First model version
Browse files- README.md +742 -1
- all_results.json +15 -0
- config.json +34 -0
- configuration_RW.py +79 -0
- eval_results.json +10 -0
- generation_config.json +6 -0
- merges.txt +0 -0
- modelling_RW.py +1100 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +24 -0
- tokenizer.json +0 -0
- tokenizer_config.json +33 -0
- train_results.json +8 -0
- trainer_state.json +0 -0
- training_args.bin +3 -0
- vocab.json +0 -0
README.md
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---
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| 1 |
---
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| 2 |
+
tags:
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| 3 |
+
- generated_from_trainer
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| 4 |
+
datasets:
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| 5 |
+
- /bscdata/data/open_data_26B_tokens_balanced_es_ca/open_data_26B_tokens_balanced_es_ca.py
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| 6 |
+
metrics:
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| 7 |
+
- accuracy
|
| 8 |
+
model-index:
|
| 9 |
+
- name: falcon_7b_balanced_tokenizer_fp16_CPT_open_data_26B_tokens_balanced_es_ca
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| 10 |
+
results:
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| 11 |
+
- task:
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| 12 |
+
name: Causal Language Modeling
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| 13 |
+
type: text-generation
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| 14 |
+
dataset:
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| 15 |
+
name: /bscdata/data/open_data_26B_tokens_balanced_es_ca/open_data_26B_tokens_balanced_es_ca.py
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| 16 |
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default
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| 17 |
+
type: /bscdata/data/open_data_26B_tokens_balanced_es_ca/open_data_26B_tokens_balanced_es_ca.py
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| 18 |
+
config: default
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| 19 |
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split: validation
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| 20 |
+
args: default
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| 21 |
+
metrics:
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| 22 |
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- name: Accuracy
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| 23 |
+
type: accuracy
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| 24 |
+
value: 0.5258444783433934
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| 25 |
---
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| 26 |
+
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| 27 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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| 28 |
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should probably proofread and complete it, then remove this comment. -->
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| 29 |
+
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| 30 |
+
# falcon_7b_balanced_tokenizer_fp16_CPT_open_data_26B_tokens_balanced_es_ca
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| 31 |
+
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| 32 |
+
This model is a fine-tuned version of [/bscdata/models/falcon_7b_balanced_tokenizer_fp16/](https://huggingface.co//bscdata/models/falcon_7b_balanced_tokenizer_fp16/) on the /bscdata/data/open_data_26B_tokens_balanced_es_ca/open_data_26B_tokens_balanced_es_ca.py default dataset.
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| 33 |
+
It achieves the following results on the evaluation set:
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| 34 |
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- Loss: 2.1504
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| 35 |
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- Accuracy: 0.5258
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| 36 |
+
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| 37 |
+
## Model description
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| 38 |
+
|
| 39 |
+
More information needed
|
| 40 |
+
|
| 41 |
+
## Intended uses & limitations
|
| 42 |
+
|
| 43 |
+
More information needed
|
| 44 |
+
|
| 45 |
+
## Training and evaluation data
|
| 46 |
+
|
| 47 |
+
More information needed
|
| 48 |
+
|
| 49 |
+
## Training procedure
|
| 50 |
+
|
| 51 |
+
### Training hyperparameters
|
| 52 |
+
|
| 53 |
+
The following hyperparameters were used during training:
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| 54 |
+
- learning_rate: 5e-05
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| 55 |
+
- train_batch_size: 1
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| 56 |
+
- eval_batch_size: 1
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| 57 |
+
- seed: 42
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| 58 |
+
- distributed_type: multi-GPU
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| 59 |
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- num_devices: 8
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| 60 |
+
- total_train_batch_size: 8
|
| 61 |
+
- total_eval_batch_size: 8
|
| 62 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
| 63 |
+
- lr_scheduler_type: linear
|
| 64 |
+
- num_epochs: 1.0
|
| 65 |
+
|
| 66 |
+
### Training results
|
| 67 |
+
|
| 68 |
+
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|
| 69 |
+
|:-------------:|:-----:|:-------:|:--------:|:---------------:|
|
| 70 |
+
| 5.3279 | 0.0 | 5000 | 0.3133 | 3.9941 |
|
| 71 |
+
| 3.5754 | 0.0 | 10000 | 0.3824 | 3.3105 |
|
| 72 |
+
| 3.6102 | 0.0 | 15000 | 0.3977 | 3.1660 |
|
| 73 |
+
| 3.0639 | 0.01 | 20000 | 0.4134 | 3.0215 |
|
| 74 |
+
| 2.9477 | 0.01 | 25000 | 0.4252 | 2.9199 |
|
| 75 |
+
| 2.8589 | 0.01 | 30000 | 0.4315 | 2.8672 |
|
| 76 |
+
| 2.8063 | 0.01 | 35000 | 0.4388 | 2.8027 |
|
| 77 |
+
| 2.7646 | 0.01 | 40000 | 0.4419 | 2.7715 |
|
| 78 |
+
| 2.7306 | 0.01 | 45000 | 0.4467 | 2.7363 |
|
| 79 |
+
| 2.7106 | 0.01 | 50000 | 0.4493 | 2.7129 |
|
| 80 |
+
| 2.6829 | 0.02 | 55000 | 0.4522 | 2.6895 |
|
| 81 |
+
| 2.6703 | 0.02 | 60000 | 0.4537 | 2.6758 |
|
| 82 |
+
| 2.6522 | 0.02 | 65000 | 0.4560 | 2.6602 |
|
| 83 |
+
| 2.6377 | 0.02 | 70000 | 0.4574 | 2.6484 |
|
| 84 |
+
| 2.6241 | 0.02 | 75000 | 0.4587 | 2.6348 |
|
| 85 |
+
| 2.6159 | 0.02 | 80000 | 0.4604 | 2.625 |
|
| 86 |
+
| 2.5959 | 0.03 | 85000 | 0.4613 | 2.6133 |
|
| 87 |
+
| 2.5877 | 0.03 | 90000 | 0.4624 | 2.6035 |
|
| 88 |
+
| 2.5832 | 0.03 | 95000 | 0.4632 | 2.5996 |
|
| 89 |
+
