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InternLM2-SFT-SCDPO

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  1. README.md +132 -3
  2. added_tokens.json +10 -0
  3. all_results.json +21 -0
  4. config.json +33 -0
  5. configuration_internlm2.py +151 -0
  6. eval_results.json +16 -0
  7. generation_config.json +7 -0
  8. model-00001-of-00009.safetensors +3 -0
  9. model-00002-of-00009.safetensors +3 -0
  10. model-00003-of-00009.safetensors +3 -0
  11. model-00004-of-00009.safetensors +3 -0
  12. model-00005-of-00009.safetensors +3 -0
  13. model-00006-of-00009.safetensors +3 -0
  14. model-00007-of-00009.safetensors +3 -0
  15. model-00008-of-00009.safetensors +3 -0
  16. model-00009-of-00009.safetensors +3 -0
  17. model.safetensors.index.json +346 -0
  18. modeling_internlm2.py +1391 -0
  19. runs/May14_07-37-31_pt-p1l1oepj-master-0/events.out.tfevents.1715644019.pt-p1l1oepj-master-0.53.0 +3 -0
  20. runs/May14_07-37-31_pt-p1l1oepj-master-0/events.out.tfevents.1715693335.pt-p1l1oepj-master-0.53.1 +3 -0
  21. special_tokens_map.json +40 -0
  22. tokenization_internlm2.py +236 -0
  23. tokenization_internlm2_fast.py +214 -0
  24. tokenizer.json +0 -0
  25. tokenizer.model +3 -0
  26. tokenizer_config.json +120 -0
  27. train_results.json +8 -0
  28. trainer_state.json +0 -0
  29. training_args.bin +3 -0
README.md CHANGED
@@ -1,3 +1,132 @@
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- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ base_model: InternLM2-SFT
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+ tags:
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+ - alignment-handbook
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+ - generated_from_trainer
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+ model-index:
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+ - name: InternLM2-SFT-SCDPO
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # InternLM2-SFT-SCDPO
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+
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+ This model is a fine-tuned version of the InternLM2-20B model using SFT and SCDPO.
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+ It achieves the following results on the evaluation set:
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+
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+ - Loss: 0.2572
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+ - Rewards/chosen: 0.7366
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+ - Rewards/rejected: -2.9817
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+ - Rewards/accuracies: 0.8929
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+ - Rewards/margins: 3.7183
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+ - Logps/rejected: -155.1884
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+ - Logps/chosen: -92.5904
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+ - Logits/rejected: -2.3032
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+ - Logits/chosen: -2.4880
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+
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+ ## Model description
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+
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+ This is a model fine-tuned for mathematical problem-solving.
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+
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+ ## Intended uses & limitations
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+
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+ The model is intended for solving math problems.
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+
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+ ## Training and evaluation data
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+
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+ | | gsm8k | math | ape | cmath | mgsm_zh |
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+ | ------------------------------ | -------- | -------- | -------- | -------- | -------- |
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+ | InternLM2-SFT | 86.4 | 55.8 | 77.1 | 88.4 | 74.8 |
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+ | InternLM2-SFT-DPO | 87 | 57.6 | 78.7 | 89.9 | 76 |
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+ | InternLM2-SFT-DPO (data-equal) | 88.2 | 57.5 | 78.8 | 89.3 | 76 |
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+ | InternLM2-SFT-SCDPO | **88.5** | **58.1** | **79.3** | **90.3** | **80.4** |
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+
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+
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 1.5e-07
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+ - train_batch_size: 2
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+ - eval_batch_size: 4
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+ - seed: 42
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+ - distributed_type: multi-GPU
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+ - num_devices: 16
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+ - total_train_batch_size: 32
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+ - total_eval_batch_size: 64
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: cosine
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 2
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
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+ | 0.6953 | 0.04 | 100 | 0.6942 | 0.0113 | 0.0155 | 0.5357 | -0.0042 | -125.2162 | -99.8434 | -2.2407 | -2.5020 |
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+ | 0.6799 | 0.07 | 200 | 0.6807 | 0.1508 | 0.0991 | 0.7857 | 0.0517 | -124.3804 | -98.4486 | -2.2421 | -2.5021 |
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+ | 0.6328 | 0.11 | 300 | 0.6358 | 0.6189 | 0.4340 | 0.8214 | 0.1850 | -121.0318 | -93.7668 | -2.2536 | -2.5140 |
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+ | 0.5964 | 0.15 | 400 | 0.5837 | 1.3765 | 0.9356 | 0.7679 | 0.4409 | -116.0156 | -86.1915 | -2.2736 | -2.5321 |
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+ | 0.5567 | 0.18 | 500 | 0.5355 | 1.8935 | 1.1726 | 0.8393 | 0.7208 | -113.6451 | -81.0216 | -2.3121 | -2.5696 |
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+ | 0.5234 | 0.22 | 600 | 0.4918 | 2.0220 | 1.0473 | 0.8571 | 0.9747 | -114.8989 | -79.7362 | -2.3776 | -2.6315 |
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+ | 0.4468 | 0.25 | 700 | 0.4557 | 2.0942 | 0.8329 | 0.8571 | 1.2613 | -117.0424 | -79.0143 | -2.4109 | -2.6510 |
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+ | 0.4445 | 0.29 | 800 | 0.4324 | 1.6535 | 0.1909 | 0.8929 | 1.4627 | -123.4627 | -83.4209 | -2.3914 | -2.6052 |
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+ | 0.4219 | 0.33 | 900 | 0.4065 | 1.6377 | -0.0568 | 0.9107 | 1.6945 | -125.9393 | -83.5790 | -2.3703 | -2.5723 |
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+ | 0.4453 | 0.36 | 1000 | 0.3895 | 1.5452 | -0.2159 | 0.9286 | 1.7611 | -127.5303 | -84.5038 | -2.4480 | -2.6886 |
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+ | 0.372 | 0.4 | 1100 | 0.3739 | 1.6809 | -0.3796 | 0.8929 | 2.0605 | -129.1675 | -83.1477 | -2.3805 | -2.6049 |
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+ | 0.3191 | 0.44 | 1200 | 0.3635 | 1.3180 | -0.7374 | 0.875 | 2.0554 | -132.7456 | -86.7765 | -2.3742 | -2.6045 |
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+ | 0.3319 | 0.47 | 1300 | 0.3556 | 1.9033 | -0.2537 | 0.875 | 2.1570 | -127.9082 | -80.9230 | -2.4013 | -2.6385 |
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+ | 0.3469 | 0.51 | 1400 | 0.3462 | 1.4997 | -0.7342 | 0.8929 | 2.2339 | -132.7133 | -84.9589 | -2.3969 | -2.6331 |
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+ | 0.2976 | 0.54 | 1500 | 0.3363 | 1.3404 | -0.9610 | 0.8929 | 2.3014 | -134.9813 | -86.5523 | -2.3936 | -2.6264 |
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+ | 0.2839 | 0.58 | 1600 | 0.3325 | 1.5509 | -0.9441 | 0.8571 | 2.4950 | -134.8124 | -84.4469 | -2.3639 | -2.5862 |
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+ | 0.3095 | 0.62 | 1700 | 0.3237 | 1.1956 | -1.3235 | 0.8929 | 2.5190 | -138.6060 | -88.0005 | -2.3813 | -2.6024 |
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+ | 0.2593 | 0.65 | 1800 | 0.3188 | 1.2642 | -1.2408 | 0.8393 | 2.5050 | -137.7795 | -87.3140 | -2.3843 | -2.6131 |
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+ | 0.2394 | 0.69 | 1900 | 0.3111 | 1.3483 | -1.1915 | 0.8393 | 2.5398 | -137.2868 | -86.4737 | -2.3883 | -2.6142 |
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+ | 0.3234 | 0.73 | 2000 | 0.3054 | 1.3375 | -1.2938 | 0.8393 | 2.6313 | -138.3099 | -86.5813 | -2.3702 | -2.5965 |
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+ | 0.2532 | 0.76 | 2100 | 0.3038 | 1.3038 | -1.4223 | 0.8571 | 2.7261 | -139.5943 | -86.9184 | -2.3411 | -2.5582 |
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+ | 0.2862 | 0.8 | 2200 | 0.2990 | 0.9190 | -1.6769 | 0.8929 | 2.5959 | -142.1404 | -90.7659 | -2.3869 | -2.6118 |
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+ | 0.2972 | 0.83 | 2300 | 0.2962 | 1.3084 | -1.4975 | 0.8571 | 2.8059 | -140.3468 | -86.8725 | -2.3515 | -2.5680 |
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+ | 0.2819 | 0.87 | 2400 | 0.2932 | 1.0128 | -1.7007 | 0.875 | 2.7134 | -142.3783 | -89.8287 | -2.3793 | -2.5986 |
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+ | 0.2523 | 0.91 | 2500 | 0.2887 | 1.2417 | -1.6991 | 0.8571 | 2.9408 | -142.3625 | -87.5393 | -2.3238 | -2.5295 |
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+ | 0.2534 | 0.94 | 2600 | 0.2876 | 1.0492 | -1.8303 | 0.8214 | 2.8795 | -143.6740 | -89.4638 | -2.3452 | -2.5586 |
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+ | 0.2065 | 0.98 | 2700 | 0.2806 | 0.8720 | -1.9390 | 0.8571 | 2.8109 | -144.7613 | -91.2366 | -2.3644 | -2.5848 |
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+ | 0.1669 | 1.02 | 2800 | 0.2792 | 1.1195 | -2.0235 | 0.875 | 3.1430 | -145.6067 | -88.7613 | -2.2594 | -2.4376 |
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+ | 0.2042 | 1.05 | 2900 | 0.2784 | 0.9707 | -2.2345 | 0.8929 | 3.2052 | -147.7169 | -90.2493 | -2.2788 | -2.4633 |
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+ | 0.1529 | 1.09 | 3000 | 0.2779 | 1.0919 | -2.2812 | 0.8929 | 3.3732 | -148.1836 | -89.0369 | -2.2437 | -2.4170 |
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+ | 0.1675 | 1.13 | 3100 | 0.2778 | 1.1553 | -2.3082 | 0.8929 | 3.4635 | -148.4539 | -88.4035 | -2.2311 | -2.3938 |
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+ | 0.1542 | 1.16 | 3200 | 0.2764 | 0.9350 | -2.5717 | 0.875 | 3.5067 | -151.0882 | -90.6065 | -2.2590 | -2.4346 |
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+ | 0.1694 | 1.2 | 3300 | 0.2728 | 0.8873 | -2.5803 | 0.8929 | 3.4676 | -151.1744 | -91.0834 | -2.2580 | -2.4308 |
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+ | 0.1763 | 1.23 | 3400 | 0.2699 | 0.8933 | -2.6673 | 0.8929 | 3.5606 | -152.0449 | -91.0235 | -2.2436 | -2.4141 |
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+ | 0.1526 | 1.27 | 3500 | 0.2666 | 0.8111 | -2.8087 | 0.875 | 3.6198 | -153.4587 | -91.8457 | -2.2513 | -2.4212 |
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+ | 0.1819 | 1.31 | 3600 | 0.2657 | 0.8766 | -2.7215 | 0.8929 | 3.5981 | -152.5868 | -91.1904 | -2.2677 | -2.4482 |
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+ | 0.1192 | 1.34 | 3700 | 0.2634 | 0.8643 | -2.7682 | 0.8929 | 3.6325 | -153.0537 | -91.3131 | -2.2649 | -2.4439 |
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+ | 0.144 | 1.38 | 3800 | 0.2639 | 0.7928 | -2.8695 | 0.8929 | 3.6623 | -154.0669 | -92.0286 | -2.2687 | -2.4451 |
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+ | 0.1603 | 1.42 | 3900 | 0.2631 | 0.9961 | -2.6996 | 0.875 | 3.6957 | -152.3678 | -89.9953 | -2.2720 | -2.4476 |
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+ | 0.2054 | 1.45 | 4000 | 0.2591 | 0.7753 | -2.8107 | 0.8929 | 3.5860 | -153.4788 | -92.2032 | -2.3164 | -2.5069 |
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+ | 0.1413 | 1.49 | 4100 | 0.2604 | 0.7969 | -2.8696 | 0.8929 | 3.6664 | -154.0672 | -91.9876 | -2.2993 | -2.4855 |
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+ | 0.1498 | 1.52 | 4200 | 0.2579 | 0.7998 | -2.8686 | 0.8929 | 3.6684 | -154.0579 | -91.9587 | -2.3021 | -2.4863 |
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+ | 0.174 | 1.56 | 4300 | 0.2609 | 0.7800 | -3.0276 | 0.8929 | 3.8076 | -155.6475 | -92.1565 | -2.2400 | -2.4000 |
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+ | 0.1129 | 1.6 | 4400 | 0.2576 | 0.7289 | -2.9876 | 0.8929 | 3.7165 | -155.2476 | -92.6674 | -2.2932 | -2.4758 |
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+ | 0.1424 | 1.63 | 4500 | 0.2585 | 0.7887 | -2.9462 | 0.8929 | 3.7349 | -154.8336 | -92.0694 | -2.3023 | -2.4859 |
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+ | 0.1531 | 1.67 | 4600 | 0.2570 | 0.7661 | -2.9310 | 0.8929 | 3.6971 | -154.6814 | -92.2956 | -2.3090 | -2.4970 |
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+ | 0.1295 | 1.71 | 4700 | 0.2564 | 0.6730 | -3.0214 | 0.8929 | 3.6944 | -155.5855 | -93.2258 | -2.3086 | -2.4952 |
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+ | 0.1277 | 1.74 | 4800 | 0.2575 | 0.6849 | -2.9809 | 0.8929 | 3.6658 | -155.1802 | -93.1070 | -2.3162 | -2.5054 |
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+ | 0.1166 | 1.78 | 4900 | 0.2568 | 0.7091 | -2.9715 | 0.8929 | 3.6807 | -155.0869 | -92.8652 | -2.3099 | -2.4983 |
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+ | 0.1273 | 1.81 | 5000 | 0.2564 | 0.7316 | -2.9732 | 0.8929 | 3.7049 | -155.1039 | -92.6402 | -2.3100 | -2.4976 |
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+ | 0.1221 | 1.85 | 5100 | 0.2575 | 0.7393 | -2.9714 | 0.9107 | 3.7107 | -155.0852 | -92.5628 | -2.2967 | -2.4797 |
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+ | 0.1752 | 1.89 | 5200 | 0.2568 | 0.7439 | -2.9732 | 0.9107 | 3.7171 | -155.1031 | -92.5173 | -2.2978 | -2.4828 |
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+ | 0.1235 | 1.92 | 5300 | 0.2566 | 0.7413 | -2.9777 | 0.8929 | 3.7190 | -155.1483 | -92.5434 | -2.3077 | -2.4947 |
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+ | 0.1288 | 1.96 | 5400 | 0.2569 | 0.7385 | -2.9689 | 0.9107 | 3.7074 | -155.0609 | -92.5715 | -2.3040 | -2.4889 |
