calibrate normalisation
74
+ self.alpha = nn.Parameter(torch.ones(self.size))
75
+ self.bias = nn.Parameter(torch.zeros(self.size))
76
+ self.eps = eps
77
+
78
+ def forward(self, x):
79
+ norm = self.alpha * (x - x.mean(dim=-1, keepdim=True)) / \
80
+ (x.std(dim=-1, keepdim=True) + self.eps) + self.bias
81
+ return norm
82
+
83
+ def attention(q, k, v, d_k, mask=None, dropout=None):
84
+ scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)
85
+ if mask is not None:
86
+ mask = mask.unsqueeze(1)
87
+ scores = scores.masked_fill(mask == 0, -1e9)
88
+ scores = F.softmax(scores, dim=-1)
89
+ if dropout is not None:
90
+ scores = dropout(scores)
91
+ output = torch.matmul(scores, v)
92
+ return output
93
+
94
+ class MultiHeadAttention(nn.Module):
95
+ def __init__(self, heads, d_model, dropout=0.1):
96
+ super().__init__()
97
+ self.d_model = d_model
98
+ self.d_k = d_model // heads
99
+ self.h = heads
100
+ self.q_linear = nn.Linear(d_model, d_model)
101
+ self.v_linear = nn.Linear(d_model, d_model)
102
+ self.k_linear = nn.Linear(d_model, d_model)
103
+ self.dropout = nn.Dropout(dropout)
104
+ self.out = nn.Linear(d_model, d_model)
105
+
106
+ def forward(self, q, k, v, mask=None):
107
+ bs = q.size(0)
108
+ # perform linear operation and split into N heads
109
+ k = self.k_linear(k).view(bs, -1, self.h, self.d_k)
110
+ q = self.q_linear(q).view(bs, -1, self.h, self.d_k)
111
+ v = self.v_linear(v).view(bs, -1, self.h, self.d_k)
112
+ # transpose to get dimensions bs * N * sl * d_model
113
+ k = k.transpose(1,2)
114
+ q = q.transpose(1,2)
115
+ v = v.transpose(1,2)
116
+ # calculate attention
117
+ scores = attention(q, k, v, self.d_k, mask, self.dropout)
118
+ # concatenate heads and put through final linear layer
119
+ concat = scores.transpose(1,2).contiguous().view(bs, -1, self.d_model)
120
+ output = self.out(concat)
121
+ return output
122
+
123
+ class FeedForward(nn.Module):
124
+ def __init__(self, d_model, d_ff=2048, dropout=0.1):
125
+ super().__init__()
126
+ # We set d_ff as a default to 2048
127
+ self.linear_1 = nn.Linear(d_model, d_ff)
128
+ self.dropout = nn.Dropout(dropout)
129
+ self.linear_2 = nn.Linear(d_ff, d_model)
130
+
131
+ def forward(self, x):
132
+ x = self.dropout(F.relu(self.linear_1(x)))
133
+ x = self.linear_2(x)
134
+ return x
135
+
136
+ def get_clones(module, N):
137
+ return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
138
+
139
+ class DecoderLayer(nn.Module):
140
+ def __init__(self, d_model, heads, dropout=0.1):
141
+ super().__init__()
142
+ self.norm_1 = Norm(d_model)
143
+ self.norm_2 = Norm(d_model)
144
+ self.attn = MultiHeadAttention(heads, d_model, dropout)
145
+ self.ff = FeedForward(d_model, dropout=dropout)
146
+ self.dropout_1 = nn.Dropout(dropout)
147
+ self.dropout_2 = nn.Dropout(dropout)
148
+
149
+ def forward(self, x, trg_mask):
150
+ x2 = self.norm_1(x)
151
+ x = x + self.dropout_1(self.attn(x2, x2, x2, trg_mask))
152
+ x2 = self.norm_2(x)
153
+ x = x + self.dropout_2(self.ff(x2))
154
+ return x
155
+
156
+ class Decoder(nn.Module):
157
+ def __init__(self, vocab_size, d_model, N, heads, dropout):
158
+ super().__init__()
159
+ self.N = N
160
+ self.embed = Embedder(vocab_size, d_model)
161
+ self.pe = PositionalEncoder(d_model, dropout=dropout)
162
+ self.layers = get_clones(DecoderLayer(d_model, heads, dropout), N)
163
+ self.norm = Norm(d_model)
164
+
165
+ def forward(self, x, trg_mask):
166
+ x = self.embed(x)
167
+ x = self.pe(x)
168
+ for layer in self.layers:
169
+ x = layer(x, trg_mask)
170
+ return self.norm(x)
171
+
172
+ class GPT2LM(nn.Module):
173
+ def __init__(self, vocab_size, d_model, N, heads, dropout, tie_weights=False):
174
+ super().__init__()
175
+ self.decoder = Decoder(vocab_size, d_model, N, heads, dropout)
176
+ self.out = nn.Linear(d_model, vocab_size)
177
+ if tie_weights:
178
+ self.out.weight = self.decoder.embed.embed.weight
179
+ print("✅ Tied embeddings enabled.")