| 2.5726 | 0.03 | 100000 | 0.4648 | 2.5859 |
|
| 90 |
+
| 2.5723 | 0.03 | 105000 | 0.4655 | 2.5801 |
|
| 91 |
+
| 2.5584 | 0.03 | 110000 | 0.4641 | 2.5938 |
|
| 92 |
+
| 2.5541 | 0.03 | 115000 | 0.4673 | 2.5664 |
|
| 93 |
+
| 2.541 | 0.04 | 120000 | 0.4684 | 2.5586 |
|
| 94 |
+
| 2.5359 | 0.04 | 125000 | 0.4674 | 2.5645 |
|
| 95 |
+
| 2.5298 | 0.04 | 130000 | 0.4699 | 2.5449 |
|
| 96 |
+
| 2.5258 | 0.04 | 135000 | 0.4703 | 2.5410 |
|
| 97 |
+
| 2.5207 | 0.04 | 140000 | 0.4709 | 2.5371 |
|
| 98 |
+
| 2.5167 | 0.04 | 145000 | 0.4719 | 2.5312 |
|
| 99 |
+
| 2.5101 | 0.04 | 150000 | 0.4702 | 2.5449 |
|
| 100 |
+
| 2.5058 | 0.05 | 155000 | 0.4730 | 2.5215 |
|
| 101 |
+
| 2.5021 | 0.05 | 160000 | 0.4734 | 2.5195 |
|
| 102 |
+
| 2.8135 | 0.05 | 165000 | 0.4317 | 2.8320 |
|
| 103 |
+
| 2.7932 | 0.05 | 170000 | 0.4730 | 2.5215 |
|
| 104 |
+
| 2.4914 | 0.05 | 175000 | 0.4752 | 2.5059 |
|
| 105 |
+
| 2.487 | 0.05 | 180000 | 0.4754 | 2.5039 |
|
| 106 |
+
| 2.4829 | 0.06 | 185000 | 0.4751 | 2.5039 |
|
| 107 |
+
| 2.4778 | 0.06 | 190000 | 0.4763 | 2.4961 |
|
| 108 |
+
| 2.4779 | 0.06 | 195000 | 0.4770 | 2.4922 |
|
| 109 |
+
| 2.4685 | 0.06 | 200000 | 0.4766 | 2.4941 |
|
| 110 |
+
| 2.4661 | 0.06 | 205000 | 0.4776 | 2.4844 |
|
| 111 |
+
| 2.4579 | 0.06 | 210000 | 0.4783 | 2.4805 |
|
| 112 |
+
| 2.4589 | 0.06 | 215000 | 0.4788 | 2.4785 |
|
| 113 |
+
| 2.4571 | 0.07 | 220000 | 0.4793 | 2.4746 |
|
| 114 |
+
| 2.4504 | 0.07 | 225000 | 0.4797 | 2.4727 |
|
| 115 |
+
| 2.4538 | 0.07 | 230000 | 0.4800 | 2.4688 |
|
| 116 |
+
| 2.4481 | 0.07 | 235000 | 0.4806 | 2.4668 |
|
| 117 |
+
| 2.4454 | 0.07 | 240000 | 0.4810 | 2.4609 |
|
| 118 |
+
| 2.44 | 0.07 | 245000 | 0.4811 | 2.4590 |
|
| 119 |
+
| 2.4392 | 0.07 | 250000 | 0.4811 | 2.4590 |
|
| 120 |
+
| 2.431 | 0.08 | 255000 | 0.4813 | 2.4570 |
|
| 121 |
+
| 2.4377 | 0.08 | 260000 | 0.4823 | 2.4512 |
|
| 122 |
+
| 2.4299 | 0.08 | 265000 | 0.4826 | 2.4473 |
|
| 123 |
+
| 2.4283 | 0.08 | 270000 | 0.4828 | 2.4473 |
|
| 124 |
+
| 2.4256 | 0.08 | 275000 | 0.4833 | 2.4434 |
|
| 125 |
+
| 2.4198 | 0.08 | 280000 | 0.4838 | 2.4414 |
|
| 126 |
+
| 2.4174 | 0.09 | 285000 | 0.4840 | 2.4414 |
|
| 127 |
+
| 2.4151 | 0.09 | 290000 | 0.4844 | 2.4355 |
|
| 128 |
+
| 2.4191 | 0.09 | 295000 | 0.4847 | 2.4336 |
|
| 129 |
+
| 2.4071 | 0.09 | 300000 | 0.4848 | 2.4316 |
|
| 130 |
+
| 2.4126 | 0.09 | 305000 | 0.4855 | 2.4277 |
|
| 131 |
+
| 2.4053 | 0.09 | 310000 | 0.4851 | 2.4297 |
|
| 132 |
+
| 2.4071 | 0.09 | 315000 | 0.4858 | 2.4258 |
|
| 133 |
+
| 2.4027 | 0.1 | 320000 | 0.4866 | 2.4219 |
|
| 134 |
+
| 2.4013 | 0.1 | 325000 | 0.4867 | 2.4180 |
|
| 135 |
+
| 2.4032 | 0.1 | 330000 | 0.4866 | 2.4180 |
|
| 136 |
+
| 2.3919 | 0.1 | 335000 | 0.4871 | 2.4160 |
|
| 137 |
+
| 2.3936 | 0.1 | 340000 | 0.4873 | 2.4141 |
|
| 138 |
+
| 2.3905 | 0.1 | 345000 | 0.4878 | 2.4102 |
|
| 139 |
+
| 2.3889 | 0.1 | 350000 | 0.4881 | 2.4102 |
|
| 140 |
+
| 2.3866 | 0.11 | 355000 | 0.4884 | 2.4082 |
|
| 141 |
+
| 2.3823 | 0.11 | 360000 | 0.4888 | 2.4062 |
|
| 142 |
+
| 2.3828 | 0.11 | 365000 | 0.4888 | 2.4023 |
|
| 143 |
+
| 2.3795 | 0.11 | 370000 | 0.4889 | 2.4004 |
|
| 144 |
+
| 2.3812 | 0.11 | 375000 | 0.4868 | 2.4160 |
|
| 145 |
+
| 2.3789 | 0.11 | 380000 | 0.4896 | 2.3965 |
|
| 146 |
+
| 2.372 | 0.12 | 385000 | 0.4895 | 2.3965 |
|
| 147 |
+
| 2.3732 | 0.12 | 390000 | 0.4899 | 2.3965 |
|
| 148 |
+
| 2.3725 | 0.12 | 395000 | 0.4903 | 2.3926 |
|
| 149 |
+
| 2.3716 | 0.12 | 400000 | 0.4904 | 2.3906 |
|
| 150 |
+
| 2.3709 | 0.12 | 405000 | 0.4904 | 2.3906 |
|
| 151 |
+
| 2.3619 | 0.12 | 410000 | 0.4906 | 2.3887 |
|
| 152 |
+
| 2.367 | 0.12 | 415000 | 0.4912 | 2.3867 |
|
| 153 |
+
| 2.3639 | 0.13 | 420000 | 0.4912 | 2.3848 |
|
| 154 |
+
| 2.3621 | 0.13 | 425000 | 0.4919 | 2.3828 |
|
| 155 |
+
| 2.3578 | 0.13 | 430000 | 0.4920 | 2.3809 |
|
| 156 |
+
| 2.3608 | 0.13 | 435000 | 0.4922 | 2.3789 |
|
| 157 |
+
| 2.3541 | 0.13 | 440000 | 0.4923 | 2.3770 |
|
| 158 |
+
| 2.3556 | 0.13 | 445000 | 0.4926 | 2.3770 |
|
| 159 |
+
| 2.3562 | 0.13 | 450000 | 0.4928 | 2.3770 |
|
| 160 |
+
| 2.3641 | 0.14 | 455000 | 0.4910 | 2.3867 |
|
| 161 |
+
| 2.3641 | 0.14 | 460000 | 0.4911 | 2.3867 |
|
| 162 |
+
| 2.3646 | 0.14 | 465000 | 0.4911 | 2.3867 |
|
| 163 |
+
| 2.3629 | 0.14 | 470000 | 0.4911 | 2.3848 |
|
| 164 |
+
| 2.3659 | 0.14 | 475000 | 0.4914 | 2.3828 |
|
| 165 |
+
| 2.3651 | 0.14 | 480000 | 0.4916 | 2.3828 |
|
| 166 |
+
| 2.3608 | 0.15 | 485000 | 0.4918 | 2.3809 |
|
| 167 |
+
| 2.3612 | 0.15 | 490000 | 0.4920 | 2.3809 |
|
| 168 |
+
| 2.3569 | 0.15 | 495000 | 0.4922 | 2.3789 |
|
| 169 |
+
| 2.3557 | 0.15 | 500000 | 0.4923 | 2.3789 |
|
| 170 |
+
| 2.3541 | 0.15 | 505000 | 0.4922 | 2.3770 |
|
| 171 |
+
| 2.351 | 0.15 | 510000 | 0.4927 | 2.375 |
|
| 172 |
+
| 2.3504 | 0.15 | 515000 | 0.4926 | 2.375 |
|
| 173 |
+
| 2.3479 | 0.16 | 520000 | 0.4929 | 2.3730 |
|
| 174 |
+
| 2.3451 | 0.16 | 525000 | 0.4929 | 2.3711 |
|
| 175 |
+
| 2.3505 | 0.16 | 530000 | 0.4934 | 2.3691 |
|
| 176 |
+
| 2.3457 | 0.16 | 535000 | 0.4934 | 2.3691 |
|
| 177 |
+
| 2.3479 | 0.16 | 540000 | 0.4937 | 2.3691 |
|
| 178 |
+
| 2.3421 | 0.16 | 545000 | 0.4936 | 2.3672 |
|
| 179 |
+
| 2.3433 | 0.16 | 550000 | 0.4937 | 2.3672 |
|
| 180 |
+
| 2.3425 | 0.17 | 555000 | 0.4939 | 2.3652 |
|
| 181 |
+
| 2.3403 | 0.17 | 560000 | 0.4942 | 2.3633 |
|
| 182 |
+
| 2.3417 | 0.17 | 565000 | 0.4944 | 2.3613 |
|
| 183 |
+
| 2.3382 | 0.17 | 570000 | 0.4947 | 2.3613 |
|
| 184 |
+
| 2.3354 | 0.17 | 575000 | 0.4949 | 2.3594 |
|
| 185 |
+
| 2.3366 | 0.17 | 580000 | 0.4947 | 2.3594 |
|
| 186 |
+
| 2.3373 | 0.18 | 585000 | 0.4945 | 2.3594 |
|
| 187 |
+
| 2.3365 | 0.18 | 590000 | 0.4949 | 2.3594 |
|
| 188 |
+
| 2.3318 | 0.18 | 595000 | 0.4953 | 2.3555 |
|
| 189 |
+
| 2.3278 | 0.18 | 600000 | 0.4958 | 2.3535 |
|
| 190 |
+
| 2.3277 | 0.18 | 605000 | 0.4959 | 2.3516 |
|
| 191 |
+
| 2.326 | 0.18 | 610000 | 0.4961 | 2.3516 |
|
| 192 |
+
| 2.3273 | 0.18 | 615000 | 0.4961 | 2.3516 |
|
| 193 |
+
| 2.3284 | 0.19 | 620000 | 0.4965 | 2.3496 |
|
| 194 |
+
| 2.3276 | 0.19 | 625000 | 0.4966 | 2.3477 |
|
| 195 |
+
| 2.3228 | 0.19 | 630000 | 0.4967 | 2.3457 |
|
| 196 |
+
| 2.3219 | 0.19 | 635000 | 0.4968 | 2.3457 |
|
| 197 |
+
| 2.326 | 0.19 | 640000 | 0.4970 | 2.3438 |
|
| 198 |
+
| 2.3191 | 0.19 | 645000 | 0.4972 | 2.3418 |
|
| 199 |
+
| 2.3167 | 0.19 | 650000 | 0.4973 | 2.3438 |
|
| 200 |
+
| 2.3172 | 0.2 | 655000 | 0.4974 | 2.3418 |
|
| 201 |
+
| 2.3194 | 0.2 | 660000 | 0.4977 | 2.3379 |
|
| 202 |
+
| 2.3204 | 0.2 | 665000 | 0.4976 | 2.3398 |
|
| 203 |
+
| 2.309 | 0.2 | 670000 | 0.4980 | 2.3359 |
|
| 204 |
+
| 2.3147 | 0.2 | 675000 | 0.4981 | 2.3379 |
|
| 205 |
+
| 2.3122 | 0.2 | 680000 | 0.4980 | 2.3359 |
|
| 206 |
+
| 2.3096 | 0.21 | 685000 | 0.4984 | 2.3340 |
|
| 207 |
+
| 2.3093 | 0.21 | 690000 | 0.4986 | 2.3340 |
|
| 208 |
+
| 2.3048 | 0.21 | 695000 | 0.4985 | 2.3320 |