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+ | 0.145 | 2.0 | 5500 | 0.2572 | 0.7366 | -2.9817 | 0.8929 | 3.7183 | -155.1884 | -92.5904 | -2.3032 | -2.4880 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.38.2
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+ - Pytorch 2.1.2
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+ - Datasets 2.14.6
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+ - Tokenizers 0.15.2
added_tokens.json ADDED
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+ {
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+ "<|assistant|>": 92546,
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+ "<|code|>": 92548,
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+ "<|endofblock|>": 92550,
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+ "<|endofmessage|>": 92551,
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+ "<|execution|>": 92549,
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+ "<|system|>": 92545,
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+ "<|text|>": 92547,
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+ "<|user|>": 92544
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+ }
all_results.json ADDED
@@ -0,0 +1,21 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "epoch": 2.0,
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+ "eval_logits/chosen": -2.4878430366516113,
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+ "eval_logits/rejected": -2.303501605987549,
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+ "eval_logps/chosen": -92.59371185302734,
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+ "eval_logps/rejected": -155.12913513183594,
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+ "eval_loss": 0.2571355104446411,
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+ "eval_rewards/accuracies": 0.8928571343421936,
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+ "eval_rewards/chosen": 0.7362550497055054,
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+ "eval_rewards/margins": 3.712023973464966,
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+ "eval_rewards/rejected": -2.975768804550171,
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+ "eval_runtime": 71.4209,
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+ "eval_samples": 890,
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+ "eval_samples_per_second": 12.461,
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+ "eval_steps_per_second": 0.196,
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+ "train_loss": 0.2711078498627443,
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+ "train_runtime": 49070.973,
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+ "train_samples": 88135,
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+ "train_samples_per_second": 3.592,
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+ "train_steps_per_second": 0.112
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+ }
config.json ADDED
@@ -0,0 +1,33 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "_name_or_path": "/mnt/cache/luzimu/rlhf_math/alignment-handbook/outs/internlm2-20b_ape_th1_169161_gsm8k_math_81087",
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+ "architectures": [
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+ "InternLM2ForCausalLM"
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+ ],
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+ "attn_implementation": "eager",
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+ "auto_map": {
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+ "AutoConfig": "configuration_internlm2.InternLM2Config",
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+ "AutoModel": "modeling_internlm2.InternLM2ForCausalLM",
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+ "AutoModelForCausalLM": "modeling_internlm2.InternLM2ForCausalLM"
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+ },
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+ "bias": false,
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+ "bos_token_id": 1,
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+ "eos_token_id": 2,
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+ "hidden_act": "silu",
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+ "hidden_size": 6144,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 16384,
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+ "max_position_embeddings": 32768,
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+ "model_type": "internlm2",
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+ "num_attention_heads": 48,
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+ "num_hidden_layers": 48,
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+ "num_key_value_heads": 8,
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+ "pad_token_id": 2,
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+ "rms_norm_eps": 1e-05,
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+ "rope_scaling": null,
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+ "rope_theta": 1000000,
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "bfloat16",
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+ "transformers_version": "4.38.2",
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+ "use_cache": true,
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+ "vocab_size": 92672
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+ }
configuration_internlm2.py ADDED
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+ # coding=utf-8
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+ # Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
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+ #
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+ # This code is based on transformers/src/transformers/models/llama/configuration_llama.py
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
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+ # you may not use this file except in compliance with the License.
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+ # You may obtain a copy of the License at
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+ #
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+ # http://www.apache.org/licenses/LICENSE-2.0
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+ #
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+ # Unless required by applicable law or agreed to in writing, software
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+ # distributed under the License is distributed on an "AS IS" BASIS,
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+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+ # See the License for the specific language governing permissions and
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+ # limitations under the License.
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+ """ InternLM2 model configuration"""
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+
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+ from transformers.configuration_utils import PretrainedConfig
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+ from transformers.utils import logging
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+
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+ logger = logging.get_logger(__name__)
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+
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+ INTERNLM2_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
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+
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+
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+ # Modified from transformers.model.llama.configuration_llama.LlamaConfig
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+ class InternLM2Config(PretrainedConfig):
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+ r"""
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+ This is the configuration class to store the configuration of a [`InternLM2Model`]. It is used to instantiate
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+ an InternLM2 model according to the specified arguments, defining the model architecture. Instantiating a
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+ configuration with the defaults will yield a similar configuration to that of the InternLM2-7B.
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+
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+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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+ documentation from [`PretrainedConfig`] for more information.
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+
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+
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+ Args:
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+ vocab_size (`int`, *optional*, defaults to 32000):
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+ Vocabulary size of the InternLM2 model. Defines the number of different tokens that can be represented by the
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+ `inputs_ids` passed when calling [`InternLM2Model`]
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+ hidden_size (`int`, *optional*, defaults to 4096):
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+ Dimension of the hidden representations.
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+ intermediate_size (`int`, *optional*, defaults to 11008):
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+ Dimension of the MLP representations.
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+ num_hidden_layers (`int`, *optional*, defaults to 32):
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+ Number of hidden layers in the Transformer encoder.
48
+ num_attention_heads (`int`, *optional*, defaults to 32):
49
+ Number of attention heads for each attention layer in the Transformer encoder.
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+ num_key_value_heads (`int`, *optional*):
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+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
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+ `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
54
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
55
+ by meanpooling all the original heads within that group. For more details checkout [this
56
+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
57
+ `num_attention_heads`.
58
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
59
+ The non-linear activation function (function or string) in the decoder.
60
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
61
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
62
+ just in case (e.g., 512 or 1024 or 2048).
63
+ initializer_range (`float`, *optional*, defaults to 0.02):
64
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
65
+ rms_norm_eps (`float`, *optional*, defaults to 1e-12):
66
+ The epsilon used by the rms normalization layers.
67
+ use_cache (`bool`, *optional*, defaults to `True`):
68
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
69
+ relevant if `config.is_decoder=True`.
70
+ tie_word_embeddings(`bool`, *optional*, defaults to `False`):
71
+ Whether to tie weight embeddings
72
+ Example:
73
+
74
+ """
75
+ model_type = "internlm2"
76
+ _auto_class = "AutoConfig"
77
+
78
+ def __init__( # pylint: disable=W0102
79
+ self,
80
+ vocab_size=103168,
81
+ hidden_size=4096,
82
+ intermediate_size=11008,
83
+ num_hidden_layers=32,
84
+ num_attention_heads=32,
85
+ num_key_value_heads=None,
86
+ hidden_act="silu",
87
+ max_position_embeddings=2048,
88
+ initializer_range=0.02,
89
+ rms_norm_eps=1e-6,
90
+ use_cache=True,
91
+ pad_token_id=0,
92
+ bos_token_id=1,
93
+ eos_token_id=2,
94
+ tie_word_embeddings=False,
95
+ bias=True,
96
+ rope_theta=10000,
97
+ rope_scaling=None,
98
+ attn_implementation="eager",
99
+ **kwargs,
100
+ ):
101
+ self.vocab_size = vocab_size
102
+ self.max_position_embeddings = max_position_embeddings
103
+ self.hidden_size = hidden_size
104
+ self.intermediate_size = intermediate_size
105
+ self.num_hidden_layers = num_hidden_layers
106
+ self.num_attention_heads = num_attention_heads
107
+ self.bias = bias
108
+
109
+ if num_key_value_heads is None:
110
+ num_key_value_heads = num_attention_heads
111
+ self.num_key_value_heads = num_key_value_heads
112
+
113
+ self.hidden_act = hidden_act
114
+ self.initializer_range = initializer_range
115
+ self.rms_norm_eps = rms_norm_eps
116
+ self.use_cache = use_cache
117
+ self.rope_theta = rope_theta
118
+ self.rope_scaling = rope_scaling
119
+ self._rope_scaling_validation()
120
+
121
+ self.attn_implementation = attn_implementation
122
+ if self.attn_implementation is None:
123
+ self.attn_implementation = "eager"
124
+ super().__init__(
125
+ pad_token_id=pad_token_id,
126
+ bos_token_id=bos_token_id,
127
+ eos_token_id=eos_token_id,
128
+ tie_word_embeddings=tie_word_embeddings,
129
+ **kwargs,
130
+ )
131
+
132
+ def _rope_scaling_validation(self):
133
+ """
134
+ Validate the `rope_scaling` configuration.
135
+ """
136
+ if self.rope_scaling is None:
137
+ return
138
+
139
+ if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
140
+ raise ValueError(
141
+ "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
142
+ f"got {self.rope_scaling}"
143
+ )
144
+ rope_scaling_type = self.rope_scaling.get("type", None)
145
+ rope_scaling_factor = self.rope_scaling.get("factor", None)
146
+ if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
147
+ raise ValueError(
148
+ f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
149
+ )
150
+ if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor < 1.0:
151
+ raise ValueError(f"`rope_scaling`'s factor field must be a float >= 1, got {rope_scaling_factor}")
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+ "eval_samples": 890,
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+ "eval_samples_per_second": 12.461,
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+ "eval_steps_per_second": 0.196
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+ }
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+ }
modeling_internlm2.py ADDED
@@ -0,0 +1,1391 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
2
+ #
3
+ # This code is based on transformers/src/transformers/models/llama/modeling_llama.py
4
+ #
5
+ # Licensed under the Apache License, Version 2.0 (the "License");
6
+ # you may not use this file except in compliance with the License.