180
+
181
+ def forward(self, x, mask):
182
+ d_output = self.decoder(x, mask)
183
+ return self.out(d_output)
184
+
185
+ # --- Data batching for arbitrary sizes ---
186
+ def batchify(data, batch_size, seq_len):
187
+ nbatch = len(data) // batch_size
188
+ data = torch.tensor(data[:nbatch * batch_size], dtype=torch.long)
189
+ data = data.view(batch_size, -1)
190
+ for i in range(0, data.size(1) - 1, seq_len):
191
+ seq_len_i = min(seq_len, data.size(1) - 1 - i)
192
+ src = data[:, i:i + seq_len_i]
193
+ tgt = data[:, i + 1:i + 1 + seq_len_i]
194
+ yield src, tgt
195
+
196
+ # --- Training and evaluation loops with tokens per second reporting ---
197
+ def train_model(model, opt):
198
+ print("Starting training...")
199
+ model.train()
200
+ train_ppls = []
201
+ valid_ppls = []
202
+ total_tokens = 0
203
+ total_time = 0
204
+
205
+ for epoch in range(opt.epochs):
206
+ total_loss = 0
207
+ batches = 0
208
+ epoch_tokens = 0
209
+ epoch_start_time = time.time()
210
+
211
+ for src, tgt in batchify(opt.train, opt.batchsize, opt.seqlen):
212
+ batch_start_time = time.time()
213
+ src, tgt = src.to(opt.device), tgt.to(opt.device)
214
+ mask = subsequent_mask(src.size(1)).to(opt.device)
215
+ output = model(src, mask)
216
+ loss = F.cross_entropy(output.view(-1, opt.vocab_size), tgt.reshape(-1), ignore_index=opt.src_pad)
217
+ opt.optimizer.zero_grad()
218
+ loss.backward()
219
+ opt.optimizer.step()
220
+
221
+ batch_time = time.time() - batch_start_time
222
+ total_loss += loss.item()
223
+ batches += 1
224
+
225
+ # Count tokens in this batch: batch_size * sequence_length
226
+ tokens_in_batch = src.numel()
227
+ epoch_tokens += tokens_in_batch
228
+ total_tokens += tokens_in_batch
229
+ total_time += batch_time
230
+
231
+ tokens_per_sec = tokens_in_batch / batch_time
232
+ if batches % opt.printevery == 0:
233
+ print(f"Epoch {epoch+1}, Batch {batches}, Loss: {loss.item():.4f}, Speed: {tokens_per_sec:.2f} tokens/sec")
234
+
235
+ epoch_time = time.time() - epoch_start_time
236
+ epoch_tokens_per_sec = epoch_tokens / epoch_time
237
+
238
+ avg_loss = total_loss / batches
239
+ train_ppl = math.exp(avg_loss)
240
+ train_ppls.append(train_ppl)
241
+
242
+ print(f"Epoch {epoch+1}/{opt.epochs}, Loss: {avg_loss:.4f}, Perplexity: {train_ppl:.2f}")
243
+ print(f"Epoch training speed: {epoch_tokens_per_sec:.2f} tokens/sec")
244
+
245
+ valid_ppl = test_model(model, opt.valid, opt, tag=f"valid-epoch{epoch+1}")
246
+ valid_ppls.append(valid_ppl)
247
+
248
+ # Report final training speed
249
+ avg_tokens_per_sec = total_tokens / total_time
250
+ print(f"\nTraining completed.")