|
| 209 |
+
| 2.3111 | 0.21 | 700000 | 0.4988 | 2.3301 |
|
| 210 |
+
| 2.3074 | 0.21 | 705000 | 0.4989 | 2.3301 |
|
| 211 |
+
| 2.3082 | 0.21 | 710000 | 0.4992 | 2.3301 |
|
| 212 |
+
| 2.3093 | 0.21 | 715000 | 0.4994 | 2.3281 |
|
| 213 |
+
| 2.3011 | 0.22 | 720000 | 0.4995 | 2.3281 |
|
| 214 |
+
| 2.2998 | 0.22 | 725000 | 0.4995 | 2.3262 |
|
| 215 |
+
| 2.3012 | 0.22 | 730000 | 0.4996 | 2.3262 |
|
| 216 |
+
| 2.3002 | 0.22 | 735000 | 0.4997 | 2.3242 |
|
| 217 |
+
| 2.2994 | 0.22 | 740000 | 0.5000 | 2.3242 |
|
| 218 |
+
| 2.299 | 0.22 | 745000 | 0.5001 | 2.3223 |
|
| 219 |
+
| 2.2969 | 0.22 | 750000 | 0.5003 | 2.3223 |
|
| 220 |
+
| 2.2934 | 0.23 | 755000 | 0.5004 | 2.3203 |
|
| 221 |
+
| 2.2988 | 0.23 | 760000 | 0.5005 | 2.3184 |
|
| 222 |
+
| 2.2911 | 0.23 | 765000 | 0.5007 | 2.3184 |
|
| 223 |
+
| 2.2929 | 0.23 | 770000 | 0.5008 | 2.3184 |
|
| 224 |
+
| 2.2926 | 0.23 | 775000 | 0.5009 | 2.3164 |
|
| 225 |
+
| 2.292 | 0.23 | 780000 | 0.5012 | 2.3164 |
|
| 226 |
+
| 2.2932 | 0.24 | 785000 | 0.5014 | 2.3145 |
|
| 227 |
+
| 2.2903 | 0.24 | 790000 | 0.5014 | 2.3145 |
|
| 228 |
+
| 2.2886 | 0.24 | 795000 | 0.5015 | 2.3125 |
|
| 229 |
+
| 2.2924 | 0.24 | 800000 | 0.5015 | 2.3125 |
|
| 230 |
+
| 2.2891 | 0.24 | 805000 | 0.5019 | 2.3105 |
|
| 231 |
+
| 2.2862 | 0.24 | 810000 | 0.5020 | 2.3086 |
|
| 232 |
+
| 2.2858 | 0.24 | 815000 | 0.5022 | 2.3086 |
|
| 233 |
+
| 2.2841 | 0.25 | 820000 | 0.5023 | 2.3066 |
|
| 234 |
+
| 2.2843 | 0.25 | 825000 | 0.5022 | 2.3086 |
|
| 235 |
+
| 2.2832 | 0.25 | 830000 | 0.5025 | 2.3066 |
|
| 236 |
+
| 2.2846 | 0.25 | 835000 | 0.5026 | 2.3066 |
|
| 237 |
+
| 2.2784 | 0.25 | 840000 | 0.5027 | 2.3047 |
|
| 238 |
+
| 2.277 | 0.25 | 845000 | 0.5028 | 2.3027 |
|
| 239 |
+
| 2.276 | 0.25 | 850000 | 0.5026 | 2.3066 |
|
| 240 |
+
| 2.2802 | 0.26 | 855000 | 0.5031 | 2.3027 |
|
| 241 |
+
| 2.2781 | 0.26 | 860000 | 0.5032 | 2.3008 |
|
| 242 |
+
| 2.2749 | 0.26 | 865000 | 0.5038 | 2.2988 |
|
| 243 |
+
| 2.2729 | 0.26 | 870000 | 0.5037 | 2.2969 |
|
| 244 |
+
| 2.2708 | 0.26 | 875000 | 0.5039 | 2.2969 |
|
| 245 |
+
| 2.2754 | 0.26 | 880000 | 0.5039 | 2.2969 |
|
| 246 |
+
| 2.2761 | 0.27 | 885000 | 0.5041 | 2.2949 |
|
| 247 |
+
| 2.2742 | 0.27 | 890000 | 0.5041 | 2.2949 |
|
| 248 |
+
| 2.2734 | 0.27 | 895000 | 0.5041 | 2.2949 |
|
| 249 |
+
| 2.2682 | 0.27 | 900000 | 0.5044 | 2.2930 |
|
| 250 |
+
| 2.2667 | 0.27 | 905000 | 0.5045 | 2.2930 |
|
| 251 |
+
| 2.2676 | 0.27 | 910000 | 0.5046 | 2.2930 |
|
| 252 |
+
| 2.2707 | 0.27 | 915000 | 0.5047 | 2.2910 |
|
| 253 |
+
| 2.265 | 0.28 | 920000 | 0.5048 | 2.2910 |
|
| 254 |
+
| 2.2676 | 0.28 | 925000 | 0.5046 | 2.2910 |
|
| 255 |
+
| 2.2662 | 0.28 | 930000 | 0.5052 | 2.2891 |
|
| 256 |
+
| 2.2706 | 0.28 | 935000 | 0.5051 | 2.2891 |
|
| 257 |
+
| 2.2657 | 0.28 | 940000 | 0.5049 | 2.2891 |
|
| 258 |
+
| 2.2672 | 0.28 | 945000 | 0.5050 | 2.2871 |
|
| 259 |
+
| 2.2716 | 0.28 | 950000 | 0.5037 | 2.2969 |
|
| 260 |
+
| 2.2702 | 0.29 | 955000 | 0.5037 | 2.2988 |
|
| 261 |
+
| 2.2708 | 0.29 | 960000 | 0.5035 | 2.2988 |
|
| 262 |
+
| 2.2738 | 0.29 | 965000 | 0.5035 | 2.2988 |
|
| 263 |
+
| 2.2737 | 0.29 | 970000 | 0.5036 | 2.2988 |
|
| 264 |
+
| 2.2763 | 0.29 | 975000 | 0.4987 | 2.3301 |
|
| 265 |
+
| 2.2738 | 0.29 | 980000 | 0.5035 | 2.2969 |
|
| 266 |
+
| 2.2737 | 0.3 | 985000 | 0.5036 | 2.2969 |
|
| 267 |
+
| 2.2748 | 0.3 | 990000 | 0.5036 | 2.2969 |
|
| 268 |
+
| 2.2724 | 0.3 | 995000 | 0.5038 | 2.2969 |
|
| 269 |
+
| 2.2744 | 0.3 | 1000000 | 0.5033 | 2.2988 |
|
| 270 |
+
| 2.2694 | 0.3 | 1005000 | 0.5033 | 2.2988 |
|
| 271 |
+
| 2.2684 | 0.3 | 1010000 | 0.5039 | 2.2949 |
|
| 272 |
+
| 2.2731 | 0.3 | 1015000 | 0.5040 | 2.2949 |
|
| 273 |
+
| 2.2714 | 0.31 | 1020000 | 0.5042 | 2.2949 |
|
| 274 |
+
| 2.2687 | 0.31 | 1025000 | 0.5045 | 2.2930 |
|
| 275 |
+
| 2.2673 | 0.31 | 1030000 | 0.5046 | 2.2930 |
|
| 276 |
+
| 2.2677 | 0.31 | 1035000 | 0.5044 | 2.2930 |
|
| 277 |
+
| 2.265 | 0.31 | 1040000 | 0.5047 | 2.2910 |
|
| 278 |
+
| 2.2659 | 0.31 | 1045000 | 0.5045 | 2.2910 |
|
| 279 |
+
| 2.2633 | 0.31 | 1050000 | 0.5042 | 2.2949 |
|
| 280 |
+
| 2.2689 | 0.32 | 1055000 | 0.5050 | 2.2891 |
|
| 281 |
+
| 2.2617 | 0.32 | 1060000 | 0.5049 | 2.2891 |
|
| 282 |
+
| 2.2613 | 0.32 | 1065000 | 0.5052 | 2.2871 |
|
| 283 |
+
| 2.2649 | 0.32 | 1070000 | 0.5047 | 2.2891 |
|
| 284 |
+
| 2.2587 | 0.32 | 1075000 | 0.5053 | 2.2871 |
|
| 285 |
+
| 2.2641 | 0.32 | 1080000 | 0.5054 | 2.2852 |
|
| 286 |
+
| 2.2634 | 0.33 | 1085000 | 0.5057 | 2.2852 |
|
| 287 |
+
| 2.2597 | 0.33 | 1090000 | 0.5057 | 2.2832 |
|
| 288 |
+
| 2.2572 | 0.33 | 1095000 | 0.5060 | 2.2832 |
|
| 289 |
+
| 2.2566 | 0.33 | 1100000 | 0.5056 | 2.2832 |
|
| 290 |
+
| 2.2576 | 0.33 | 1105000 | 0.5056 | 2.2832 |
|
| 291 |
+
| 2.2612 | 0.33 | 1110000 | 0.5057 | 2.2832 |
|
| 292 |
+
| 2.2585 | 0.33 | 1115000 | 0.5059 | 2.2812 |
|
| 293 |
+
| 2.2528 | 0.34 | 1120000 | 0.5060 | 2.2812 |
|
| 294 |
+
| 2.2599 | 0.34 | 1125000 | 0.5060 | 2.2812 |
|
| 295 |
+
| 2.2556 | 0.34 | 1130000 | 0.5066 | 2.2773 |
|
| 296 |
+
| 2.2519 | 0.34 | 1135000 | 0.5064 | 2.2793 |
|
| 297 |
+
| 2.2567 | 0.34 | 1140000 | 0.5068 | 2.2773 |
|
| 298 |
+
| 2.2516 | 0.34 | 1145000 | 0.5069 | 2.2754 |
|
| 299 |
+
| 2.2533 | 0.34 | 1150000 | 0.5068 | 2.2754 |
|
| 300 |
+
| 2.2532 | 0.35 | 1155000 | 0.5070 | 2.2754 |
|
| 301 |
+
| 2.2572 | 0.35 | 1160000 | 0.5064 | 2.2793 |
|
| 302 |
+
| 2.2514 | 0.35 | 1165000 | 0.5072 | 2.2734 |
|
| 303 |
+
| 2.2471 | 0.35 | 1170000 | 0.5073 | 2.2734 |
|
| 304 |
+
| 2.2524 | 0.35 | 1175000 | 0.5076 | 2.2715 |
|
| 305 |
+
| 2.247 | 0.35 | 1180000 | 0.5073 | 2.2715 |
|
| 306 |
+
| 2.2491 | 0.35 | 1185000 | 0.5077 | 2.2715 |
|
| 307 |
+
| 2.2481 | 0.36 | 1190000 | 0.5078 | 2.2695 |
|
| 308 |
+
| 2.2465 | 0.36 | 1195000 | 0.5069 | 2.2734 |
|
| 309 |
+
| 2.2494 | 0.36 | 1200000 | 0.5067 | 2.2793 |
|
| 310 |
+
| 2.2541 | 0.36 | 1205000 | 0.5069 | 2.2754 |
|
| 311 |
+
| 2.25 | 0.36 | 1210000 | 0.5067 | 2.2754 |
|
| 312 |
+
| 2.25 | 0.36 | 1215000 | 0.5064 | 2.2793 |
|
| 313 |
+
| 2.2508 | 0.37 | 1220000 | 0.5070 | 2.2734 |
|
| 314 |
+
| 2.2496 | 0.37 | 1225000 | 0.5070 | 2.2734 |
|
| 315 |
+
| 2.2499 | 0.37 | 1230000 | 0.5073 | 2.2734 |
|
| 316 |
+
| 2.2467 | 0.37 | 1235000 | 0.5076 | 2.2715 |
|
| 317 |
+
| 2.2497 | 0.37 | 1240000 | 0.5073 | 2.2715 |
|
| 318 |
+
| 2.2463 | 0.37 | 1245000 | 0.5073 | 2.2715 |
|
| 319 |
+
| 2.2479 | 0.37 | 1250000 | 0.5078 | 2.2695 |
|
| 320 |
+
| 2.2445 | 0.38 | 1255000 | 0.5079 | 2.2695 |
|
| 321 |
+
| 2.247 | 0.38 | 1260000 | 0.5078 | 2.2695 |
|
| 322 |
+
| 2.2443 | 0.38 | 1265000 | 0.5079 | 2.2676 |
|
| 323 |
+
| 2.243 | 0.38 | 1270000 | 0.5081 | 2.2676 |
|
| 324 |
+
| 2.2454 | 0.38 | 1275000 | 0.5077 | 2.2715 |
|
| 325 |
+
| 2.2451 | 0.38 | 1280000 | 0.5081 | 2.2695 |
|
| 326 |
+
| 2.2455 | 0.38 | 1285000 | 0.5084 | 2.2656 |