7
+ # You may obtain a copy of the License at
8
+ #
9
+ # http://www.apache.org/licenses/LICENSE-2.0
10
+ #
11
+ # Unless required by applicable law or agreed to in writing, software
12
+ # distributed under the License is distributed on an "AS IS" BASIS,
13
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14
+ # See the License for the specific language governing permissions and
15
+ # limitations under the License.
16
+ """ PyTorch InternLM2 model."""
17
+ import math
18
+ import queue
19
+ import threading
20
+ import warnings
21
+ from typing import List, Optional, Tuple, Union
22
+
23
+ import torch
24
+ import torch.nn.functional as F
25
+ import torch.utils.checkpoint
26
+ from einops import rearrange
27
+ from torch import nn
28
+ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
29
+ from transformers.activations import ACT2FN
30
+ from transformers.modeling_outputs import (
31
+ BaseModelOutputWithPast,
32
+ CausalLMOutputWithPast,
33
+ SequenceClassifierOutputWithPast,
34
+ )
35
+ from transformers.modeling_utils import PreTrainedModel
36
+ from transformers.utils import (
37
+ add_start_docstrings,
38
+ add_start_docstrings_to_model_forward,
39
+ logging,
40
+ replace_return_docstrings,
41
+ )
42
+
43
+ try:
44
+ from transformers.generation.streamers import BaseStreamer
45
+ except: # noqa # pylint: disable=bare-except
46
+ BaseStreamer = None
47
+
48
+ from .configuration_internlm2 import InternLM2Config
49
+
50
+ logger = logging.get_logger(__name__)
51
+
52
+ _CONFIG_FOR_DOC = "InternLM2Config"
53
+
54
+ flash_attn_func, flash_attn_varlen_func = None, None
55
+ pad_input, index_first_axis, unpad_input = None, None, None
56
+ def _import_flash_attn():
57
+ global flash_attn_func, flash_attn_varlen_func
58
+ global pad_input, index_first_axis, unpad_input
59
+ try:
60
+ from flash_attn import flash_attn_func as _flash_attn_func, flash_attn_varlen_func as _flash_attn_varlen_func
61
+ from flash_attn.bert_padding import pad_input as _pad_input, index_first_axis as _index_first_axis, unpad_input as _unpad_input
62
+ flash_attn_func, flash_attn_varlen_func = _flash_attn_func, _flash_attn_varlen_func
63
+ pad_input, index_first_axis, unpad_input = _pad_input, _index_first_axis, _unpad_input
64
+ except ImportError:
65
+ raise ImportError("flash_attn is not installed.")
66
+
67
+ # Copied from transformers.models.llama.modeling_llama._get_unpad_data
68
+ def _get_unpad_data(attention_mask):
69
+ seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
70
+ indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
71
+ max_seqlen_in_batch = seqlens_in_batch.max().item()
72
+ cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
73
+ return (
74
+ indices,
75
+ cu_seqlens,
76
+ max_seqlen_in_batch,
77
+ )
78
+
79
+
80
+ # Copied from transformers.models.bart.modeling_bart._make_causal_mask
81
+ def _make_causal_mask(
82
+ input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
83
+ ):
84
+ """
85
+ Make causal mask used for bi-directional self-attention.
86
+ """
87
+ bsz, tgt_len = input_ids_shape
88
+ mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
89
+ mask_cond = torch.arange(mask.size(-1), device=device)
90
+ mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
91
+ mask = mask.to(dtype)
92
+
93
+ if past_key_values_length > 0:
94
+ mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
95
+ return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
96
+
97
+
98
+ # Copied from transformers.models.bart.modeling_bart._expand_mask
99
+ def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
100
+ """
101
+ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
102
+ """
103
+ bsz, src_len = mask.size()
104
+ tgt_len = tgt_len if tgt_len is not None else src_len
105
+
106
+ expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
107
+
108
+ inverted_mask = 1.0 - expanded_mask
109
+
110
+ return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
111
+
112
+
113
+ # Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->InternLM2
114
+ class InternLM2RMSNorm(nn.Module):
115
+ def __init__(self, hidden_size, eps=1e-6):
116
+ """
117
+ InternLM2RMSNorm is equivalent to T5LayerNorm
118
+ """
119
+ super().__init__()
120
+ self.weight = nn.Parameter(torch.ones(hidden_size))
121
+ self.variance_epsilon = eps
122
+
123
+ def forward(self, hidden_states):
124
+ input_dtype = hidden_states.dtype
125
+ hidden_states = hidden_states.to(torch.float32)
126
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
127
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
128
+ return self.weight * hidden_states.to(input_dtype)
129
+
130
+
131
+ # Copied from transformers.model.llama.modeling_llama.LlamaRotaryEmbedding with Llama->InternLM2
132
+ class InternLM2RotaryEmbedding(nn.Module):
133
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
134
+ super().__init__()
135
+
136
+ self.dim = dim
137
+ self.max_position_embeddings = max_position_embeddings
138
+ self.base = base
139
+ inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
140
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
141
+
142
+ # Build here to make `torch.jit.trace` work.
143
+ self._set_cos_sin_cache(
144
+ seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
145
+ )
146
+
147
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
148
+ self.max_seq_len_cached = seq_len
149
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
150
+
151
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
152
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
153
+ emb = torch.cat((freqs, freqs), dim=-1)
154
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
155
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
156
+
157
+ def forward(self, x, seq_len=None):
158
+ # x: [bs, num_attention_heads, seq_len, head_size]
159
+ if seq_len > self.max_seq_len_cached:
160
+ self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=torch.float32)
161
+
162
+ return (
163
+ self.cos_cached[:seq_len].to(dtype=x.dtype),
164
+ self.sin_cached[:seq_len].to(dtype=x.dtype),
165
+ )
166
+
167
+
168
+ # Copied from transformers.model.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->InternLM2
169
+ class InternLM2LinearScalingRotaryEmbedding(InternLM2RotaryEmbedding):
170
+ """InternLM2RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
171
+
172
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
173
+ self.scaling_factor = scaling_factor
174
+ super().__init__(dim, max_position_embeddings, base, device)
175
+
176
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
177
+ self.max_seq_len_cached = seq_len
178
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
179
+ t = t / self.scaling_factor
180
+
181
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
182
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
183
+ emb = torch.cat((freqs, freqs), dim=-1)
184
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
185
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
186
+
187
+
188
+ # Copied from transformers.model.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->InternLM2
189
+ class InternLM2DynamicNTKScalingRotaryEmbedding(InternLM2RotaryEmbedding):
190
+ """InternLM2RotaryEmbedding extended with Dynamic NTK scaling.
191
+ Credits to the Reddit users /u/bloc97 and /u/emozilla.
192
+ """
193
+
194
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
195
+ self.scaling_factor = scaling_factor
196
+ super().__init__(dim, max_position_embeddings, base, device)
197
+
198
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
199
+ self.max_seq_len_cached = seq_len
200
+
201
+ if seq_len > self.max_position_embeddings:
202
+ base = self.base * (
203
+ (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
204
+ ) ** (self.dim / (self.dim - 2))
205
+ inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
206
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
207
+
208
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
209
+
210
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
211
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
212
+ emb = torch.cat((freqs, freqs), dim=-1)
213
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
214
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
215
+
216
+
217
+ # Copied from transformers.model.llama.modeling_llama.rotate_half
218
+ def rotate_half(x):
219
+ """Rotates half the hidden dims of the input."""
220
+ x1 = x[..., : x.shape[-1] // 2]
221
+ x2 = x[..., x.shape[-1] // 2 :]
222
+ return torch.cat((-x2, x1), dim=-1)
223
+
224
+
225
+ # Copied from transformers.model.llama.modeling_llama.apply_rotary_pos_emb
226
+ def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
227
+ """Applies Rotary Position Embedding to the query and key tensors."""
228
+ cos = cos[position_ids].unsqueeze(unsqueeze_dim)
229
+ sin = sin[position_ids].unsqueeze(unsqueeze_dim)
230
+ q_embed = (q * cos) + (rotate_half(q) * sin)
231
+ k_embed = (k * cos) + (rotate_half(k) * sin)
232
+ return q_embed, k_embed
233
+
234
+
235
+ class InternLM2MLP(nn.Module):
236
+ def __init__(self, config):
237
+ super().__init__()
238
+ self.config = config
239
+ self.hidden_size = config.hidden_size
240
+ self.intermediate_size = config.intermediate_size
241
+ self.w1 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
242
+ self.w3 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
243
+ self.w2 = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
244
+ self.act_fn = ACT2FN[config.hidden_act]
245
+
246
+ def forward(self, x):
247
+ down_proj = self.w2(self.act_fn(self.w1(x)) * self.w3(x))
248
+
249
+ return down_proj
250
+
251
+
252
+ # Copied from transformers.model.llama.modeling_llama.repeat_kv
253
+ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
254
+ """
255
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
256
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
257
+ """
258
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
259
+ if n_rep == 1:
260
+ return hidden_states
261
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
262
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
263
+
264
+
265
+ # Modified from transformers.model.llama.modeling_llama.LlamaAttention
266
+ class InternLM2Attention(nn.Module):
267
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
268
+
269
+ def __init__(self, config: InternLM2Config):
270
+ super().__init__()
271
+ self.config = config
272
+ self.hidden_size = config.hidden_size
273
+ self.num_heads = config.num_attention_heads
274
+ self.head_dim = self.hidden_size // self.num_heads
275
+ self.num_key_value_heads = config.num_key_value_heads
276
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
277
+ self.max_position_embeddings = config.max_position_embeddings
278
+ self.is_causal = True
279
+
280
+ if (self.head_dim * self.num_heads) != self.hidden_size:
281
+ raise ValueError(
282
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
283
+ f" and `num_heads`: {self.num_heads})."
284
+ )
285
+
286
+ self.wqkv = nn.Linear(
287
+ self.hidden_size,
288
+ (self.num_heads + 2 * self.num_key_value_heads) * self.head_dim,
289
+ bias=config.bias,
290
+ )
291
+
292
+ self.wo = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.bias)
293
+ self._init_rope()
294
+
295
+ def _init_rope(self):
296
+ if self.config.rope_scaling is None:
297
+ self.rotary_emb = InternLM2RotaryEmbedding(
298
+ self.head_dim,
299
+ max_position_embeddings=self.max_position_embeddings,
300
+ base=self.config.rope_theta,
301
+ )
302
+ else:
303
+ scaling_type = self.config.rope_scaling["type"]
304
+ scaling_factor = self.config.rope_scaling["factor"]
305
+ if scaling_type == "dynamic":
306
+ self.rotary_emb = InternLM2DynamicNTKScalingRotaryEmbedding(
307
+ self.head_dim,
308
+ max_position_embeddings=self.max_position_embeddings,
309
+ base=self.config.rope_theta,
310
+ scaling_factor=scaling_factor,
311
+ )
312
+ elif scaling_type == "linear":
313
+ self.rotary_emb = InternLM2LinearScalingRotaryEmbedding(
314
+ self.head_dim,
315
+ max_position_embeddings=self.max_position_embeddings,
316
+ base=self.config.rope_theta,
317
+ scaling_factor=scaling_factor,
318
+ )
319
+ else:
320
+ raise ValueError("Currently we only support rotary embedding's type being 'dynamic' or 'linear'.")