251
+ print(f"Average training speed: {avg_tokens_per_sec:.2f} tokens/sec")
252
+
253
+ # Save training speed to file
254
+ with open(os.path.join("saved", opt.dir_name, "training_speed.txt"), "w") as f:
255
+ f.write(f"Total tokens processed: {total_tokens}\n")
256
+ f.write(f"Total training time: {total_time:.2f} seconds\n")
257
+ f.write(f"Average training speed: {avg_tokens_per_sec:.2f} tokens/sec\n")
258
+
259
+ # Ensure directory exists
260
+ dir_name = os.path.join("saved", opt.dir_name)
261
+ if not os.path.exists(dir_name):
262
+ os.makedirs(dir_name)
263
+ print(f"Created directory: {dir_name}")
264
+
265
+ # Save the model
266
+ save_path = os.path.join(dir_name, "gpt2lm_wiki103.pth")
267
+ print(f"Saving model to: {save_path}")
268
+ print(f"Directory exists: {os.path.exists(dir_name)}")
269
+ print(f"Write permissions: {os.access(dir_name, os.W_OK)}")
270
+ torch.save(model.state_dict(), save_path)
271
+ print(f"Model saved successfully to {save_path}")
272
+
273
+ # Plot learning curve
274
+ plt.plot(range(1, opt.epochs+1), train_ppls, label='Train PPL')
275
+ plt.plot(range(1, opt.epochs+1), valid_ppls, label='Valid PPL')
276
+ plt.xlabel("Epoch")
277
+ plt.ylabel("Perplexity")
278
+ plt.legend()
279
+ plt.title("Training & Validation Perplexity")
280
+ plt.savefig(os.path.join(dir_name, "learning_curve.png"))
281
+ print(f"Saved learning curve to {dir_name}/learning_curve.png")
282
+
283
+ # Save perplexity log
284
+ with open(os.path.join(dir_name, "perplexity_log.txt"), "w") as f:
285
+ for i in range(opt.epochs):
286
+ f.write(f"Epoch {i+1}: Train PPL = {train_ppls[i]:.2f}, Valid PPL = {valid_ppls[i]:.2f}\n")
287
+
288
+ return avg_tokens_per_sec
289
+
290
+ def test_model(model, data, opt, tag="valid"):
291
+ print(f"Running {tag} set...")
292
+ model.eval()
293
+ total_loss, batches = 0, 0
294
+ with torch.no_grad():
295
+ for src, tgt in batchify(data, opt.batchsize, opt.seqlen):
296
+ src, tgt = src.to(opt.device), tgt.to(opt.device)
297
+ mask = subsequent_mask(src.size(1)).to(opt.device)
298
+ output = model(src, mask)
299
+ loss = F.cross_entropy(output.view(-1, opt.vocab_size), tgt.reshape(-1), ignore_index=opt.src_pad)
300
+ total_loss += loss.item()
301
+ batches += 1
302
+ avg_loss = total_loss / batches
303
+ ppl = math.exp(avg_loss)
304
+ print(f"{tag.capitalize()} PPL: {ppl:.2f}")
305
+ model.train()
306
+ return ppl
307
+
308
+ def main():
309
+ random.seed(10)
310
+ parser = argparse.ArgumentParser()
311
+ parser.add_argument('-no_cuda', action='store_true')
312
+ parser.add_argument('-SGDR', action='store_true')
313
+ parser.add_argument('-epochs', type=int, default=1) # Reduced for faster iteration
314
+ parser.add_argument('-d_model', type=int, default=512)
315
+ parser.add_argument('-n_layers', type=int, default=6)
316
+ parser.add_argument('-heads', type=int, default=8)
317
+ parser.add_argument('-dropout', type=float, default=0.1)
318
+ parser.add_argument('-batchsize', type=int, default=8) # Increased batch size
319
+ parser.add_argument('-printevery', type=int, default=100)
320
+ parser.add_argument('-lr', type=float, default=0.0001) # Slightly higher learning rate
321
+ parser.add_argument('-seqlen', type=int, default=512)
322
+ parser.add_argument('-threshold', type=int, default=3)
323
+ parser.add_argument('-savename', type=str)
324
+ parser.add_argument('-loadname', type=str)
325
+ parser.add_argument('-tied', type=int, default=1)
326
+ parser.add_argument('-dir_name', type=str, default='wiki103_model')
327
+ parser.add_argument('-norm', type=float, default=2.0)
328