|
| 327 |
+
| 2.241 | 0.39 | 1290000 | 0.5083 | 2.2676 |
|
| 328 |
+
| 2.243 | 0.39 | 1295000 | 0.5086 | 2.2637 |
|
| 329 |
+
| 2.2408 | 0.39 | 1300000 | 0.5084 | 2.2637 |
|
| 330 |
+
| 2.2508 | 0.39 | 1305000 | 0.5063 | 2.2793 |
|
| 331 |
+
| 2.252 | 0.39 | 1310000 | 0.5047 | 2.2910 |
|
| 332 |
+
| 2.7482 | 0.39 | 1315000 | 0.4506 | 2.6465 |
|
| 333 |
+
| 2.4189 | 0.4 | 1320000 | 0.5070 | 2.2754 |
|
| 334 |
+
| 2.2446 | 0.4 | 1325000 | 0.5081 | 2.2676 |
|
| 335 |
+
| 2.2416 | 0.4 | 1330000 | 0.5087 | 2.2637 |
|
| 336 |
+
| 2.2421 | 0.4 | 1335000 | 0.5088 | 2.2617 |
|
| 337 |
+
| 2.2367 | 0.4 | 1340000 | 0.5092 | 2.2617 |
|
| 338 |
+
| 2.2355 | 0.4 | 1345000 | 0.5091 | 2.2598 |
|
| 339 |
+
| 2.2379 | 0.4 | 1350000 | 0.5094 | 2.2598 |
|
| 340 |
+
| 2.2365 | 0.41 | 1355000 | 0.5094 | 2.2598 |
|
| 341 |
+
| 2.2379 | 0.41 | 1360000 | 0.5091 | 2.2578 |
|
| 342 |
+
| 2.235 | 0.41 | 1365000 | 0.5095 | 2.2578 |
|
| 343 |
+
| 2.236 | 0.41 | 1370000 | 0.5093 | 2.2578 |
|
| 344 |
+
| 2.2344 | 0.41 | 1375000 | 0.5095 | 2.2578 |
|
| 345 |
+
| 2.2348 | 0.41 | 1380000 | 0.5096 | 2.2559 |
|
| 346 |
+
| 2.2306 | 0.41 | 1385000 | 0.5097 | 2.2559 |
|
| 347 |
+
| 2.2293 | 0.42 | 1390000 | 0.5098 | 2.2559 |
|
| 348 |
+
| 2.2311 | 0.42 | 1395000 | 0.5101 | 2.2539 |
|
| 349 |
+
| 2.231 | 0.42 | 1400000 | 0.5101 | 2.2539 |
|
| 350 |
+
| 2.2272 | 0.42 | 1405000 | 0.5102 | 2.2520 |
|
| 351 |
+
| 2.2264 | 0.42 | 1410000 | 0.5102 | 2.2539 |
|
| 352 |
+
| 2.2295 | 0.42 | 1415000 | 0.5104 | 2.2520 |
|
| 353 |
+
| 2.2281 | 0.43 | 1420000 | 0.5104 | 2.2520 |
|
| 354 |
+
| 2.2234 | 0.43 | 1425000 | 0.5107 | 2.25 |
|
| 355 |
+
| 2.2293 | 0.43 | 1430000 | 0.5107 | 2.25 |
|
| 356 |
+
| 2.2256 | 0.43 | 1435000 | 0.5109 | 2.25 |
|
| 357 |
+
| 2.2247 | 0.43 | 1440000 | 0.5108 | 2.25 |
|
| 358 |
+
| 2.222 | 0.43 | 1445000 | 0.5108 | 2.25 |
|
| 359 |
+
| 2.2228 | 0.43 | 1450000 | 0.5106 | 2.2480 |
|
| 360 |
+
| 2.2241 | 0.44 | 1455000 | 0.5111 | 2.2480 |
|
| 361 |
+
| 2.2219 | 0.44 | 1460000 | 0.5111 | 2.2461 |
|
| 362 |
+
| 2.2219 | 0.44 | 1465000 | 0.5113 | 2.2461 |
|
| 363 |
+
| 2.2215 | 0.44 | 1470000 | 0.5113 | 2.2461 |
|
| 364 |
+
| 2.2193 | 0.44 | 1475000 | 0.5116 | 2.2441 |
|
| 365 |
+
| 2.2183 | 0.44 | 1480000 | 0.5115 | 2.2441 |
|
| 366 |
+
| 2.2177 | 0.44 | 1485000 | 0.5116 | 2.2441 |
|
| 367 |
+
| 2.2211 | 0.45 | 1490000 | 0.5116 | 2.2422 |
|
| 368 |
+
| 2.2183 | 0.45 | 1495000 | 0.5118 | 2.2422 |
|
| 369 |
+
| 2.2182 | 0.45 | 1500000 | 0.5120 | 2.2402 |
|
| 370 |
+
| 2.2148 | 0.45 | 1505000 | 0.5122 | 2.2402 |
|
| 371 |
+
| 2.2217 | 0.45 | 1510000 | 0.5123 | 2.2402 |
|
| 372 |
+
| 2.2117 | 0.45 | 1515000 | 0.5124 | 2.2383 |
|
| 373 |
+
| 2.2152 | 0.46 | 1520000 | 0.5123 | 2.2383 |
|
| 374 |
+
| 2.2148 | 0.46 | 1525000 | 0.5125 | 2.2383 |
|
| 375 |
+
| 2.2151 | 0.46 | 1530000 | 0.5127 | 2.2363 |
|
| 376 |
+
| 2.2129 | 0.46 | 1535000 | 0.5127 | 2.2363 |
|
| 377 |
+
| 2.2145 | 0.46 | 1540000 | 0.5128 | 2.2363 |
|
| 378 |
+
| 2.2099 | 0.46 | 1545000 | 0.5129 | 2.2363 |
|
| 379 |
+
| 2.2125 | 0.46 | 1550000 | 0.5132 | 2.2344 |
|
| 380 |
+
| 2.2101 | 0.47 | 1555000 | 0.5131 | 2.2344 |
|
| 381 |
+
| 2.211 | 0.47 | 1560000 | 0.5132 | 2.2344 |
|
| 382 |
+
| 2.2086 | 0.47 | 1565000 | 0.5132 | 2.2344 |
|
| 383 |
+
| 2.2137 | 0.47 | 1570000 | 0.5132 | 2.2324 |
|
| 384 |
+
| 2.2122 | 0.47 | 1575000 | 0.5134 | 2.2324 |
|
| 385 |
+
| 2.2053 | 0.47 | 1580000 | 0.5134 | 2.2324 |
|
| 386 |
+
| 2.208 | 0.47 | 1585000 | 0.5134 | 2.2305 |
|
| 387 |
+
| 2.2081 | 0.48 | 1590000 | 0.5136 | 2.2305 |
|
| 388 |
+
| 2.2077 | 0.48 | 1595000 | 0.5138 | 2.2305 |
|
| 389 |
+
| 2.2061 | 0.48 | 1600000 | 0.5136 | 2.2305 |
|
| 390 |
+
| 2.2055 | 0.48 | 1605000 | 0.5139 | 2.2285 |
|
| 391 |
+
| 2.2065 | 0.48 | 1610000 | 0.5139 | 2.2285 |
|
| 392 |
+
| 2.2054 | 0.48 | 1615000 | 0.5139 | 2.2285 |
|
| 393 |
+
| 2.2035 | 0.49 | 1620000 | 0.5140 | 2.2285 |
|
| 394 |
+
| 2.2021 | 0.49 | 1625000 | 0.5140 | 2.2285 |
|
| 395 |
+
| 2.2036 | 0.49 | 1630000 | 0.5138 | 2.2285 |
|
| 396 |
+
| 2.204 | 0.49 | 1635000 | 0.5140 | 2.2266 |
|
| 397 |
+
| 2.2042 | 0.49 | 1640000 | 0.5141 | 2.2266 |
|
| 398 |
+
| 2.2024 | 0.49 | 1645000 | 0.5142 | 2.2266 |
|
| 399 |
+
| 2.2023 | 0.49 | 1650000 | 0.5144 | 2.2266 |
|
| 400 |
+
| 2.1976 | 0.5 | 1655000 | 0.5146 | 2.2246 |
|
| 401 |
+
| 2.2028 | 0.5 | 1660000 | 0.5147 | 2.2246 |
|
| 402 |
+
| 2.1971 | 0.5 | 1665000 | 0.5146 | 2.2246 |
|
| 403 |
+
| 2.1978 | 0.5 | 1670000 | 0.5146 | 2.2246 |
|
| 404 |
+
| 2.1955 | 0.5 | 1675000 | 0.5148 | 2.2227 |
|
| 405 |
+
| 2.1967 | 0.5 | 1680000 | 0.5147 | 2.2227 |
|
| 406 |
+
| 2.1975 | 0.5 | 1685000 | 0.5152 | 2.2227 |
|
| 407 |
+
| 2.1972 | 0.51 | 1690000 | 0.5149 | 2.2207 |
|
| 408 |
+
| 2.1967 | 0.51 | 1695000 | 0.5151 | 2.2207 |
|
| 409 |
+
| 2.194 | 0.51 | 1700000 | 0.5151 | 2.2207 |
|
| 410 |
+
| 2.2009 | 0.51 | 1705000 | 0.5139 | 2.2285 |
|
| 411 |
+
| 2.2085 | 0.51 | 1710000 | 0.5136 | 2.2305 |
|
| 412 |
+
| 2.2077 | 0.51 | 1715000 | 0.5137 | 2.2305 |
|
| 413 |
+
| 2.205 | 0.52 | 1720000 | 0.5134 | 2.2305 |
|
| 414 |
+
| 2.2063 | 0.52 | 1725000 | 0.5134 | 2.2305 |
|
| 415 |
+
| 2.2076 | 0.52 | 1730000 | 0.5135 | 2.2305 |
|
| 416 |
+
| 2.2036 | 0.52 | 1735000 | 0.5133 | 2.2305 |
|
| 417 |
+
| 2.2064 | 0.52 | 1740000 | 0.5138 | 2.2305 |
|
| 418 |
+
| 2.2053 | 0.52 | 1745000 | 0.5137 | 2.2305 |
|
| 419 |
+
| 2.2048 | 0.52 | 1750000 | 0.5139 | 2.2305 |
|
| 420 |
+
| 2.2075 | 0.53 | 1755000 | 0.5138 | 2.2305 |
|
| 421 |
+
| 2.2041 | 0.53 | 1760000 | 0.5136 | 2.2285 |
|
| 422 |
+
| 2.2057 | 0.53 | 1765000 | 0.5139 | 2.2285 |
|
| 423 |
+
| 2.2054 | 0.53 | 1770000 | 0.5139 | 2.2285 |
|
| 424 |
+
| 2.2085 | 0.53 | 1775000 | 0.5139 | 2.2285 |
|
| 425 |
+
| 2.2051 | 0.53 | 1780000 | 0.5141 | 2.2266 |
|
| 426 |
+
| 2.2023 | 0.53 | 1785000 | 0.5139 | 2.2266 |
|
| 427 |
+
| 2.205 | 0.54 | 1790000 | 0.5141 | 2.2266 |
|
| 428 |
+
| 2.2009 | 0.54 | 1795000 | 0.5141 | 2.2266 |
|
| 429 |
+
| 2.1998 | 0.54 | 1800000 | 0.5143 | 2.2266 |
|
| 430 |
+
| 2.2009 | 0.54 | 1805000 | 0.5144 | 2.2246 |
|
| 431 |
+
| 2.2027 | 0.54 | 1810000 | 0.5143 | 2.2266 |
|
| 432 |
+
| 2.2007 | 0.54 | 1815000 | 0.5146 | 2.2246 |
|
| 433 |
+
| 2.1978 | 0.55 | 1820000 | 0.5145 | 2.2246 |
|
| 434 |
+
| 2.1999 | 0.55 | 1825000 | 0.5146 | 2.2227 |
|
| 435 |
+
| 2.1978 | 0.55 | 1830000 | 0.5148 | 2.2227 |
|
| 436 |
+
| 2.1989 | 0.55 | 1835000 | 0.5147 | 2.2227 |
|
| 437 |
+
| 2.1989 | 0.55 | 1840000 | 0.5148 | 2.2227 |
|
| 438 |
+
| 2.1982 | 0.55 | 1845000 | 0.5150 | 2.2207 |
|
| 439 |
+
| 2.1974 | 0.55 | 1850000 | 0.5151 | 2.2207 |
|
| 440 |
+
| 2.1972 | 0.56 | 1855000 | 0.5151 | 2.2207 |
|
| 441 |
+
| 2.1966 | 0.56 | 1860000 | 0.5151 | 2.2207 |
|
| 442 |
+
| 2.198 | 0.56 | 1865000 | 0.5150 | 2.2207 |
|
| 443 |
+
| 2.1978 | 0.56 | 1870000 | 0.5152 | 2.2207 |
|