321
+ return self.rotary_emb
322
+
323
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
324
+ return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
325
+
326
+ def forward(
327
+ self,
328
+ hidden_states: torch.Tensor,
329
+ attention_mask: Optional[torch.Tensor] = None,
330
+ position_ids: Optional[torch.LongTensor] = None,
331
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
332
+ output_attentions: bool = False,
333
+ use_cache: bool = False,
334
+ **kwargs,
335
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
336
+ if "padding_mask" in kwargs:
337
+ warnings.warn(
338
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. "
339
+ "Please make sure use `attention_mask` instead.`"
340
+ )
341
+
342
+ bsz, q_len, _ = hidden_states.size()
343
+
344
+ qkv_states = self.wqkv(hidden_states)
345
+
346
+ qkv_states = rearrange(
347
+ qkv_states,
348
+ "b q (h gs d) -> b q h gs d",
349
+ gs=2 + self.num_key_value_groups,
350
+ d=self.head_dim,
351
+ )
352
+
353
+ query_states = qkv_states[..., : self.num_key_value_groups, :]
354
+ query_states = rearrange(query_states, "b q h gs d -> b q (h gs) d")
355
+ key_states = qkv_states[..., -2, :]
356
+ value_states = qkv_states[..., -1, :]
357
+
358
+ query_states = query_states.transpose(1, 2)
359
+ key_states = key_states.transpose(1, 2)
360
+ value_states = value_states.transpose(1, 2)
361
+
362
+ kv_seq_len = key_states.shape[-2]
363
+ if past_key_value is not None:
364
+ kv_seq_len += past_key_value[0].shape[-2]
365
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
366
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
367
+
368
+ if past_key_value is not None:
369
+ # reuse k, v, self_attention
370
+ key_states = torch.cat([past_key_value[0], key_states], dim=2)
371
+ value_states = torch.cat([past_key_value[1], value_states], dim=2)
372
+
373
+ past_key_value = (key_states, value_states) if use_cache else None
374
+
375
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
376
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
377
+
378
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
379
+
380
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
381
+ raise ValueError(
382
+ f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
383
+ f" {attn_weights.size()}"
384
+ )
385
+
386
+ if attention_mask is not None:
387
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
388
+ raise ValueError(
389
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
390
+ )
391
+ attn_weights = attn_weights + attention_mask
392
+
393
+ # upcast attention to fp32
394
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
395
+ attn_output = torch.matmul(attn_weights, value_states)
396
+
397
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
398
+ raise ValueError(
399
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
400
+ f" {attn_output.size()}"
401
+ )
402
+
403
+ attn_output = attn_output.transpose(1, 2).contiguous()
404
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
405
+
406
+ attn_output = self.wo(attn_output)
407
+
408
+ if not output_attentions:
409
+ attn_weights = None
410
+
411
+ return attn_output, attn_weights, past_key_value
412
+
413
+
414
+ # Modified from transformers.model.llama.modeling_llama.InternLM2FlashAttention2
415
+ class InternLM2FlashAttention2(InternLM2Attention):
416
+ """
417
+ InternLM2 flash attention module. This module inherits from `InternLM2Attention` as the weights of the module stays
418
+ untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
419
+ flash attention and deal with padding tokens in case the input contains any of them.
420
+ """
421
+
422
+ def forward(
423
+ self,
424
+ hidden_states: torch.Tensor,
425
+ attention_mask: Optional[torch.LongTensor] = None,
426
+ position_ids: Optional[torch.LongTensor] = None,
427
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
428
+ output_attentions: bool = False,
429
+ use_cache: bool = False,
430
+ **kwargs,
431
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
432
+ # InternLM2FlashAttention2 attention does not support output_attentions
433
+ if "padding_mask" in kwargs:
434
+ warnings.warn(
435
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. "
436
+ "Please make sure use `attention_mask` instead.`"
437
+ )
438
+
439
+ # overwrite attention_mask with padding_mask
440
+ attention_mask = kwargs.pop("padding_mask")
441
+
442
+ output_attentions = False
443
+
444
+ bsz, q_len, _ = hidden_states.size()
445
+
446
+ qkv_states = self.wqkv(hidden_states)
447
+
448
+ qkv_states = rearrange(
449
+ qkv_states,
450
+ "b q (h gs d) -> b q h gs d",
451
+ gs=2 + self.num_key_value_groups,
452
+ d=self.head_dim,
453
+ )
454
+
455
+ query_states = qkv_states[..., : self.num_key_value_groups, :]
456
+ query_states = rearrange(query_states, "b q h gs d -> b q (h gs) d")
457
+ key_states = qkv_states[..., -2, :]
458
+ value_states = qkv_states[..., -1, :]
459
+
460
+ query_states = query_states.transpose(1, 2)
461
+ key_states = key_states.transpose(1, 2)
462
+ value_states = value_states.transpose(1, 2)
463
+
464
+ kv_seq_len = key_states.shape[-2]
465
+ if past_key_value is not None:
466
+ kv_seq_len += past_key_value[0].shape[-2]
467
+
468
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
469
+
470
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
471
+
472
+ if past_key_value is not None:
473
+ # reuse k, v, self_attention
474
+ key_states = torch.cat([past_key_value[0], key_states], dim=2)
475
+ value_states = torch.cat([past_key_value[1], value_states], dim=2)
476
+
477
+ past_key_value = (key_states, value_states) if use_cache else None
478
+
479
+ query_states = query_states.transpose(1, 2)
480
+ key_states = key_states.transpose(1, 2)
481
+ value_states = value_states.transpose(1, 2)
482
+
483
+ attn_output = self._flash_attention_forward(
484
+ query_states, key_states, value_states, attention_mask, q_len
485
+ )
486
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
487
+ attn_output = self.wo(attn_output)
488
+
489
+ if not output_attentions:
490
+ attn_weights = None
491
+
492
+ return attn_output, attn_weights, past_key_value
493
+
494
+ def _flash_attention_forward(
495
+ self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
496
+ ):
497
+ """
498
+ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
499
+ first unpad the input, then computes the attention scores and pad the final attention scores.
500
+
501
+ Args:
502
+ query_states (`torch.Tensor`):
503
+ Input query states to be passed to Flash Attention API
504
+ key_states (`torch.Tensor`):
505
+ Input key states to be passed to Flash Attention API
506
+ value_states (`torch.Tensor`):
507
+ Input value states to be passed to Flash Attention API
508
+ attention_mask (`torch.Tensor`):
509
+ The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
510
+ position of padding tokens and 1 for the position of non-padding tokens.
511
+ dropout (`int`, *optional*):
512
+ Attention dropout
513
+ softmax_scale (`float`, *optional*):
514
+ The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
515
+ """
516
+ # Contains at least one padding token in the sequence
517
+ causal = self.is_causal and query_length != 1
518
+ if attention_mask is not None:
519
+ batch_size = query_states.shape[0]
520
+ query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._unpad_input(
521
+ query_states, key_states, value_states, attention_mask, query_length
522
+ )
523
+
524
+ cu_seqlens_q, cu_seqlens_k = cu_seq_lens
525
+ max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
526
+
527
+ attn_output_unpad = flash_attn_varlen_func(
528
+ query_states,
529
+ key_states,
530
+ value_states,
531
+ cu_seqlens_q=cu_seqlens_q,
532
+ cu_seqlens_k=cu_seqlens_k,
533
+ max_seqlen_q=max_seqlen_in_batch_q,
534
+ max_seqlen_k=max_seqlen_in_batch_k,
535
+ dropout_p=dropout,
536
+ softmax_scale=softmax_scale,
537
+ causal=causal,
538
+ )
539
+
540
+ attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
541
+ else:
542
+ attn_output = flash_attn_func(
543
+ query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
544
+ )
545
+
546
+ return attn_output
547
+
548
+ def _unpad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
549
+ indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
550
+ batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
551
+
552
+ key_layer = index_first_axis(
553
+ key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
554
+ )
555
+ value_layer = index_first_axis(
556
+ value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
557
+ )
558
+
559
+ if query_length == kv_seq_len:
560
+ query_layer = index_first_axis(
561
+ query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
562
+ )
563
+ cu_seqlens_q = cu_seqlens_k
564
+ max_seqlen_in_batch_q = max_seqlen_in_batch_k
565
+ indices_q = indices_k
566
+ elif query_length == 1:
567
+ max_seqlen_in_batch_q = 1
568
+ cu_seqlens_q = torch.arange(
569
+ batch_size + 1, dtype=torch.int32, device=query_layer.device
570
+ ) # There is a memcpy here, that is very bad.
571
+ indices_q = cu_seqlens_q[:-1]
572
+ query_layer = query_layer.squeeze(1)
573
+ else:
574
+ # The -q_len: slice assumes left padding.
575
+ attention_mask = attention_mask[:, -query_length:]
576
+ query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
577
+
578
+ return (
579
+ query_layer,
580
+ key_layer,
581
+ value_layer,
582
+ indices_q.to(torch.int64),
583
+ (cu_seqlens_q, cu_seqlens_k),
584
+ (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
585
+ )
586
+
587
+ INTERNLM2_ATTENTION_CLASSES = {
588
+ "eager": InternLM2Attention,
589
+ "flash_attention_2": InternLM2FlashAttention2,
590
+ }
591
+
592
+ # Modified from transformers.model.llama.modeling_llama.LlamaDecoderLayer
593
+ class InternLM2DecoderLayer(nn.Module):
594
+ def __init__(self, config: InternLM2Config):
595
+ super().__init__()
596
+ self.hidden_size = config.hidden_size
597
+
598
+ self.attention = INTERNLM2_ATTENTION_CLASSES[config.attn_implementation](config=config)
599
+
600
+ self.feed_forward = InternLM2MLP(config)
601
+ self.attention_norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
602
+ self.ffn_norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
603
+
604
+ def forward(
605
+ self,
606
+ hidden_states: torch.Tensor,
607
+ attention_mask: Optional[torch.Tensor] = None,
608
+ position_ids: Optional[torch.LongTensor] = None,
609
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
610
+ output_attentions: Optional[bool] = False,
611
+ use_cache: Optional[bool] = False,
612
+ **kwargs,
613
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
614
+ """
615
+ Args:
616
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
617
+ attention_mask (`torch.FloatTensor`, *optional*):
618
+ attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
619
+ query_sequence_length, key_sequence_length)` if default attention is used.
620
+ output_attentions (`bool`, *optional*):
621
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
622
+ returned tensors for more detail.
623
+ use_cache (`bool`, *optional*):
624
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
625
+ (see `past_key_values`).
626
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
627
+ """
628
+ if "padding_mask" in kwargs:
629
+ warnings.warn(
630
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. "
631
+ "Please make sure use `attention_mask` instead.`"
632
+ )
633
+
634
+ residual = hidden_states
635
+
636
+ hidden_states = self.attention_norm(hidden_states)
637
+
638
+ # Self Attention
639
+ hidden_states, self_attn_weights, present_key_value = self.attention(
640
+ hidden_states=hidden_states,
641
+ attention_mask=attention_mask,
642
+ position_ids=position_ids,
643
+ past_key_value=past_key_value,
644
+ output_attentions=output_attentions,
645
+ use_cache=use_cache,
646
+ **kwargs,
647
+ )
648
+ hidden_states = residual + hidden_states
649
+
650
+ # Fully Connected
651
+ residual = hidden_states
652
+ hidden_states = self.ffn_norm(hidden_states)
653
+ hidden_states = self.feed_forward(hidden_states)
654
+ hidden_states = residual + hidden_states
655
+
656
+ outputs = (hidden_states,)
657
+
658
+ if output_attentions:
659
+ outputs += (self_attn_weights,)
660
+
661
+ if use_cache:
662
+ outputs += (present_key_value,)
663
+
664
+ return outputs
665
+
666
+
667
+ InternLM2_START_DOCSTRING = r"""
668
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
669
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
670
+ etc.)
671
+
672
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
673
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
674
+ and behavior.