+ opt = parser.parse_args()
329
+ opt.verbose = False
330
+
331
+ # Use GPU if available and not explicitly disabled
332
+ if not opt.no_cuda and torch.cuda.is_available():
333
+ opt.device = torch.device("cuda:0")
334
+ print(f"Using CUDA device: {torch.cuda.get_device_name(0)}")
335
+ else:
336
+ opt.device = torch.device("cpu")
337
+ print("Using CPU for training")
338
+
339
+ time_name = time.strftime("%y%m%d_%H%M%S")
340
+ opt.time_name = time_name
341
+ dir_name = "saved/%s" % (opt.dir_name)
342
+ if not os.path.exists(dir_name):
343
+ os.makedirs(dir_name)
344
+ source_name = sys.argv[0]
345
+ shutil.copy(source_name, dir_name + "/" + os.path.basename(source_name))
346
+ opt.log_file = dir_name + "/log_file.txt"
347
+ print(str(opt))
348
+
349
+ # Load and tokenize Wikitext-103 dataset
350
+ tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")
351
+ # Changed file paths for Wikitext-103
352
+ opt.train = read_corpus('wiki103.train.txt', tokenizer)
353
+ opt.valid = read_corpus('wiki103.valid.txt', tokenizer)
354
+ opt.test = read_corpus('wiki103.test.txt', tokenizer)
355
+
356
+ obs = len(opt.train)
357
+ print(f"Training set: {obs} tokens")
358
+
359
+ opt.vocab_size = 50257 # GPT-2 tokenizer vocabulary size
360
+ temp = []
361
+ for i in range(opt.vocab_size):
362
+ temp.append(i)
363
+ opt.indices = torch.tensor(temp).to(opt.device)
364
+
365
+ # Initialize model
366
+ model = GPT2LM(opt.vocab_size, opt.d_model, opt.n_layers, opt.heads, opt.dropout, tie_weights=(opt.tied == 1)).to(opt.device)
367
+ model_parameters = filter(lambda p: p.requires_grad, model.parameters())
368
+ params = sum([np.prod(p.size()) for p in model_parameters])
369
+ text = 'total params: %d' % (params)
370
+ print(text)
371
+
372
+ # Choose optimizer
373
+ opt.optimizer = torch.optim.Adam(model.parameters(), lr=opt.lr, betas=(0.9, 0.98), eps=1e-9)
374
+
375
+ if opt.savename is not None:
376
+ try:
377
+ os.mkdir(opt.savename)
378
+ except:
379
+ nothing = 1
380
+ opt.src_pad = 0
381
+ opt.trg_pad = 0
382
+
383
+ # Train and evaluate
384
+ avg_tokens_per_sec = train_model(model, opt)
385
+ test_model(model, opt.valid, opt, tag="valid")
386
+ test_model(model, opt.test, opt, tag="test")
387
+
388
+ print(f"\nFinal training speed: {avg_tokens_per_sec:.2f} tokens/sec")
389
+
390
+ # Write a summary of speed improvement suggestions
391
+ with open(os.path.join(dir_name, "speed_improvement_suggestions.txt"), "w") as f:
392
+ f.write("Suggestions for improving training speed:\n\n")
393
+ f.write("1. Use mixed precision training (FP16/BF16)\n")
394
+ f.write("2. Increase batch size and use gradient accumulation\n")
395
+ f.write("3. Enable tensor core operations on compatible GPUs\n")
396
+ f.write("4. Optimize data loading with prefetching and parallel workers\n")
397
+ f.write("5. Use model parallelism or distributed training\n")
398
+ f.write("6. Consider using optimized implementations like FlashAttention\n")
399
+ f.write("7. Experiment with smaller model sizes or pruning\n")
400
+ f.write("8. Profile and optimize bottlenecks\n")
401
+ f.write("9. Use memory-efficient optimizers\n")
402
+ f.write("10. Consider efficient implementations like xformers or rotary embeddings\n")
403
+
404
+ if __name__ == "__main__":
405
+ main()
wiki103.test.txt ADDED
The diff for this file is too large to render. See raw diff
 
wiki103.train.txt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:2082f99929dee10a3b96da7d6ef7ca4dbb4d7df9d51db264f14173a36989f826
3
+ size 539209157
wiki103.valid.txt ADDED
The diff for this file is too large to render. See raw diff