| 444 |
+
| 2.1938 | 0.56 | 1875000 | 0.5152 | 2.2207 |
|
| 445 |
+
| 2.1908 | 0.56 | 1880000 | 0.5152 | 2.2188 |
|
| 446 |
+
| 2.1899 | 0.56 | 1885000 | 0.5152 | 2.2188 |
|
| 447 |
+
| 2.1938 | 0.57 | 1890000 | 0.5152 | 2.2188 |
|
| 448 |
+
| 2.1909 | 0.57 | 1895000 | 0.5154 | 2.2188 |
|
| 449 |
+
| 2.1921 | 0.57 | 1900000 | 0.5155 | 2.2188 |
|
| 450 |
+
| 2.1926 | 0.57 | 1905000 | 0.5156 | 2.2168 |
|
| 451 |
+
| 2.194 | 0.57 | 1910000 | 0.5154 | 2.2168 |
|
| 452 |
+
| 2.1942 | 0.57 | 1915000 | 0.5152 | 2.2188 |
|
| 453 |
+
| 2.1947 | 0.58 | 1920000 | 0.5151 | 2.2188 |
|
| 454 |
+
| 2.1941 | 0.58 | 1925000 | 0.5151 | 2.2207 |
|
| 455 |
+
| 2.1984 | 0.58 | 1930000 | 0.5152 | 2.2207 |
|
| 456 |
+
| 2.1929 | 0.58 | 1935000 | 0.5151 | 2.2207 |
|
| 457 |
+
| 2.1921 | 0.58 | 1940000 | 0.5154 | 2.2188 |
|
| 458 |
+
| 2.1932 | 0.58 | 1945000 | 0.5153 | 2.2188 |
|
| 459 |
+
| 2.1959 | 0.58 | 1950000 | 0.5154 | 2.2188 |
|
| 460 |
+
| 2.1927 | 0.59 | 1955000 | 0.5154 | 2.2188 |
|
| 461 |
+
| 2.1949 | 0.59 | 1960000 | 0.5155 | 2.2188 |
|
| 462 |
+
| 2.1918 | 0.59 | 1965000 | 0.5154 | 2.2168 |
|
| 463 |
+
| 2.1957 | 0.59 | 1970000 | 0.5155 | 2.2168 |
|
| 464 |
+
| 2.1884 | 0.59 | 1975000 | 0.5157 | 2.2168 |
|
| 465 |
+
| 2.1942 | 0.59 | 1980000 | 0.5156 | 2.2148 |
|
| 466 |
+
| 2.1938 | 0.59 | 1985000 | 0.5156 | 2.2168 |
|
| 467 |
+
| 2.1935 | 0.6 | 1990000 | 0.5160 | 2.2148 |
|
| 468 |
+
| 2.1902 | 0.6 | 1995000 | 0.5157 | 2.2148 |
|
| 469 |
+
| 2.188 | 0.6 | 2000000 | 0.5158 | 2.2148 |
|
| 470 |
+
| 2.1862 | 0.6 | 2005000 | 0.5159 | 2.2129 |
|
| 471 |
+
| 2.1886 | 0.6 | 2010000 | 0.5161 | 2.2129 |
|
| 472 |
+
| 2.1811 | 0.6 | 2015000 | 0.5161 | 2.2129 |
|
| 473 |
+
| 2.19 | 0.61 | 2020000 | 0.5160 | 2.2129 |
|
| 474 |
+
| 2.1895 | 0.61 | 2025000 | 0.5165 | 2.2129 |
|
| 475 |
+
| 2.1904 | 0.61 | 2030000 | 0.5161 | 2.2129 |
|
| 476 |
+
| 2.1854 | 0.61 | 2035000 | 0.5165 | 2.2129 |
|
| 477 |
+
| 2.1883 | 0.61 | 2040000 | 0.5165 | 2.2109 |
|
| 478 |
+
| 2.1859 | 0.61 | 2045000 | 0.5165 | 2.2109 |
|
| 479 |
+
| 2.1849 | 0.61 | 2050000 | 0.5168 | 2.2090 |
|
| 480 |
+
| 2.1844 | 0.62 | 2055000 | 0.5167 | 2.2109 |
|
| 481 |
+
| 2.1866 | 0.62 | 2060000 | 0.5167 | 2.2090 |
|
| 482 |
+
| 2.1865 | 0.62 | 2065000 | 0.5168 | 2.2090 |
|
| 483 |
+
| 2.1846 | 0.62 | 2070000 | 0.5171 | 2.2070 |
|
| 484 |
+
| 2.1821 | 0.62 | 2075000 | 0.5170 | 2.2070 |
|
| 485 |
+
| 2.184 | 0.62 | 2080000 | 0.5170 | 2.2070 |
|
| 486 |
+
| 2.1847 | 0.62 | 2085000 | 0.5173 | 2.2051 |
|
| 487 |
+
| 2.1836 | 0.63 | 2090000 | 0.5174 | 2.2051 |
|
| 488 |
+
| 2.1791 | 0.63 | 2095000 | 0.5174 | 2.2051 |
|
| 489 |
+
| 2.1812 | 0.63 | 2100000 | 0.5173 | 2.2051 |
|
| 490 |
+
| 2.1835 | 0.63 | 2105000 | 0.5176 | 2.2051 |
|
| 491 |
+
| 2.1806 | 0.63 | 2110000 | 0.5176 | 2.2051 |
|
| 492 |
+
| 2.1832 | 0.63 | 2115000 | 0.5175 | 2.2051 |
|
| 493 |
+
| 2.1766 | 0.64 | 2120000 | 0.5178 | 2.2031 |
|
| 494 |
+
| 2.1775 | 0.64 | 2125000 | 0.5178 | 2.2031 |
|
| 495 |
+
| 2.1801 | 0.64 | 2130000 | 0.5177 | 2.2031 |
|
| 496 |
+
| 2.1789 | 0.64 | 2135000 | 0.5178 | 2.2031 |
|
| 497 |
+
| 2.1794 | 0.64 | 2140000 | 0.5178 | 2.2031 |
|
| 498 |
+
| 2.1799 | 0.64 | 2145000 | 0.5179 | 2.2012 |
|
| 499 |
+
| 2.1746 | 0.64 | 2150000 | 0.5180 | 2.2012 |
|
| 500 |
+
| 2.1766 | 0.65 | 2155000 | 0.5179 | 2.2012 |
|
| 501 |
+
| 2.1754 | 0.65 | 2160000 | 0.5177 | 2.2012 |
|
| 502 |
+
| 2.1764 | 0.65 | 2165000 | 0.5177 | 2.2012 |
|
| 503 |
+
| 2.1745 | 0.65 | 2170000 | 0.5183 | 2.1992 |
|
| 504 |
+
| 2.1735 | 0.65 | 2175000 | 0.5180 | 2.1992 |
|
| 505 |
+
| 2.1778 | 0.65 | 2180000 | 0.5181 | 2.1992 |
|
| 506 |
+
| 2.1717 | 0.65 | 2185000 | 0.5183 | 2.1992 |
|
| 507 |
+
| 2.1752 | 0.66 | 2190000 | 0.5185 | 2.1973 |
|
| 508 |
+
| 2.1747 | 0.66 | 2195000 | 0.5185 | 2.1973 |
|
| 509 |
+
| 2.1754 | 0.66 | 2200000 | 0.5186 | 2.1973 |
|
| 510 |
+
| 2.1728 | 0.66 | 2205000 | 0.5188 | 2.1973 |
|
| 511 |
+
| 2.1684 | 0.66 | 2210000 | 0.5186 | 2.1973 |
|
| 512 |
+
| 2.1722 | 0.66 | 2215000 | 0.5188 | 2.1953 |
|
| 513 |
+
| 2.1692 | 0.67 | 2220000 | 0.5190 | 2.1953 |
|
| 514 |
+
| 2.176 | 0.67 | 2225000 | 0.5191 | 2.1953 |
|
| 515 |
+
| 2.1697 | 0.67 | 2230000 | 0.5190 | 2.1953 |
|
| 516 |
+
| 2.1731 | 0.67 | 2235000 | 0.5191 | 2.1953 |
|
| 517 |
+
| 2.173 | 0.67 | 2240000 | 0.5191 | 2.1934 |
|
| 518 |
+
| 2.1714 | 0.67 | 2245000 | 0.5193 | 2.1934 |
|
| 519 |
+
| 2.1719 | 0.67 | 2250000 | 0.5192 | 2.1934 |
|
| 520 |
+
| 2.1667 | 0.68 | 2255000 | 0.5190 | 2.1934 |
|
| 521 |
+
| 2.1653 | 0.68 | 2260000 | 0.5192 | 2.1934 |
|
| 522 |
+
| 2.1656 | 0.68 | 2265000 | 0.5193 | 2.1914 |
|
| 523 |
+
| 2.1695 | 0.68 | 2270000 | 0.5194 | 2.1914 |
|
| 524 |
+
| 2.17 | 0.68 | 2275000 | 0.5196 | 2.1914 |
|
| 525 |
+
| 2.1628 | 0.68 | 2280000 | 0.5197 | 2.1914 |
|
| 526 |
+
| 2.1648 | 0.68 | 2285000 | 0.5196 | 2.1895 |
|
| 527 |
+
| 2.1647 | 0.69 | 2290000 | 0.5199 | 2.1895 |
|
| 528 |
+
| 2.1648 | 0.69 | 2295000 | 0.5198 | 2.1895 |
|
| 529 |
+
| 2.168 | 0.69 | 2300000 | 0.5197 | 2.1895 |
|
| 530 |
+
| 2.1607 | 0.69 | 2305000 | 0.5198 | 2.1895 |
|
| 531 |
+
| 2.1674 | 0.69 | 2310000 | 0.5200 | 2.1875 |
|
| 532 |
+
| 2.1656 | 0.69 | 2315000 | 0.5200 | 2.1875 |
|
| 533 |
+
| 2.1637 | 0.7 | 2320000 | 0.5202 | 2.1875 |
|
| 534 |
+
| 2.1649 | 0.7 | 2325000 | 0.5201 | 2.1875 |
|
| 535 |
+
| 2.1625 | 0.7 | 2330000 | 0.5201 | 2.1875 |
|
| 536 |
+
| 2.1627 | 0.7 | 2335000 | 0.5203 | 2.1875 |
|
| 537 |
+
| 2.1598 | 0.7 | 2340000 | 0.5203 | 2.1855 |
|
| 538 |
+
| 2.1638 | 0.7 | 2345000 | 0.5201 | 2.1875 |
|
| 539 |
+
| 2.1588 | 0.7 | 2350000 | 0.5205 | 2.1855 |
|
| 540 |
+
| 2.1633 | 0.71 | 2355000 | 0.5205 | 2.1855 |
|
| 541 |
+
| 2.1621 | 0.71 | 2360000 | 0.5205 | 2.1855 |
|
| 542 |
+
| 2.165 | 0.71 | 2365000 | 0.5207 | 2.1836 |
|
| 543 |
+
| 2.159 | 0.71 | 2370000 | 0.5206 | 2.1836 |
|
| 544 |
+
| 2.1573 | 0.71 | 2375000 | 0.5207 | 2.1836 |
|
| 545 |
+
| 2.1556 | 0.71 | 2380000 | 0.5208 | 2.1836 |
|
| 546 |
+
| 2.1562 | 0.71 | 2385000 | 0.5210 | 2.1836 |
|
| 547 |
+
| 2.1572 | 0.72 | 2390000 | 0.5209 | 2.1836 |
|
| 548 |
+
| 2.1577 | 0.72 | 2395000 | 0.5209 | 2.1816 |
|
| 549 |
+
| 2.1529 | 0.72 | 2400000 | 0.5210 | 2.1816 |
|
| 550 |
+
| 2.1636 | 0.72 | 2405000 | 0.5211 | 2.1816 |
|
| 551 |
+
| 2.1521 | 0.72 | 2410000 | 0.5213 | 2.1816 |
|
| 552 |
+
| 2.1574 | 0.72 | 2415000 | 0.5214 | 2.1816 |
|
| 553 |
+
| 2.1546 | 0.72 | 2420000 | 0.5213 | 2.1797 |
|
| 554 |
+
| 2.1572 | 0.73 | 2425000 | 0.5212 | 2.1797 |
|
| 555 |
+
| 2.1544 | 0.73 | 2430000 | 0.5212 | 2.1797 |
|
| 556 |
+
| 2.15 | 0.73 | 2435000 | 0.5213 | 2.1797 |
|
| 557 |
+
| 2.1537 | 0.73 | 2440000 | 0.5217 | 2.1777 |
|
| 558 |
+
| 2.1552 | 0.73 | 2445000 | 0.5216 | 2.1777 |
|
| 559 |
+
| 2.1522 | 0.73 | 2450000 | 0.5215 | 2.1777 |
|
| 560 |
+
| 2.1487 | 0.74 | 2455000 | 0.5215 | 2.1777 |
|
| 561 |