675
+
676
+ Parameters:
677
+ config ([`InternLM2Config`]):
678
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
679
+ load the weights associated with the model, only the configuration. Check out the
680
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
681
+ """
682
+
683
+
684
+ # Copied from transformers.models.llama.modeling_llama.LlamaPreTrainedModel with Llama->InternLM2
685
+ @add_start_docstrings(
686
+ "The bare InternLM2 Model outputting raw hidden-states without any specific head on top.",
687
+ InternLM2_START_DOCSTRING,
688
+ )
689
+ class InternLM2PreTrainedModel(PreTrainedModel):
690
+ config_class = InternLM2Config
691
+ base_model_prefix = "model"
692
+ supports_gradient_checkpointing = True
693
+ _no_split_modules = ["InternLM2DecoderLayer"]
694
+ _skip_keys_device_placement = "past_key_values"
695
+
696
+ def _init_weights(self, module):
697
+ std = self.config.initializer_range
698
+ if isinstance(module, nn.Linear):
699
+ module.weight.data.normal_(mean=0.0, std=std)
700
+ if module.bias is not None:
701
+ module.bias.data.zero_()
702
+ elif isinstance(module, nn.Embedding):
703
+ module.weight.data.normal_(mean=0.0, std=std)
704
+ if module.padding_idx is not None:
705
+ module.weight.data[module.padding_idx].zero_()
706
+
707
+
708
+ InternLM2_INPUTS_DOCSTRING = r"""
709
+ Args:
710
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
711
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
712
+ it.
713
+
714
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
715
+ [`PreTrainedTokenizer.__call__`] for details.
716
+
717
+ [What are input IDs?](../glossary#input-ids)
718
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
719
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
720
+
721
+ - 1 for tokens that are **not masked**,
722
+ - 0 for tokens that are **masked**.
723
+
724
+ [What are attention masks?](../glossary#attention-mask)
725
+
726
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
727
+ [`PreTrainedTokenizer.__call__`] for details.
728
+
729
+ If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
730
+ `past_key_values`).
731
+
732
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
733
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
734
+ information on the default strategy.
735
+
736
+ - 1 indicates the head is **not masked**,
737
+ - 0 indicates the head is **masked**.
738
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
739
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
740
+ config.n_positions - 1]`.
741
+
742
+ [What are position IDs?](../glossary#position-ids)
743
+ past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or
744
+ when `config.use_cache=True`):
745
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
746
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
747
+ `(batch_size, num_heads, decoder_sequence_length, embed_size_per_head)`.
748
+
749
+ Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
750
+ blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
751
+
752
+ If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
753
+ have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
754
+ of shape `(batch_size, sequence_length)`.
755
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
756
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
757
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
758
+ model's internal embedding lookup matrix.
759
+ use_cache (`bool`, *optional*):
760
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
761
+ `past_key_values`).
762
+ output_attentions (`bool`, *optional*):
763
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
764
+ tensors for more detail.
765
+ output_hidden_states (`bool`, *optional*):
766
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
767
+ more detail.
768
+ return_dict (`bool`, *optional*):
769
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
770
+ """
771
+
772
+
773
+ # Modified from transformers.model.llama.modeling_llama.LlamaModel
774
+ @add_start_docstrings(
775
+ "The bare InternLM2 Model outputting raw hidden-states without any specific head on top.",
776
+ InternLM2_START_DOCSTRING,
777
+ )
778
+ class InternLM2Model(InternLM2PreTrainedModel):
779
+ """
780
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`InternLM2DecoderLayer`]
781
+
782
+ Args:
783
+ config: InternLM2Config
784
+ """
785
+
786
+ _auto_class = "AutoModel"
787
+
788
+ def __init__(self, config: InternLM2Config):
789
+ super().__init__(config)
790
+ self.padding_idx = config.pad_token_id
791
+ self.vocab_size = config.vocab_size
792
+ self.config = config
793
+
794
+ self.tok_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
795
+
796
+ self.layers = nn.ModuleList([InternLM2DecoderLayer(config) for _ in range(config.num_hidden_layers)])
797
+ self.norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
798
+
799
+ self.gradient_checkpointing = False
800
+ # Initialize weights and apply final processing
801
+ self.post_init()
802
+
803
+ def get_input_embeddings(self):
804
+ return self.tok_embeddings
805
+
806
+ def set_input_embeddings(self, value):
807
+ self.tok_embeddings = value
808
+
809
+ def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
810
+ # create causal mask
811
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
812
+ combined_attention_mask = None
813
+ if input_shape[-1] > 1:
814
+ combined_attention_mask = _make_causal_mask(
815
+ input_shape,
816
+ inputs_embeds.dtype,
817
+ device=inputs_embeds.device,
818
+ past_key_values_length=past_key_values_length,
819
+ )
820
+
821
+ if attention_mask is not None:
822
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
823
+ expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
824
+ inputs_embeds.device
825
+ )
826
+ combined_attention_mask = (
827
+ expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
828
+ )
829
+
830
+ return combined_attention_mask
831
+
832
+ @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
833
+ def forward(
834
+ self,
835
+ input_ids: torch.LongTensor = None,
836
+ attention_mask: Optional[torch.Tensor] = None,
837
+ position_ids: Optional[torch.LongTensor] = None,
838
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
839
+ inputs_embeds: Optional[torch.FloatTensor] = None,
840
+ use_cache: Optional[bool] = None,
841
+ output_attentions: Optional[bool] = None,
842
+ output_hidden_states: Optional[bool] = None,
843
+ return_dict: Optional[bool] = None,
844
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
845
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
846
+ output_hidden_states = (
847
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
848
+ )
849
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
850
+
851
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
852
+
853
+ if self.config.attn_implementation == "flash_attention_2":
854
+ _import_flash_attn()
855
+
856
+ # retrieve input_ids and inputs_embeds
857
+ if input_ids is not None and inputs_embeds is not None:
858
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
859
+ elif input_ids is not None:
860
+ batch_size, seq_length = input_ids.shape[:2]
861
+ elif inputs_embeds is not None:
862
+ batch_size, seq_length = inputs_embeds.shape[:2]
863
+ else:
864
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
865
+
866
+ seq_length_with_past = seq_length
867
+ past_key_values_length = 0
868
+ if past_key_values is not None:
869
+ past_key_values_length = past_key_values[0][0].shape[2]
870
+ seq_length_with_past = seq_length_with_past + past_key_values_length
871
+
872
+ if position_ids is None:
873
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
874
+ position_ids = torch.arange(
875
+ past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
876
+ )
877
+ position_ids = position_ids.unsqueeze(0)
878
+
879
+ if inputs_embeds is None:
880
+ inputs_embeds = self.tok_embeddings(input_ids)
881
+
882
+ if self.config.attn_implementation == "flash_attention_2":
883
+ # 2d mask is passed through the layers
884
+ attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
885
+ else:
886
+ if attention_mask is None:
887
+ attention_mask = torch.ones(
888
+ (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
889
+ )
890
+ attention_mask = self._prepare_decoder_attention_mask(
891
+ attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
892
+ )
893
+
894
+ # embed positions
895
+ hidden_states = inputs_embeds
896
+
897
+ if self.gradient_checkpointing and self.training:
898
+ if use_cache:
899
+ logger.warning_once(
900
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
901
+ )
902
+ use_cache = False
903
+
904
+ # decoder layers
905
+ all_hidden_states = () if output_hidden_states else None
906
+ all_self_attns = () if output_attentions else None
907
+ next_decoder_cache = () if use_cache else None
908
+
909
+ for idx, decoder_layer in enumerate(self.layers):
910
+ if output_hidden_states:
911
+ all_hidden_states += (hidden_states,)
912
+
913
+ past_key_value = past_key_values[idx] if past_key_values is not None else None
914
+
915
+ if self.gradient_checkpointing and self.training:
916
+
917
+ def create_custom_forward(module):
918
+ def custom_forward(*inputs):
919
+ # None for past_key_value
920
+ return module(*inputs, output_attentions, None)
921
+
922
+ return custom_forward
923
+
924
+ layer_outputs = torch.utils.checkpoint.checkpoint(
925
+ create_custom_forward(decoder_layer),
926
+ hidden_states,
927
+ attention_mask,
928
+ position_ids,
929
+ None,
930
+ )
931
+ else:
932
+ layer_outputs = decoder_layer(
933
+ hidden_states,
934
+ attention_mask=attention_mask,
935
+ position_ids=position_ids,
936
+ past_key_value=past_key_value,
937
+ output_attentions=output_attentions,
938
+ use_cache=use_cache,
939
+ )
940
+
941
+ hidden_states = layer_outputs[0]
942
+
943
+ if use_cache:
944
+ next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
945
+
946
+ if output_attentions:
947
+ all_self_attns += (layer_outputs[1],)
948
+
949
+ hidden_states = self.norm(hidden_states)
950
+
951
+ # add hidden states from the last decoder layer
952
+ if output_hidden_states:
953
+ all_hidden_states += (hidden_states,)
954
+
955
+ next_cache = next_decoder_cache if use_cache else None
956
+ if not return_dict:
957
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
958
+ return BaseModelOutputWithPast(
959
+ last_hidden_state=hidden_states,
960
+ past_key_values=next_cache,
961
+ hidden_states=all_hidden_states,
962
+ attentions=all_self_attns,
963
+ )
964
+
965
+
966
+ # Modified from transformers.model.llama.modeling_llama.LlamaForCausalLM
967
+ class InternLM2ForCausalLM(InternLM2PreTrainedModel):
968
+ _auto_class = "AutoModelForCausalLM"
969
+
970
+ _tied_weights_keys = ["output.weight"]
971
+
972
+ def __init__(self, config):
973
+ super().__init__(config)
974
+ self.model = InternLM2Model(config)
975
+ self.vocab_size = config.vocab_size
976
+ self.output = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
977
+
978
+ # Initialize weights and apply final processing
979
+ self.post_init()
980
+
981
+ def get_input_embeddings(self):
982
+ return self.model.tok_embeddings
983
+
984
+ def set_input_embeddings(self, value):
985
+ self.model.tok_embeddings = value
986
+
987
+ def get_output_embeddings(self):
988
+ return self.output
989
+
990
+ def set_output_embeddings(self, new_embeddings):
991
+ self.output = new_embeddings
992
+
993
+ def set_decoder(self, decoder):
994
+ self.model = decoder
995
+
996
+ def get_decoder(self):
997
+ return self.model
998
+
999
+ @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
1000
+ @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
1001
+ def forward(
1002
+ self,
1003
+ input_ids: torch.LongTensor = None,
1004
+ attention_mask: Optional[torch.Tensor] = None,
1005
+ position_ids: Optional[torch.LongTensor] = None,
1006
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
1007
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1008
+ labels: Optional[torch.LongTensor] = None,
1009
+ use_cache: Optional[bool] = None,
1010
+ output_attentions: Optional[bool] = None,
1011
+ output_hidden_states: Optional[bool] = None,
1012
+ return_dict: Optional[bool] = None,
1013
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
1014
+ r"""
1015
+ Args:
1016
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
1017
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
1018
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
1019
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
1020
+
1021
+ Returns:
1022
+
1023
+ Example:
1024
+
1025
+ ```python
1026
+ >>> from transformers import AutoTokenizer, InternLM2ForCausalLM
1027
+
1028
+ >>> model = InternLM2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
1029
+ >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
1030
+
1031
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
1032
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
1033
+
1034
+ >>> # Generate
1035
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
1036
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
1037
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
1038
+ ```"""
1039
+
1040
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
1041
+ output_hidden_states = (
1042
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
1043
+ )
1044
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1045
+
1046
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
1047
+ outputs = self.model(
1048
+ input_ids=input_ids,
1049
+ attention_mask=attention_mask,
1050
+ position_ids=position_ids,
1051
+ past_key_values=past_key_values,
1052
+ inputs_embeds=inputs_embeds,
1053
+ use_cache=use_cache,
1054
+ output_attentions=output_attentions,
1055
+ output_hidden_states=output_hidden_states,
1056