+
| 2.1582 | 0.74 | 2460000 | 0.5215 | 2.1777 |
|
| 562 |
+
| 2.1582 | 0.74 | 2465000 | 0.5218 | 2.1777 |
|
| 563 |
+
| 2.1529 | 0.74 | 2470000 | 0.5218 | 2.1777 |
|
| 564 |
+
| 2.1549 | 0.74 | 2475000 | 0.5219 | 2.1758 |
|
| 565 |
+
| 2.1525 | 0.74 | 2480000 | 0.5219 | 2.1758 |
|
| 566 |
+
| 2.1478 | 0.74 | 2485000 | 0.5221 | 2.1758 |
|
| 567 |
+
| 2.1524 | 0.75 | 2490000 | 0.5220 | 2.1758 |
|
| 568 |
+
| 2.1477 | 0.75 | 2495000 | 0.5220 | 2.1738 |
|
| 569 |
+
| 2.1524 | 0.75 | 2500000 | 0.5222 | 2.1738 |
|
| 570 |
+
| 2.147 | 0.75 | 2505000 | 0.5222 | 2.1738 |
|
| 571 |
+
| 2.1481 | 0.75 | 2510000 | 0.5223 | 2.1738 |
|
| 572 |
+
| 2.1494 | 0.75 | 2515000 | 0.5223 | 2.1738 |
|
| 573 |
+
| 2.1484 | 0.75 | 2520000 | 0.5223 | 2.1738 |
|
| 574 |
+
| 2.1474 | 0.76 | 2525000 | 0.5223 | 2.1738 |
|
| 575 |
+
| 2.1487 | 0.76 | 2530000 | 0.5223 | 2.1738 |
|
| 576 |
+
| 2.1465 | 0.76 | 2535000 | 0.5225 | 2.1719 |
|
| 577 |
+
| 2.1456 | 0.76 | 2540000 | 0.5226 | 2.1719 |
|
| 578 |
+
| 2.1482 | 0.76 | 2545000 | 0.5224 | 2.1719 |
|
| 579 |
+
| 2.1451 | 0.76 | 2550000 | 0.5226 | 2.1719 |
|
| 580 |
+
| 2.143 | 0.77 | 2555000 | 0.5226 | 2.1719 |
|
| 581 |
+
| 2.1463 | 0.77 | 2560000 | 0.5225 | 2.1719 |
|
| 582 |
+
| 2.1466 | 0.77 | 2565000 | 0.5228 | 2.1699 |
|
| 583 |
+
| 2.1423 | 0.77 | 2570000 | 0.5229 | 2.1699 |
|
| 584 |
+
| 2.1423 | 0.77 | 2575000 | 0.5231 | 2.1699 |
|
| 585 |
+
| 2.1444 | 0.77 | 2580000 | 0.5230 | 2.1699 |
|
| 586 |
+
| 2.1402 | 0.77 | 2585000 | 0.5230 | 2.1680 |
|
| 587 |
+
| 2.1376 | 0.78 | 2590000 | 0.5231 | 2.1680 |
|
| 588 |
+
| 2.1395 | 0.78 | 2595000 | 0.5232 | 2.1680 |
|
| 589 |
+
| 2.1399 | 0.78 | 2600000 | 0.5233 | 2.1680 |
|
| 590 |
+
| 2.1379 | 0.78 | 2605000 | 0.5231 | 2.1680 |
|
| 591 |
+
| 2.1411 | 0.78 | 2610000 | 0.5234 | 2.1660 |
|
| 592 |
+
| 2.1421 | 0.78 | 2615000 | 0.5232 | 2.1660 |
|
| 593 |
+
| 2.1412 | 0.78 | 2620000 | 0.5237 | 2.1660 |
|
| 594 |
+
| 2.1381 | 0.79 | 2625000 | 0.5236 | 2.1660 |
|
| 595 |
+
| 2.142 | 0.79 | 2630000 | 0.5236 | 2.1660 |
|
| 596 |
+
| 2.1394 | 0.79 | 2635000 | 0.5236 | 2.1641 |
|
| 597 |
+
| 2.1384 | 0.79 | 2640000 | 0.5234 | 2.1641 |
|
| 598 |
+
| 2.138 | 0.79 | 2645000 | 0.5236 | 2.1641 |
|
| 599 |
+
| 2.1346 | 0.79 | 2650000 | 0.5239 | 2.1641 |
|
| 600 |
+
| 2.1376 | 0.8 | 2655000 | 0.5239 | 2.1641 |
|
| 601 |
+
| 2.1409 | 0.8 | 2660000 | 0.5240 | 2.1641 |
|
| 602 |
+
| 2.1343 | 0.8 | 2665000 | 0.5240 | 2.1641 |
|
| 603 |
+
| 2.1363 | 0.8 | 2670000 | 0.5240 | 2.1621 |
|
| 604 |
+
| 2.1343 | 0.8 | 2675000 | 0.5242 | 2.1621 |
|
| 605 |
+
| 2.1381 | 0.8 | 2680000 | 0.5243 | 2.1621 |
|
| 606 |
+
| 2.1355 | 0.8 | 2685000 | 0.5241 | 2.1621 |
|
| 607 |
+
| 2.1394 | 0.81 | 2690000 | 0.5242 | 2.1602 |
|
| 608 |
+
| 2.1359 | 0.81 | 2695000 | 0.5245 | 2.1602 |
|
| 609 |
+
| 2.1365 | 0.81 | 2700000 | 0.5244 | 2.1602 |
|
| 610 |
+
| 2.131 | 0.81 | 2705000 | 0.5244 | 2.1602 |
|
| 611 |
+
| 2.1337 | 0.81 | 2710000 | 0.5244 | 2.1602 |
|
| 612 |
+
| 2.1307 | 0.81 | 2715000 | 0.5246 | 2.1582 |
|
| 613 |
+
| 2.1333 | 0.81 | 2720000 | 0.5247 | 2.1582 |
|
| 614 |
+
| 2.1354 | 0.82 | 2725000 | 0.5246 | 2.1582 |
|
| 615 |
+
| 2.1372 | 0.82 | 2730000 | 0.5248 | 2.1582 |
|
| 616 |
+
| 2.1323 | 0.82 | 2735000 | 0.5248 | 2.1582 |
|
| 617 |
+
| 2.1315 | 0.82 | 2740000 | 0.5249 | 2.1562 |
|
| 618 |
+
| 2.1341 | 0.82 | 2745000 | 0.5249 | 2.1562 |
|
| 619 |
+
| 2.132 | 0.82 | 2750000 | 0.5250 | 2.1562 |
|
| 620 |
+
| 2.1322 | 0.83 | 2755000 | 0.5252 | 2.1562 |
|
| 621 |
+
| 2.1298 | 0.83 | 2760000 | 0.5252 | 2.1562 |
|
| 622 |
+
| 2.1285 | 0.83 | 2765000 | 0.5252 | 2.1543 |
|
| 623 |
+
| 2.1299 | 0.83 | 2770000 | 0.5252 | 2.1562 |
|
| 624 |
+
| 2.1304 | 0.83 | 2775000 | 0.5253 | 2.1543 |
|
| 625 |
+
| 2.1288 | 0.83 | 2780000 | 0.5254 | 2.1543 |
|
| 626 |
+
| 2.1295 | 0.83 | 2785000 | 0.5253 | 2.1543 |
|
| 627 |
+
| 2.129 | 0.84 | 2790000 | 0.5255 | 2.1543 |
|
| 628 |
+
| 2.1285 | 0.84 | 2795000 | 0.5254 | 2.1543 |
|
| 629 |
+
| 2.1292 | 0.84 | 2800000 | 0.5253 | 2.1543 |
|
| 630 |
+
| 2.1278 | 0.84 | 2805000 | 0.5256 | 2.1523 |
|
| 631 |
+
| 2.1239 | 0.84 | 2810000 | 0.5255 | 2.1523 |
|
| 632 |
+
| 2.1241 | 0.84 | 2815000 | 0.5259 | 2.1523 |
|
| 633 |
+
| 2.1232 | 0.84 | 2820000 | 0.5257 | 2.1523 |
|
| 634 |
+
| 2.1241 | 0.85 | 2825000 | 0.5257 | 2.1504 |
|
| 635 |
+
| 2.1236 | 0.85 | 2830000 | 0.5259 | 2.1504 |
|
| 636 |
+
| 2.1272 | 0.85 | 2835000 | 0.5259 | 2.1504 |
|
| 637 |
+
| 2.1271 | 0.85 | 2840000 | 0.5261 | 2.1504 |
|
| 638 |
+
| 2.1249 | 0.85 | 2845000 | 0.5262 | 2.1484 |
|
| 639 |
+
| 2.1245 | 0.85 | 2850000 | 0.5260 | 2.1484 |
|
| 640 |
+
| 2.1222 | 0.86 | 2855000 | 0.5261 | 2.1484 |
|
| 641 |
+
| 2.125 | 0.86 | 2860000 | 0.5263 | 2.1484 |
|
| 642 |
+
| 2.1261 | 0.86 | 2865000 | 0.5261 | 2.1484 |
|
| 643 |
+
| 2.1247 | 0.86 | 2870000 | 0.5262 | 2.1484 |
|
| 644 |
+
| 2.1225 | 0.86 | 2875000 | 0.5263 | 2.1484 |
|
| 645 |
+
| 2.122 | 0.86 | 2880000 | 0.5261 | 2.1484 |
|
| 646 |
+
| 2.1237 | 0.86 | 2885000 | 0.5261 | 2.1465 |
|
| 647 |
+
| 2.1219 | 0.87 | 2890000 | 0.5262 | 2.1465 |
|
| 648 |
+
| 2.1248 | 0.87 | 2895000 | 0.5262 | 2.1465 |
|
| 649 |
+
| 2.1191 | 0.87 | 2900000 | 0.5264 | 2.1465 |
|
| 650 |
+
| 2.1181 | 0.87 | 2905000 | 0.5264 | 2.1465 |
|
| 651 |
+
| 2.1176 | 0.87 | 2910000 | 0.5263 | 2.1465 |
|
| 652 |
+
| 2.1191 | 0.87 | 2915000 | 0.5267 | 2.1465 |
|
| 653 |
+
| 2.1206 | 0.87 | 2920000 | 0.5268 | 2.1445 |
|
| 654 |
+
| 2.1148 | 0.88 | 2925000 | 0.5267 | 2.1445 |
|
| 655 |
+
| 2.1188 | 0.88 | 2930000 | 0.5270 | 2.1445 |
|
| 656 |
+
| 2.1118 | 0.88 | 2935000 | 0.5270 | 2.1445 |
|
| 657 |
+
| 2.1283 | 0.88 | 2940000 | 2.1582 | 0.5244 |
|
| 658 |
+
| 2.1336 | 0.88 | 2945000 | 2.1621 | 0.5240 |
|
| 659 |
+
| 2.1311 | 0.88 | 2950000 | 2.1621 | 0.5237 |
|
| 660 |
+
| 2.1377 | 0.89 | 2955000 | 2.1641 | 0.5236 |
|
| 661 |
+
| 2.136 | 0.89 | 2960000 | 2.1641 | 0.5236 |
|
| 662 |
+
| 2.1394 | 0.89 | 2965000 | 2.1641 | 0.5233 |
|
| 663 |
+
| 2.1405 | 0.89 | 2970000 | 2.1660 | 0.5233 |
|
| 664 |
+
| 2.1391 | 0.89 | 2975000 | 2.1660 | 0.5236 |
|
| 665 |
+
| 2.1353 | 0.89 | 2980000 | 2.1660 | 0.5234 |
|
| 666 |
+
| 2.1392 | 0.89 | 2985000 | 2.1660 | 0.5234 |
|
| 667 |
+
| 2.1384 | 0.9 | 2990000 | 2.1660 | 0.5235 |
|
| 668 |
+
| 2.1373 | 0.9 | 2995000 | 2.1660 | 0.5233 |
|
| 669 |
+
| 2.1346 | 0.9 | 3000000 | 2.1660 | 0.5234 |
|
| 670 |
+
| 2.1368 | 0.9 | 3005000 | 2.1660 | 0.5235 |
|
| 671 |
+
| 2.1383 | 0.9 | 3010000 | 2.1660 | 0.5233 |
|
| 672 |
+
| 2.1447 | 0.9 | 3015000 | 2.1660 | 0.5233 |
|
| 673 |
+
| 2.1392 | 0.9 | 3020000 | 2.1660 | 0.5234 |
|
| 674 |
+
| 2.1359 | 0.91 | 3025000 | 2.1660 | 0.5233 |
|
| 675 |
+
| 2.1408 | 0.91 | 3030000 | 2.1660 | 0.5233 |
|
| 676 |
+
| 2.1437 | 0.91 | 3035000 | 2.1660 | 0.5233 |
|
| 677 |
+
| 2.1354 | 0.91 | 3040000 | 2.1660 | 0.5233 |
|
| 678 |