+ return_dict=return_dict,
1057
+ )
1058
+
1059
+ hidden_states = outputs[0]
1060
+ logits = self.output(hidden_states)
1061
+ logits = logits.float()
1062
+
1063
+ loss = None
1064
+ if labels is not None:
1065
+ # Shift so that tokens < n predict n
1066
+ shift_logits = logits[..., :-1, :].contiguous()
1067
+ shift_labels = labels[..., 1:].contiguous()
1068
+ # Flatten the tokens
1069
+ loss_fct = CrossEntropyLoss()
1070
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
1071
+ shift_labels = shift_labels.view(-1)
1072
+ # Enable model parallelism
1073
+ shift_labels = shift_labels.to(shift_logits.device)
1074
+ loss = loss_fct(shift_logits, shift_labels)
1075
+
1076
+ if not return_dict:
1077
+ output = (logits,) + outputs[1:]
1078
+ return (loss,) + output if loss is not None else output
1079
+
1080
+ return CausalLMOutputWithPast(
1081
+ loss=loss,
1082
+ logits=logits,
1083
+ past_key_values=outputs.past_key_values,
1084
+ hidden_states=outputs.hidden_states,
1085
+ attentions=outputs.attentions,
1086
+ )
1087
+
1088
+ def prepare_inputs_for_generation(
1089
+ self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
1090
+ ):
1091
+ if past_key_values is not None:
1092
+ past_length = past_key_values[0][0].shape[2]
1093
+
1094
+ # Some generation methods already pass only the last input ID
1095
+ if input_ids.shape[1] > past_length:
1096
+ remove_prefix_length = past_length
1097
+ else:
1098
+ # Default to old behavior: keep only final ID
1099
+ remove_prefix_length = input_ids.shape[1] - 1
1100
+
1101
+ input_ids = input_ids[:, remove_prefix_length:]
1102
+
1103
+ position_ids = kwargs.get("position_ids", None)
1104
+ if attention_mask is not None and position_ids is None:
1105
+ # create position_ids on the fly for batch generation
1106
+ position_ids = attention_mask.long().cumsum(-1) - 1
1107
+ position_ids.masked_fill_(attention_mask == 0, 1)
1108
+ if past_key_values:
1109
+ position_ids = position_ids[:, -input_ids.shape[1] :]
1110
+
1111
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
1112
+ if inputs_embeds is not None and past_key_values is None:
1113
+ model_inputs = {"inputs_embeds": inputs_embeds}
1114
+ else:
1115
+ model_inputs = {"input_ids": input_ids}
1116
+
1117
+ model_inputs.update(
1118
+ {
1119
+ "position_ids": position_ids,
1120
+ "past_key_values": past_key_values,
1121
+ "use_cache": kwargs.get("use_cache"),
1122
+ "attention_mask": attention_mask,
1123
+ }
1124
+ )
1125
+ return model_inputs
1126
+
1127
+ @staticmethod
1128
+ def _reorder_cache(past_key_values, beam_idx):
1129
+ reordered_past = ()
1130
+ for layer_past in past_key_values:
1131
+ reordered_past += (
1132
+ tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
1133
+ )
1134
+ return reordered_past
1135
+
1136
+ def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = [], meta_instruction=""):
1137
+ if tokenizer.add_bos_token:
1138
+ prompt = ""
1139
+ else:
1140
+ prompt = tokenizer.bos_token
1141
+ if meta_instruction:
1142
+ prompt += f"""<|im_start|>system\n{meta_instruction}<|im_end|>\n"""
1143
+ for record in history:
1144
+ prompt += f"""<|im_start|>user\n{record[0]}<|im_end|>\n<|im_start|>assistant\n{record[1]}<|im_end|>\n"""
1145
+ prompt += f"""<|im_start|>user\n{query}<|im_end|>\n<|im_start|>assistant\n"""
1146
+ return tokenizer([prompt], return_tensors="pt")
1147
+
1148
+ @torch.no_grad()
1149
+ def chat(
1150
+ self,
1151
+ tokenizer,
1152
+ query: str,
1153
+ history: List[Tuple[str, str]] = [],
1154
+ streamer: Optional[BaseStreamer] = None,
1155
+ max_new_tokens: int = 1024,
1156
+ do_sample: bool = True,
1157
+ temperature: float = 0.8,
1158
+ top_p: float = 0.8,
1159
+ meta_instruction: str = "You are an AI assistant whose name is InternLM (书生·浦语).\n"
1160
+ "- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless.\n"
1161
+ "- InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such as English and 中文.",
1162
+ **kwargs,
1163
+ ):
1164
+ inputs = self.build_inputs(tokenizer, query, history, meta_instruction)
1165
+ inputs = {k: v.to(self.device) for k, v in inputs.items() if torch.is_tensor(v)}
1166
+ # also add end-of-assistant token in eos token id to avoid unnecessary generation
1167
+ eos_token_id = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids(["<|im_end|>"])[0]]
1168
+ outputs = self.generate(
1169
+ **inputs,
1170
+ streamer=streamer,
1171
+ max_new_tokens=max_new_tokens,
1172
+ do_sample=do_sample,
1173
+ temperature=temperature,
1174
+ top_p=top_p,
1175
+ eos_token_id=eos_token_id,
1176
+ **kwargs,
1177
+ )
1178
+ outputs = outputs[0].cpu().tolist()[len(inputs["input_ids"][0]) :]
1179
+ response = tokenizer.decode(outputs, skip_special_tokens=True)
1180
+ response = response.split("<|im_end|>")[0]
1181
+ history = history + [(query, response)]
1182
+ return response, history
1183
+
1184
+ @torch.no_grad()
1185
+ def stream_chat(
1186
+ self,
1187
+ tokenizer,
1188
+ query: str,
1189
+ history: List[Tuple[str, str]] = [],
1190
+ max_new_tokens: int = 1024,
1191
+ do_sample: bool = True,
1192
+ temperature: float = 0.8,
1193
+ top_p: float = 0.8,
1194
+ **kwargs,
1195
+ ):
1196
+ """
1197
+ Return a generator in format: (response, history)
1198
+ Eg.
1199
+ ('你好，有什么可以帮助您的吗', [('你好', '你好，有什么可以帮助您的吗')])
1200
+ ('你好，有什么可以帮助您的吗？', [('你好', '你好，有什么可以帮助您的吗？')])
1201
+ """
1202
+ if BaseStreamer is None:
1203
+ raise ModuleNotFoundError(
1204
+ "The version of `transformers` is too low. Please make sure "
1205
+ "that you have installed `transformers>=4.28.0`."
1206
+ )
1207
+
1208
+ response_queue = queue.Queue(maxsize=20)
1209
+
1210
+ class ChatStreamer(BaseStreamer):
1211
+ def __init__(self, tokenizer) -> None:
1212
+ super().__init__()
1213
+ self.tokenizer = tokenizer
1214
+ self.queue = response_queue
1215
+ self.query = query
1216
+ self.history = history
1217
+ self.response = ""
1218
+ self.cache = []
1219
+ self.received_inputs = False
1220
+ self.queue.put((self.response, history + [(self.query, self.response)]))
1221
+
1222
+ def put(self, value):
1223
+ if len(value.shape) > 1 and value.shape[0] > 1:
1224
+ raise ValueError("ChatStreamer only supports batch size 1")
1225
+ elif len(value.shape) > 1:
1226
+ value = value[0]
1227
+
1228
+ if not self.received_inputs:
1229
+ # The first received value is input_ids, ignore here
1230
+ self.received_inputs = True
1231
+ return
1232
+
1233
+ self.cache.extend(value.tolist())
1234
+ token = self.tokenizer.decode(self.cache, skip_special_tokens=True)
1235
+ if token.strip() != "<|im_end|>":
1236
+ self.response = self.response + token
1237
+ history = self.history + [(self.query, self.response)]
1238
+ self.queue.put((self.response, history))
1239
+ self.cache = []
1240
+ else:
1241
+ self.end()
1242
+
1243
+ def end(self):
1244
+ self.queue.put(None)
1245
+
1246
+ def stream_producer():
1247
+ return self.chat(
1248
+ tokenizer=tokenizer,
1249
+ query=query,
1250
+ streamer=ChatStreamer(tokenizer=tokenizer),
1251
+ history=history,
1252
+ max_new_tokens=max_new_tokens,
1253
+ do_sample=do_sample,
1254
+ temperature=temperature,
1255
+ top_p=top_p,
1256
+ **kwargs,
1257
+ )
1258
+
1259
+ def consumer():
1260
+ producer = threading.Thread(target=stream_producer)
1261
+ producer.start()
1262
+ while True:
1263
+ res = response_queue.get()
1264
+ if res is None:
1265
+ return
1266
+ yield res
1267
+
1268
+ return consumer()
1269
+
1270
+
1271
+ # Copied from transformers.model.llama.modeling_llama.LlamaForSequenceClassification with Llama->InternLM2
1272
+ @add_start_docstrings(
1273
+ """
1274
+ The InternLM2 Model transformer with a sequence classification head on top (linear layer).
1275
+
1276
+ [`InternLM2ForSequenceClassification`] uses the last token in order to do the classification,
1277
+ as other causal models (e.g. GPT-2) do.
1278
+
1279
+ Since it does classification on the last token, it requires to know the position of the last token. If a
1280
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
1281
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
1282
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
1283
+ each row of the batch).
1284
+ """,
1285
+ InternLM2_START_DOCSTRING,
1286
+ )
1287
+ class InternLM2ForSequenceClassification(InternLM2PreTrainedModel):
1288
+ def __init__(self, config):
1289
+ super().__init__(config)
1290
+ self.num_labels = config.num_labels
1291
+ self.model = InternLM2Model(config)
1292
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
1293
+
1294
+ # Initialize weights and apply final processing
1295
+ self.post_init()
1296
+
1297
+ def get_input_embeddings(self):
1298
+ return self.model.tok_embeddings
1299
+
1300
+ def set_input_embeddings(self, value):
1301
+ self.model.tok_embeddings = value
1302
+
1303
+ @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
1304
+ def forward(
1305
+ self,
1306
+ input_ids: torch.LongTensor = None,
1307
+ attention_mask: Optional[torch.Tensor] = None,
1308
+ position_ids: Optional[torch.LongTensor] = None,
1309
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
1310
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1311
+ labels: Optional[torch.LongTensor] = None,
1312
+ use_cache: Optional[bool] = None,
1313
+ output_attentions: Optional[bool] = None,
1314
+ output_hidden_states: Optional[bool] = None,
1315
+ return_dict: Optional[bool] = None,
1316
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
1317
+ r"""
1318
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1319
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
1320
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
1321
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
1322
+ """
1323
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1324
+
1325
+ transformer_outputs = self.model(
1326
+ input_ids,
1327
+ attention_mask=attention_mask,
1328
+ position_ids=position_ids,
1329
+ past_key_values=past_key_values,
1330
+ inputs_embeds=inputs_embeds,
1331
+ use_cache=use_cache,
1332
+ output_attentions=output_attentions,
1333
+ output_hidden_states=output_hidden_states,
1334
+ return_dict=return_dict,
1335
+ )
1336
+ hidden_states = transformer_outputs[0]
1337
+ logits = self.score(hidden_states)
1338
+
1339
+ if input_ids is not None:
1340
+ batch_size = input_ids.shape[0]
1341
+ else:
1342
+ batch_size = inputs_embeds.shape[0]
1343
+
1344
+ if self.config.pad_token_id is None and batch_size != 1:
1345
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
1346
+ if self.config.pad_token_id is None:
1347
+ sequence_lengths = -1
1348
+ else:
1349
+ if input_ids is not None:
1350
+ sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1).to(
1351
+ logits.device
1352
+ )
1353
+ else:
1354
+ sequence_lengths = -1
1355
+
1356
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
1357
+
1358
+ loss = None
1359
+ if labels is not None:
1360
+ labels = labels.to(logits.device)
1361
+ if self.config.problem_type is None:
1362
+ if self.num_labels == 1:
1363
+ self.config.problem_type = "regression"
1364
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
1365
+ self.config.problem_type = "single_label_classification"
1366
+ else:
1367
+ self.config.problem_type = "multi_label_classification"
1368
+
1369
+ if self.config.problem_type == "regression":
1370
+ loss_fct = MSELoss()
1371
+ if self.num_labels == 1:
1372
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
1373
+ else:
1374
+ loss = loss_fct(pooled_logits, labels)
1375
+ elif self.config.problem_type == "single_label_classification":
1376
+ loss_fct = CrossEntropyLoss()
1377
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
1378
+ elif self.config.problem_type == "multi_label_classification":
1379
+ loss_fct = BCEWithLogitsLoss()
1380
+ loss = loss_fct(pooled_logits, labels)
1381
+ if not return_dict:
1382
+ output = (pooled_logits,) + transformer_outputs[1:]
1383
+ return ((loss,) + output) if loss is not None else output
1384
+
1385
+ return SequenceClassifierOutputWithPast(
1386
+ loss=loss,
1387
+ logits=pooled_logits,
1388
+ past_key_values=transformer_outputs.past_key_values,
1389
+ hidden_states=transformer_outputs.hidden_states,
1390
+ attentions=transformer_outputs.attentions,
1391
+ )
runs/May14_07-37-31_pt-p1l1oepj-master-0/events.out.tfevents.1715644019.pt-p1l1oepj-master-0.53.0 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:9ecb428fcfc1dfdf3b266b63bb5b18c345e3fb68e8fd770c65e690d89ef43053
3
+ size 426166
runs/May14_07-37-31_pt-p1l1oepj-master-0/events.out.tfevents.1715693335.pt-p1l1oepj-master-0.53.1 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:ddaf8704ade429eb1957961cc7ea9941c1f591c9f0e07f622b4c13126660b774
3
+ size 828
special_tokens_map.json ADDED
@@ -0,0 +1,40 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "additional_special_tokens": [
3
+ "<|user|>",
4
+ "<|system|>",
5
+ "<|assistant|>",
6
+ "<|text|>",
7
+ "<|code|>",
8
+ "<|execution|>",
9
+ "<|endofblock|>",
10
+ "<|endofmessage|>"
11
+ ],
12
+ "bos_token": {
13
+ "content": "<s>",
14
+ "lstrip": false,
15
+ "normalized": false,
16
+ "rstrip": false,
17
+ "single_word": false
18
+ },
19
+ "eos_token": {
20
+ "content": "</s>",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false
25
+ },
26
+ "pad_token": {
27
+ "content": "</s>",
28
+ "lstrip": false,
29
+ "normalized": false,
30
+ "rstrip": false,
31
+ "single_word": false
32
+ },
33
+ "unk_token": {
34
+ "content": "<unk>",
35
+ "lstrip": false,
36
+ "normalized": false,
37
+ "rstrip": false,
38
+ "single_word": false
39
+ }
40
+ }
tokenization_internlm2.py ADDED
@@ -0,0 +1,236 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # This code is based on transformers/src/transformers/models/llama/tokenization_llama.py
5
+ #
6
+ # Licensed under the Apache License, Version 2.0 (the "License");
7
+ # you may not use this file except in compliance with the License.