+
| 2.1371 | 0.91 | 3045000 | 2.1660 | 0.5235 |
|
| 679 |
+
| 2.1399 | 0.91 | 3050000 | 2.1660 | 0.5234 |
|
| 680 |
+
| 2.1387 | 0.92 | 3055000 | 2.1660 | 0.5234 |
|
| 681 |
+
| 2.1406 | 0.92 | 3060000 | 2.1660 | 0.5232 |
|
| 682 |
+
| 2.1387 | 0.92 | 3065000 | 2.1660 | 0.5235 |
|
| 683 |
+
| 2.1413 | 0.92 | 3070000 | 2.1660 | 0.5235 |
|
| 684 |
+
| 2.1371 | 0.92 | 3075000 | 2.1641 | 0.5235 |
|
| 685 |
+
| 2.138 | 0.92 | 3080000 | 2.1641 | 0.5235 |
|
| 686 |
+
| 2.1385 | 0.92 | 3085000 | 2.1641 | 0.5236 |
|
| 687 |
+
| 2.135 | 0.93 | 3090000 | 2.1660 | 0.5234 |
|
| 688 |
+
| 2.1401 | 0.93 | 3095000 | 2.1641 | 0.5236 |
|
| 689 |
+
| 2.1374 | 0.93 | 3100000 | 2.1641 | 0.5236 |
|
| 690 |
+
| 2.1358 | 0.93 | 3105000 | 2.1641 | 0.5237 |
|
| 691 |
+
| 2.1344 | 0.93 | 3110000 | 2.1621 | 0.5239 |
|
| 692 |
+
| 2.1368 | 0.93 | 3115000 | 2.1621 | 0.5239 |
|
| 693 |
+
| 2.1345 | 0.93 | 3120000 | 2.1621 | 0.5237 |
|
| 694 |
+
| 2.1358 | 0.94 | 3125000 | 2.1621 | 0.5239 |
|
| 695 |
+
| 2.1395 | 0.94 | 3130000 | 2.1621 | 0.5239 |
|
| 696 |
+
| 2.1359 | 0.94 | 3135000 | 2.1621 | 0.5243 |
|
| 697 |
+
| 2.1373 | 0.94 | 3140000 | 2.1602 | 0.5242 |
|
| 698 |
+
| 2.1357 | 0.94 | 3145000 | 2.1602 | 0.5243 |
|
| 699 |
+
| 2.1354 | 0.94 | 3150000 | 2.1602 | 0.5244 |
|
| 700 |
+
| 2.1323 | 0.95 | 3155000 | 2.1602 | 0.5244 |
|
| 701 |
+
| 2.133 | 0.95 | 3160000 | 2.1602 | 0.5242 |
|
| 702 |
+
| 2.1315 | 0.95 | 3165000 | 2.1602 | 0.5244 |
|
| 703 |
+
| 2.1363 | 0.95 | 3170000 | 2.1602 | 0.5243 |
|
| 704 |
+
| 2.1349 | 0.95 | 3175000 | 2.1602 | 0.5245 |
|
| 705 |
+
| 2.1336 | 0.95 | 3180000 | 2.1602 | 0.5244 |
|
| 706 |
+
| 2.1364 | 0.95 | 3185000 | 2.1582 | 0.5244 |
|
| 707 |
+
| 2.133 | 0.96 | 3190000 | 2.1582 | 0.5243 |
|
| 708 |
+
| 2.1349 | 0.96 | 3195000 | 2.1582 | 0.5245 |
|
| 709 |
+
| 2.134 | 0.96 | 3200000 | 2.1582 | 0.5246 |
|
| 710 |
+
| 2.1308 | 0.96 | 3205000 | 2.1562 | 0.5249 |
|
| 711 |
+
| 2.1302 | 0.96 | 3210000 | 2.1562 | 0.5247 |
|
| 712 |
+
| 2.1302 | 0.96 | 3215000 | 2.1562 | 0.5247 |
|
| 713 |
+
| 2.1331 | 0.96 | 3220000 | 2.1562 | 0.5248 |
|
| 714 |
+
| 2.1273 | 0.97 | 3225000 | 2.1562 | 0.5247 |
|
| 715 |
+
| 2.1286 | 0.97 | 3230000 | 2.1562 | 0.5250 |
|
| 716 |
+
| 2.1282 | 0.97 | 3235000 | 2.1543 | 0.5250 |
|
| 717 |
+
| 2.1309 | 0.97 | 3240000 | 2.1543 | 0.5251 |
|
| 718 |
+
| 2.1295 | 0.97 | 3245000 | 2.1543 | 0.5254 |
|
| 719 |
+
| 2.1275 | 0.97 | 3250000 | 2.1543 | 0.5254 |
|
| 720 |
+
| 2.133 | 0.98 | 3255000 | 2.1543 | 0.5254 |
|
| 721 |
+
| 2.1301 | 0.98 | 3260000 | 2.1543 | 0.5251 |
|
| 722 |
+
| 2.1314 | 0.98 | 3265000 | 2.1523 | 0.5253 |
|
| 723 |
+
| 2.1258 | 0.98 | 3270000 | 2.1523 | 0.5255 |
|
| 724 |
+
| 2.1286 | 0.98 | 3275000 | 2.1523 | 0.5254 |
|
| 725 |
+
| 2.1267 | 0.98 | 3280000 | 2.1523 | 0.5254 |
|
| 726 |
+
| 2.13 | 0.98 | 3285000 | 2.1523 | 0.5254 |
|
| 727 |
+
| 2.1284 | 0.99 | 3290000 | 2.1523 | 0.5255 |
|
| 728 |
+
| 2.1295 | 0.99 | 3295000 | 2.1523 | 0.5254 |
|
| 729 |
+
| 2.1241 | 0.99 | 3300000 | 2.1523 | 0.5256 |
|
| 730 |
+
| 2.1297 | 0.99 | 3305000 | 2.1523 | 0.5258 |
|
| 731 |
+
| 2.126 | 0.99 | 3310000 | 2.1504 | 0.5256 |
|
| 732 |
+
| 2.1263 | 0.99 | 3315000 | 2.1504 | 0.5256 |
|
| 733 |
+
| 2.1273 | 0.99 | 3320000 | 2.1504 | 0.5256 |
|
| 734 |
+
| 2.1214 | 1.0 | 3325000 | 2.1504 | 0.5255 |
|
| 735 |
+
| 2.1275 | 1.0 | 3330000 | 2.1504 | 0.5256 |
|
| 736 |
+
| 2.1227 | 1.0 | 3335000 | 2.1504 | 0.5258 |
|
| 737 |
+
|
| 738 |
+
|
| 739 |
+
### Framework versions
|
| 740 |
+
|
| 741 |
+
- Transformers 4.30.2
|
| 742 |
+
- Pytorch 2.0.0
|
| 743 |
+
- Datasets 2.13.1
|
| 744 |
+
- Tokenizers 0.13.3
|
all_results.json
ADDED
|
@@ -0,0 +1,15 @@
|
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|
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|
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|
| 1 |
+
{
|
| 2 |
+
"epoch": 1.0,
|
| 3 |
+
"eval_accuracy": 0.5258444783433934,
|
| 4 |
+
"eval_loss": 2.150390625,
|
| 5 |
+
"eval_runtime": 41.4599,
|
| 6 |
+
"eval_samples": 3568,
|
| 7 |
+
"eval_samples_per_second": 86.059,
|
| 8 |
+
"eval_steps_per_second": 10.757,
|
| 9 |
+
"perplexity": 8.5882125125473,
|
| 10 |
+
"train_loss": 0.2576859601399347,
|
| 11 |
+
"train_runtime": 150388.7494,
|
| 12 |
+
"train_samples": 26705019,
|
| 13 |
+
"train_samples_per_second": 177.573,
|
| 14 |
+
"train_steps_per_second": 22.197
|
| 15 |
+
}
|
config.json
ADDED
|
@@ -0,0 +1,34 @@
|
|
|
|
|
|
|
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|
|
|
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|
|
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|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "/bscdata/models/falcon_7b_balanced_tokenizer_fp16/",
|
| 3 |
+
"alibi": false,
|
| 4 |
+
"apply_residual_connection_post_layernorm": false,
|
| 5 |
+
"architectures": [
|
| 6 |
+
"RWForCausalLM"
|
| 7 |
+
],
|
| 8 |
+
"attention_dropout": 0.0,
|
| 9 |
+
"auto_map": {
|
| 10 |
+
"AutoConfig": "configuration_RW.RWConfig",
|
| 11 |
+
"AutoModel": "modelling_RW.RWModel",
|
| 12 |
+
"AutoModelForCausalLM": "modelling_RW.RWForCausalLM",
|
| 13 |
+
"AutoModelForQuestionAnswering": "modelling_RW.RWForQuestionAnswering",
|
| 14 |
+
"AutoModelForSequenceClassification": "modelling_RW.RWForSequenceClassification",
|
| 15 |
+
"AutoModelForTokenClassification": "modelling_RW.RWForTokenClassification"
|
| 16 |
+
},
|
| 17 |
+
"bias": false,
|
| 18 |
+
"bos_token_id": 50256,
|
| 19 |
+
"eos_token_id": 50256,
|
| 20 |
+
"hidden_dropout": 0.0,
|
| 21 |
+
"hidden_size": 4544,
|
| 22 |
+
"initializer_range": 0.02,
|
| 23 |
+
"layer_norm_epsilon": 1e-05,
|
| 24 |
+
"model_type": "RefinedWebModel",
|
| 25 |
+
"multi_query": true,
|
| 26 |
+
"n_head": 71,
|
| 27 |
+
"n_layer": 32,
|
| 28 |
+
"pad_token_id": 50256,
|
| 29 |
+
"parallel_attn": true,
|
| 30 |
+
"torch_dtype": "float16",
|
| 31 |
+
"transformers_version": "4.30.2",
|
| 32 |
+
"use_cache": true,
|
| 33 |
+
"vocab_size": 50257
|
| 34 |
+
}
|
configuration_RW.py
ADDED
|
@@ -0,0 +1,79 @@
|
|
|
|
|
|
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|
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|
|
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|
|
|
| 1 |
+
# coding=utf-8
|
| 2 |
+
# Copyright 2022 the Big Science Workshop and HuggingFace Inc. team. All rights reserved.
|
| 3 |
+
#
|
| 4 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 5 |
+
# you may not use this file except in compliance with the License.
|
| 6 |
+
# You may obtain a copy of the License at
|
| 7 |
+
#
|
| 8 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
| 9 |
+
#
|
| 10 |
+
# Unless required by applicable law or agreed to in writing, software
|
| 11 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 12 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 13 |