8
+ # You may obtain a copy of the License at
9
+ #
10
+ # http://www.apache.org/licenses/LICENSE-2.0
11
+ #
12
+ # Unless required by applicable law or agreed to in writing, software
13
+ # distributed under the License is distributed on an "AS IS" BASIS,
14
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
15
+ # See the License for the specific language governing permissions and
16
+ # limitations under the License.
17
+
18
+ """Tokenization classes for InternLM."""
19
+ import os
20
+ from shutil import copyfile
21
+ from typing import Any, Dict, List, Optional, Tuple
22
+
23
+ import sentencepiece as spm
24
+ from transformers.tokenization_utils import PreTrainedTokenizer
25
+ from transformers.utils import logging
26
+
27
+ logger = logging.get_logger(__name__)
28
+
29
+ VOCAB_FILES_NAMES = {"vocab_file": "./tokenizer.model"}
30
+
31
+ PRETRAINED_VOCAB_FILES_MAP = {}
32
+
33
+
34
+ # Modified from transformers.model.llama.tokenization_llama.LlamaTokenizer
35
+ class InternLM2Tokenizer(PreTrainedTokenizer):
36
+ """
37
+ Construct a InternLM2 tokenizer. Based on byte-level Byte-Pair-Encoding.
38
+
39
+ Args:
40
+ vocab_file (`str`):
41
+ Path to the vocabulary file.
42
+ """
43
+
44
+ vocab_files_names = VOCAB_FILES_NAMES
45
+ pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
46
+ model_input_names = ["input_ids", "attention_mask"]
47
+ _auto_class = "AutoTokenizer"
48
+
49
+ def __init__(
50
+ self,
51
+ vocab_file,
52
+ unk_token="<unk>",
53
+ bos_token="<s>",
54
+ eos_token="</s>",
55
+ pad_token="</s>",
56
+ sp_model_kwargs: Optional[Dict[str, Any]] = None,
57
+ add_bos_token=True,
58
+ add_eos_token=False,
59
+ decode_with_prefix_space=False,
60
+ clean_up_tokenization_spaces=False,
61
+ **kwargs,
62
+ ):
63
+ self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
64
+ self.vocab_file = vocab_file
65
+ self.add_bos_token = add_bos_token
66
+ self.add_eos_token = add_eos_token
67
+ self.decode_with_prefix_space = decode_with_prefix_space
68
+ self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
69
+ self.sp_model.Load(vocab_file)
70
+ self._no_prefix_space_tokens = None
71
+ super().__init__(
72
+ bos_token=bos_token,
73
+ eos_token=eos_token,
74
+ unk_token=unk_token,
75
+ pad_token=pad_token,
76
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
77
+ **kwargs,
78
+ )
79
+
80
+ @property
81
+ def no_prefix_space_tokens(self):
82
+ if self._no_prefix_space_tokens is None:
83
+ vocab = self.convert_ids_to_tokens(list(range(self.vocab_size)))
84
+ self._no_prefix_space_tokens = {i for i, tok in enumerate(vocab) if not tok.startswith("▁")}
85
+ return self._no_prefix_space_tokens
86
+
87
+ @property
88
+ def vocab_size(self):
89
+ """Returns vocab size"""
90
+ return self.sp_model.get_piece_size()
91
+
92
+ @property
93
+ def bos_token_id(self) -> Optional[int]:
94
+ return self.sp_model.bos_id()
95
+
96
+ @property
97
+ def eos_token_id(self) -> Optional[int]:
98
+ return self.sp_model.eos_id()
99
+
100
+ def get_vocab(self):
101
+ """Returns vocab as a dict"""
102
+ vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
103
+ vocab.update(self.added_tokens_encoder)
104
+ return vocab
105
+
106
+ def _tokenize(self, text):
107
+ """Returns a tokenized string."""
108
+ return self.sp_model.encode(text, out_type=str)
109
+
110
+ def _convert_token_to_id(self, token):
111
+ """Converts a token (str) in an id using the vocab."""
112
+ return self.sp_model.piece_to_id(token)
113
+
114
+ def _convert_id_to_token(self, index):
115
+ """Converts an index (integer) in a token (str) using the vocab."""
116
+ token = self.sp_model.IdToPiece(index)
117
+ return token
118
+
119
+ def _maybe_add_prefix_space(self, tokens, decoded):
120
+ if tokens and tokens[0] not in self.no_prefix_space_tokens:
121
+ return " " + decoded
122
+ else:
123
+ return decoded
124
+
125
+ def convert_tokens_to_string(self, tokens):
126
+ """Converts a sequence of tokens (string) in a single string."""
127
+ current_sub_tokens = []
128
+ out_string = ""
129
+ prev_is_special = False
130
+ for token in tokens:
131
+ # make sure that special tokens are not decoded using sentencepiece model
132
+ if token in self.all_special_tokens:
133
+ if not prev_is_special:
134
+ out_string += " "
135
+ out_string += self.sp_model.decode(current_sub_tokens) + token
136
+ prev_is_special = True
137
+ current_sub_tokens = []
138
+ else:
139
+ current_sub_tokens.append(token)
140
+ prev_is_special = False
141
+ out_string += self.sp_model.decode(current_sub_tokens)
142
+ out_string = self.clean_up_tokenization(out_string)
143
+ out_string = self._maybe_add_prefix_space(tokens=tokens, decoded=out_string)
144
+ return out_string[1:]
145
+
146
+ def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
147
+ """
148
+ Save the vocabulary and special tokens file to a directory.
149
+
150
+ Args:
151
+ save_directory (`str`):
152
+ The directory in which to save the vocabulary.
153
+
154
+ Returns:
155
+ `Tuple(str)`: Paths to the files saved.
156
+ """
157
+ if not os.path.isdir(save_directory):
158
+ logger.error(f"Vocabulary path ({save_directory}) should be a directory")
159
+ return
160
+ out_vocab_file = os.path.join(
161
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
162
+ )
163
+
164
+ if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
165
+ copyfile(self.vocab_file, out_vocab_file)
166
+ elif not os.path.isfile(self.vocab_file):
167
+ with open(out_vocab_file, "wb") as fi:
168
+ content_spiece_model = self.sp_model.serialized_model_proto()
169
+ fi.write(content_spiece_model)
170
+
171
+ return (out_vocab_file,)
172
+
173
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
174
+ if self.add_bos_token:
175
+ bos_token_ids = [self.bos_token_id]
176
+ else:
177
+ bos_token_ids = []
178
+
179
+ output = bos_token_ids + token_ids_0
180
+
181
+ if token_ids_1 is not None:
182
+ output = output + token_ids_1
183
+
184
+ if self.add_eos_token:
185
+ output = output + [self.eos_token_id]
186
+
187
+ return output
188
+
189
+ def get_special_tokens_mask(
190
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
191
+ ) -> List[int]:
192
+ """
193
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
194
+ special tokens using the tokenizer `prepare_for_model` method.
195
+
196
+ Args:
197
+ token_ids_0 (`List[int]`):
198
+ List of IDs.
199
+ token_ids_1 (`List[int]`, *optional*):
200
+ Optional second list of IDs for sequence pairs.
201
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
202
+ Whether or not the token list is already formatted with special tokens for the model.
203
+
204
+ Returns:
205
+ `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
206
+ """
207
+ if already_has_special_tokens:
208
+ return super().get_special_tokens_mask(
209
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
210
+ )
211
+
212
+ if token_ids_1 is None:
213
+ return [1] + ([0] * len(token_ids_0)) + [1]
214
+ return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
215
+
216
+ def create_token_type_ids_from_sequences(
217
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
218
+ ) -> List[int]:
219
+ """
220
+ Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
221
+ use of token type ids, therefore a list of zeros is returned.
222
+
223
+ Args:
224
+ token_ids_0 (`List[int]`):
225
+ List of IDs.
226
+ token_ids_1 (`List[int]`, *optional*):
227
+ Optional second list of IDs for sequence pairs.
228
+
229
+ Returns:
230
+ `List[int]`: List of zeros.
231
+ """
232
+ eos = [self.eos_token_id]
233
+
234
+ if token_ids_1 is None:
235
+ return len(token_ids_0 + eos) * [0]
236
+ return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
tokenization_internlm2_fast.py ADDED
@@ -0,0 +1,214 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # This code is based on transformers/src/transformers/models/llama/tokenization_llama_fast.py
5
+ #
6
+ # Licensed under the Apache License, Version 2.0 (the "License");
7
+ # you may not use this file except in compliance with the License.
8
+ # You may obtain a copy of the License at
9
+ #
10
+ # http://www.apache.org/licenses/LICENSE-2.0
11
+ #
12
+ # Unless required by applicable law or agreed to in writing, software
13
+ # distributed under the License is distributed on an "AS IS" BASIS,
14
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
15
+ # See the License for the specific language governing permissions and
16
+ # limitations under the License.
17
+
18
+ """Tokenization Fast class for InternLM."""
19
+ import os
20
+ from shutil import copyfile
21
+ from typing import Any, Dict, Optional, Tuple
22
+
23
+ from tokenizers import processors, decoders, Tokenizer, normalizers
24
+ from tokenizers.models import BPE
25
+
26
+ from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
27
+ from transformers.utils import logging
28
+
29
+ from transformers.convert_slow_tokenizer import (
30
+ SLOW_TO_FAST_CONVERTERS,
31
+ SpmConverter,
32
+ SentencePieceExtractor,
33
+ )
34
+
35
+ from .tokenization_internlm2 import InternLM2Tokenizer
36
+
37
+ logger = logging.get_logger(__name__)
38
+
39
+ VOCAB_FILES_NAMES = {"vocab_file": "./tokenizer.model"}
40
+
41
+ # Modified from transformers.convert_slow_tokenizer.LlamaConverter
42
+ class InternLM2Converter(SpmConverter):
43
+ handle_byte_fallback = True
44
+
45
+ def vocab(self, proto):
46
+ vocab = [
47
+ ("<unk>", 0.0),
48
+ ("<s>", 0.0),
49
+ ("</s>", 0.0),
50
+ ]
51
+ vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]]
52
+ return vocab
53
+
54
+ def unk_id(self, proto):
55
+ unk_id = 0
56
+ return unk_id
57
+
58
+ def decoder(self, replacement, add_prefix_space):
59
+ return decoders.Sequence(
60
+ [
61
+ decoders.Replace("▁", " "),
62
+ decoders.ByteFallback(),
63
+ decoders.Fuse(),
64
+ decoders.Strip(content=" ", left=1),
65
+ ]
66
+ )
67
+
68
+ def tokenizer(self, proto):
69
+ model_type = proto.trainer_spec.model_type
70
+ vocab_scores = self.vocab(proto)
71
+ # special tokens
72
+ added_tokens = self.original_tokenizer.added_tokens_decoder
73
+ for i in range(len(vocab_scores)):
74
+ piece, score = vocab_scores[i]
75
+ if i in added_tokens:
76
+ vocab_scores[i] = (added_tokens[i].content, score)
77
+ if model_type == 1:
78
+ raise RuntimeError("InternLM2 is supposed to be a BPE model!")
79
+
80
+ elif model_type == 2:
81
+ _, merges = SentencePieceExtractor(self.original_tokenizer.vocab_file).extract(vocab_scores)
82
+ bpe_vocab = {word: i for i, (word, _score) in enumerate(vocab_scores)}
83
+ tokenizer = Tokenizer(
84
+ BPE(bpe_vocab, merges, unk_token=proto.trainer_spec.unk_piece, fuse_unk=True, byte_fallback=True)
85
+ )
86
+ tokenizer.add_special_tokens(
87
+ [ added_token for index, added_token in added_tokens.items()]
88
+ )
89
+ else:
90
+ raise Exception(
91
+ "You're trying to run a `Unigram` model but you're file was trained with a different algorithm"
92
+ )
93
+
94
+ return tokenizer
95
+
96
+ def normalizer(self, proto):
97
+ normalizers_list = []
98
+ if proto.normalizer_spec.add_dummy_prefix:
99
+ normalizers_list.append(normalizers.Prepend(prepend="▁"))
100
+ normalizers_list.append(normalizers.Replace(pattern=" ", content="▁"))
101
+ return normalizers.Sequence(normalizers_list)
102
+
103
+ def pre_tokenizer(self, replacement, add_prefix_space):
104
+ return None
105
+
106
+ SLOW_TO_FAST_CONVERTERS["InternLM2Tokenizer"] = InternLM2Converter
107
+
108
+
109
+ # Modified from transformers.model.llama.tokenization_llama_fast.LlamaTokenizerFast -> InternLM2TokenizerFast
110
+ class InternLM2TokenizerFast(PreTrainedTokenizerFast):
111
+ vocab_files_names = VOCAB_FILES_NAMES
112
+ slow_tokenizer_class = InternLM2Tokenizer
113
+ padding_side = "left"
114
+ model_input_names = ["input_ids", "attention_mask"]
115
+ _auto_class = "AutoTokenizer"
116
+
117
+ def __init__(
118
+ self,
119
+ vocab_file,
120
+ unk_token="<unk>",
121
+ bos_token="<s>",
122
+ eos_token="</s>",
123
+ pad_token="</s>",
124
+ sp_model_kwargs: Optional[Dict[str, Any]] = None,
125
+ add_bos_token=True,
126
+ add_eos_token=False,
127
+ decode_with_prefix_space=False,
128
+ clean_up_tokenization_spaces=False,
129
+ **kwargs,
130
+ ):
131
+ super().__init__(
132
+ vocab_file=vocab_file,
133
+ unk_token=unk_token,
134
+ bos_token=bos_token,
135
+ eos_token=eos_token,
136
+ pad_token=pad_token,
137
+ sp_model_kwargs=sp_model_kwargs,
138
+ add_bos_token=add_bos_token,
139
+ add_eos_token=add_eos_token,
140
+ decode_with_prefix_space=decode_with_prefix_space,
141
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
142
+ **kwargs,
143
+ )
144
+ self._add_bos_token = add_bos_token
145
+ self._add_eos_token = add_eos_token
146
+ self.update_post_processor()
147
+ self.vocab_file = vocab_file
148
+
149
+ @property
150
+ def can_save_slow_tokenizer(self) -> bool:
151
+ return os.path.isfile(self.vocab_file) if self.vocab_file else False
152
+
153
+ def update_post_processor(self):
154
+ """
155
+ Updates the underlying post processor with the current `bos_token` and `eos_token`.
156
+ """
157
+ bos = self.bos_token
158
+ bos_token_id = self.bos_token_id
159
+ if bos is None and self.add_bos_token:
160
+ raise ValueError("add_bos_token = True but bos_token = None")
161
+
162
+ eos = self.eos_token
163
+ eos_token_id = self.eos_token_id
164
+ if eos is None and self.add_eos_token:
165
+ raise ValueError("add_eos_token = True but eos_token = None")
166
+
167
+ single = f"{(bos+':0 ') if self.add_bos_token else ''}$A:0{(' '+eos+':0') if self.add_eos_token else ''}"
168
+ pair = f"{single}{(' '+bos+':1') if self.add_bos_token else ''} $B:1{(' '+eos+':1') if self.add_eos_token else ''}"
169
+
170
+ special_tokens = []
171
+ if self.add_bos_token:
172
+ special_tokens.append((bos, bos_token_id))
173
+ if self.add_eos_token:
174
+ special_tokens.append((eos, eos_token_id))
175
+ self._tokenizer.post_processor = processors.TemplateProcessing(
176
+ single=single, pair=pair, special_tokens=special_tokens
177
+ )
178
+
179
+ @property
180
+ def add_eos_token(self):
181
+ return self._add_eos_token
182
+
183
+ @property
184
+ def add_bos_token(self):
185
+ return self._add_bos_token
186
+
187
+ @add_eos_token.setter
188
+ def add_eos_token(self, value):
189
+ self._add_eos_token = value
190
+ self.update_post_processor()
191
+
192
+ @add_bos_token.setter
193
+ def add_bos_token(self, value):
194
+ self._add_bos_token = value
195
+ self.update_post_processor()
196
+
197
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
198
+ if not self.can_save_slow_tokenizer:
199
+ raise ValueError(
200
+ "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
201
+ "tokenizer."
202
+ )
203
+
204
+ if not os.path.isdir(save_directory):
205
+ logger.error(f"Vocabulary path ({save_directory}) should be a directory")
206
+ return
207
+ out_vocab_file = os.path.join(
208
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
209
+ )
210
+
211
+ if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
212
+ copyfile(self.vocab_file, out_vocab_file)
213
+
214
+ return (out_vocab_file,)
tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
tokenizer.model ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:f868398fc4e05ee1e8aeba95ddf18ddcc45b8bce55d5093bead5bbf80429b48b
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+ size 1477754
tokenizer_config.json ADDED
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1
+ {
2
+ "add_bos_token": true,
3
+ "add_eos_token": false,
4
+ "added_tokens_decoder": {
5
+ "0": {
6
+ "content": "<unk>",
7
+ "lstrip": false,
8
+ "normalized": false,
9
+ "rstrip": false,
10
+ "single_word": false,
11
+ "special": true
12
+ },
13
+ "1": {
14
+ "content": "<s>",
15
+ "lstrip": false,
16
+ "normalized": false,
17
+ "rstrip": false,
18
+ "single_word": false,
19
+ "special": true
20
+ },
21
+ "2": {
22
+ "content": "</s>",
23
+ "lstrip": false,
24
+ "normalized": false,
25
+ "rstrip": false,
26
+ "single_word": false,
27
+ "special": true
28
+ },
29
+ "92544": {
30
+ "content": "<|user|>",
31
+ "lstrip": false,
32
+ "normalized": false,
33
+ "rstrip": false,
34
+ "single_word": false,
35
+ "special": true
36
+ },
37
+ "92545": {
38
+ "content": "<|system|>",
39
+ "lstrip": false,
40
+ "normalized": false,
41
+ "rstrip": false,
42
+ "single_word": false,
43
+ "special": true
44
+ },
45
+ "92546": {
46
+ "content": "<|assistant|>",
47
+ "lstrip": false,
48
+ "normalized": false,
49
+ "rstrip": false,
50
+ "single_word": false,
51
+ "special": true
52
+ },
53
+ "92547": {
54
+ "content": "<|text|>",
55
+ "lstrip": false,
56
+ "normalized": false,
57
+ "rstrip": false,
58
+ "single_word": false,
59
+ "special": true
60
+ },
61
+ "92548": {
62
+ "content": "<|code|>",
63
+ "lstrip": false,
64
+ "normalized": false,
65
+ "rstrip": false,
66
+ "single_word": false,
67
+ "special": true
68
+ },
69
+ "92549": {
70
+ "content": "<|execution|>",
71
+ "lstrip": false,
72
+ "normalized": false,
73
+ "rstrip": false,
74
+ "single_word": false,
75
+ "special": true
76
+ },
77
+ "92550": {
78
+ "content": "<|endofblock|>",
79
+ "lstrip": false,
80
+ "normalized": false,
81
+ "rstrip": false,
82
+ "single_word": false,
83
+ "special": true
84
+ },
85
+ "92551": {
86
+ "content": "<|endofmessage|>",
87
+ "lstrip": false,
88
+ "normalized": false,
89
+ "rstrip": false,
90
+ "single_word": false,
91
+ "special": true
92
+ }
93
+ },
94
+ "additional_special_tokens": [
95
+ "<|user|>",
96
+ "<|system|>",
97
+ "<|assistant|>",
98
+ "<|text|>",
99
+ "<|code|>",
100
+ "<|execution|>",
101
+ "<|endofblock|>",
102
+ "<|endofmessage|>"
103
+ ],
104
+ "auto_map": {
105
+ "AutoTokenizer": [
106
+ "tokenization_internlm2.InternLM2Tokenizer",
107
+ "tokenization_internlm2_fast.InternLM2TokenizerFast"
108
+ ]
109
+ },
110
+ "bos_token": "<s>",
111
+ "chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>' }}{% elif message['role'] == 'system' %}\n{{ '<|system|>' }}{% elif message['role'] == 'assistant_correct' %}\n{{ '<|assistant|>' }}{% endif %}\n{% for block in message['content'] %}\n{% if block['type'] == 'text' %}\n{{ '<|text|>' }}{% elif block['type'] == 'code' %}\n{{ '<|code|>' }}{% elif block['type'] == 'execution' %}\n{{ '<|execution|>' }}{% endif %}\n{{ block['content'] + '<|endofblock|>' }}{% endfor %}\n{% if message['role'] != 'assistant_correct' %}\n{{ '<|endofmessage|>' }}{% endif %}{% endfor %}",
112
+ "clean_up_tokenization_spaces": false,
113
+ "decode_with_prefix_space": false,
114
+ "eos_token": "</s>",
115
+ "model_max_length": 2048,
116
+ "pad_token": "</s>",
117
+ "sp_model_kwargs": null,
118
+ "tokenizer_class": "InternLM2Tokenizer",
119
+ "unk_token": "<unk>"
120
+ }
train_results.json ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "epoch": 2.0,
3
+ "train_loss": 0.2711078498627443,
4
+ "train_runtime": 49070.973,
5
+ "train_samples": 88135,
6
+ "train_samples_per_second": 3.592,
7
+ "train_steps_per_second": 0.112
8
+ }
trainer_state.json ADDED
The diff for this file is too large to render. See raw diff
 
training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:5e2dc1e14b5872983d56265bfab3d968c256a50d7ec35bfaae995ad6e102f79c
3
+ size 7224