+
# See the License for the specific language governing permissions and
|
| 14 |
+
# limitations under the License.
|
| 15 |
+
""" Bloom configuration"""
|
| 16 |
+
from transformers.configuration_utils import PretrainedConfig
|
| 17 |
+
from transformers.utils import logging
|
| 18 |
+
|
| 19 |
+
|
| 20 |
+
logger = logging.get_logger(__name__)
|
| 21 |
+
|
| 22 |
+
|
| 23 |
+
class RWConfig(PretrainedConfig):
|
| 24 |
+
model_type = "RefinedWebModel"
|
| 25 |
+
keys_to_ignore_at_inference = ["past_key_values"]
|
| 26 |
+
attribute_map = {
|
| 27 |
+
"num_hidden_layers": "n_layer",
|
| 28 |
+
"num_attention_heads": "n_head",
|
| 29 |
+
}
|
| 30 |
+
|
| 31 |
+
def __init__(
|
| 32 |
+
self,
|
| 33 |
+
vocab_size=250880,
|
| 34 |
+
hidden_size=64,
|
| 35 |
+
n_layer=2,
|
| 36 |
+
n_head=8,
|
| 37 |
+
layer_norm_epsilon=1e-5,
|
| 38 |
+
initializer_range=0.02,
|
| 39 |
+
use_cache=True,
|
| 40 |
+
bos_token_id=1,
|
| 41 |
+
eos_token_id=2,
|
| 42 |
+
apply_residual_connection_post_layernorm=False,
|
| 43 |
+
hidden_dropout=0.0,
|
| 44 |
+
attention_dropout=0.0,
|
| 45 |
+
multi_query=False,
|
| 46 |
+
alibi=False,
|
| 47 |
+
bias=False,
|
| 48 |
+
parallel_attn=False,
|
| 49 |
+
**kwargs,
|
| 50 |
+
):
|
| 51 |
+
self.vocab_size = vocab_size
|
| 52 |
+
# Backward compatibility with n_embed kwarg
|
| 53 |
+
n_embed = kwargs.pop("n_embed", None)
|
| 54 |
+
self.hidden_size = hidden_size if n_embed is None else n_embed
|
| 55 |
+
self.n_layer = n_layer
|
| 56 |
+
self.n_head = n_head
|
| 57 |
+
self.layer_norm_epsilon = layer_norm_epsilon
|
| 58 |
+
self.initializer_range = initializer_range
|
| 59 |
+
self.use_cache = use_cache
|
| 60 |
+
self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm
|
| 61 |
+
self.hidden_dropout = hidden_dropout
|
| 62 |
+
self.attention_dropout = attention_dropout
|
| 63 |
+
|
| 64 |
+
self.bos_token_id = bos_token_id
|
| 65 |
+
self.eos_token_id = eos_token_id
|
| 66 |
+
self.multi_query = multi_query
|
| 67 |
+
self.alibi = alibi
|
| 68 |
+
self.bias = bias
|
| 69 |
+
self.parallel_attn = parallel_attn
|
| 70 |
+
|
| 71 |
+
super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
|
| 72 |
+
|
| 73 |
+
@property
|
| 74 |
+
def head_dim(self):
|
| 75 |
+
return self.hidden_size // self.n_head
|
| 76 |
+
|
| 77 |
+
@property
|
| 78 |
+
def rotary(self):
|
| 79 |
+
return not self.alibi
|
eval_results.json
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"epoch": 1.0,
|
| 3 |
+
"eval_accuracy": 0.5258444783433934,
|
| 4 |
+
"eval_loss": 2.150390625,
|
| 5 |
+
"eval_runtime": 41.4599,
|
| 6 |
+
"eval_samples": 3568,
|
| 7 |
+
"eval_samples_per_second": 86.059,
|
| 8 |
+
"eval_steps_per_second": 10.757,
|
| 9 |
+
"perplexity": 8.5882125125473
|
| 10 |
+
}
|
generation_config.json
ADDED
|
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_from_model_config": true,
|
| 3 |
+
"bos_token_id": 1,
|
| 4 |
+
"eos_token_id": 2,
|
| 5 |
+
"transformers_version": "4.30.2"
|
| 6 |
+
}
|
merges.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
modelling_RW.py
ADDED
|
@@ -0,0 +1,1100 @@
|
|
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|
| 1 |
+
# port of models described in RW
|
| 2 |
+
# We use the bloom model as a starting point for these model.
|
| 3 |
+
# Please refer to the bloom models for usage instructions.
|
| 4 |
+
|
| 5 |
+
import math
|
| 6 |
+
import warnings
|
| 7 |
+
from typing import Optional, Tuple, Union
|
| 8 |
+
|
| 9 |
+
import torch
|
| 10 |
+
import torch.utils.checkpoint
|
| 11 |
+
from torch import nn
|
| 12 |
+
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss
|
| 13 |
+
from torch.nn import functional as F
|
| 14 |
+
|
| 15 |
+
from transformers.modeling_outputs import (
|
| 16 |
+
BaseModelOutputWithPastAndCrossAttentions,
|
| 17 |
+
CausalLMOutputWithCrossAttentions,
|
| 18 |
+
QuestionAnsweringModelOutput,
|
| 19 |
+
SequenceClassifierOutputWithPast,
|
| 20 |
+
TokenClassifierOutput,
|
| 21 |
+
)
|
| 22 |
+
from transformers.modeling_utils import PreTrainedModel
|
| 23 |
+
from transformers.utils import logging
|
| 24 |
+
from .configuration_RW import RWConfig
|
| 25 |
+
|
| 26 |
+
logger = logging.get_logger(__name__)
|
| 27 |
+
|
| 28 |
+
# NOTE(Hesslow): Unfortunately we did not fuse matmul and bias during training, this means that there's one additional quantization to bfloat16 between the operations.
|
| 29 |
+
# In order not to degrade the quality of our HF-port, we keep these characteristics in the final model.
|
| 30 |
+
class Linear(nn.Linear):
|
| 31 |
+
def forward(self, input: torch.Tensor) -> torch.Tensor:
|
| 32 |
+
ret = input @ self.weight.T
|
| 33 |
+
if self.bias is None:
|
| 34 |
+
return ret
|
| 35 |
+
else:
|
| 36 |
+
return ret + self.bias
|
| 37 |
+
|
| 38 |
+
|
| 39 |
+
from einops import rearrange
|
| 40 |
+
|
| 41 |
+
# rotary pos emb helpers (torch.jit.script does not seem to support staticmethod...)
|
| 42 |
+
def rotate_half(x):
|
| 43 |
+
x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2 :]
|
| 44 |
+
return torch.cat((-x2, x1), dim=x1.ndim - 1) # dim=-1 triggers a bug in torch < 1.8.0
|
| 45 |
+
|
| 46 |
+
|
| 47 |
+
class RotaryEmbedding(torch.nn.Module):
|
| 48 |
+
"""Implementation of RotaryEmbedding from GPT-NeoX.
|
| 49 |
+
This implementation is design to operate on queries and keys that are compatible with
|
| 50 |
+
[batch_size, n_heads_per_partition, seq_len, head_dim] (e.g. MinGPTAttention format).
|
| 51 |
+
"""
|
| 52 |
+
|
| 53 |
+
def __init__(
|
| 54 |
+
self,
|
| 55 |
+
head_dim: int,
|
| 56 |
+
base=10000,
|
| 57 |
+
):
|
| 58 |
+
super().__init__()
|
| 59 |
+
inv_freq = 1.0 / (base ** (torch.arange(0, head_dim, 2).float() / head_dim))
|
| 60 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
| 61 |
+
self.head_dim = head_dim
|
| 62 |
+
self.seq_len_cached = None
|
| 63 |
+
self.batch_size_cached = None
|
| 64 |
+
self.cos_cached: torch.Tensor | None = None
|
| 65 |
+
self.sin_cached: torch.Tensor | None = None
|
| 66 |
+
|
| 67 |
+
def cos_sin(
|
| 68 |
+
self,
|
| 69 |
+
seq_len: int,
|
| 70 |
+
device="cuda",
|
| 71 |
+
dtype=torch.bfloat16,
|
| 72 |
+
) -> torch.Tensor:
|
| 73 |
+
if seq_len != self.seq_len_cached:
|
| 74 |
+
self.seq_len_cached = seq_len
|
| 75 |
+
t = torch.arange(seq_len, device=device).type_as(self.inv_freq)
|
| 76 |
+
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
|
| 77 |
+
emb = torch.cat((freqs, freqs), dim=-1).to(device)
|
| 78 |
+
|
| 79 |
+
if dtype in [torch.float16, torch.bfloat16]:
|
| 80 |
+
emb = emb.float()
|
| 81 |
+
|
| 82 |
+
self.cos_cached = emb.cos()[None, :, :]
|
| 83 |
+
self.sin_cached = emb.sin()[None, :, :]
|
| 84 |
+
|
| 85 |
+
self.cos_cached = self.cos_cached.type(dtype)
|
| 86 |
+
self.sin_cached = self.sin_cached.type(dtype)
|
| 87 |
+
|
| 88 |
+
return self.cos_cached, self.sin_cached
|
| 89 |
+
|
| 90 |
+
def forward(self, q, k):
|
| 91 |
+
batch, seq_len, head_dim = q.shape
|
| 92 |
+
cos, sin = self.cos_sin(seq_len, q.device, q.dtype)
|
| 93 |
+
return (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)
|
| 94 |
+
|
| 95 |
+
|
| 96 |
+
def _make_causal_mask(
|
| 97 |
+
input_ids_shape: torch.Size, device: torch.device, past_key_values_length: int
|
| 98 |
+
) -> torch.BoolTensor:
|
| 99 |
+
batch_size, target_length = input_ids_shape
|
| 100 |
+
mask = torch.empty((target_length, target_length + past_key_values_length), dtype=torch.bool, device=device)
|
| 101 |
+
# ONNX doesn't support `torch.Tensor.triu` properly, thus we use this workaround
|
| 102 |
+
seq_ids = torch.arange(target_length, device=device)
|
| 103 |
+
mask[:, past_key_values_length:] = seq_ids[:, None] < seq_ids[None, :]
|
| 104 |
+
|
| 105 |
+
if past_key_values_length > 0:
|
| 106 |
+
mask[:, :past_key_values_length] = False
|
| 107 |
+
|
| 108 |
+
expanded_mask = mask[None, None, :, :].expand(batch_size, 1, target_length, target_length + past_key_values_length)
|
| 109 |
+
return expanded_mask
|
| 110 |
+
|
| 111 |
+
|
| 112 |
+
def _expand_mask(mask: torch.Tensor, tgt_length: int) -> torch.BoolTensor:
|
| 113 |
+
batch_size, src_length = mask.shape
|
| 114 |
+
tgt_length = tgt_length if tgt_length is not None else src_length
|
| 115 |
+
|
| 116 |
+
expanded_mask = ~(mask[:, None, None, :].to(torch.bool))
|
| 117 |
+
return expanded_mask.expand(batch_size, 1, tgt_length, src_length)
|
| 118 |
+
|
| 119 |
+
|
| 120 |
+
def build_alibi_tensor(attention_mask: torch.Tensor, num_heads: int, dtype: torch.dtype) -> torch.Tensor:
|
| 121 |
+
batch_size, seq_length = attention_mask.shape
|
| 122 |
+
closest_power_of_2 = 2 ** math.floor(math.log2(num_heads))
|
| 123 |
+
base = torch.tensor(
|
| 124 |
+
2 ** (-(2 ** -(math.log2(closest_power_of_2) - 3))), device=attention_mask.device, dtype=torch.float32
|
| 125 |
+
)
|
| 126 |
+
powers = torch.arange(1, 1 + closest_power_of_2, device=attention_mask.device, dtype=torch.int32)
|
| 127 |
+
slopes = torch.pow(base, powers)
|
| 128 |
+
|
| 129 |
+
if closest_power_of_2 != num_heads:
|
| 130 |
+
extra_base = torch.tensor(
|
| 131 |
+
2 ** (-(2 ** -(math.log2(2 * closest_power_of_2) - 3))), device=attention_mask.device, dtype=torch.float32
|
| 132 |
+
)
|
| 133 |
+
num_remaining_heads = min(closest_power_of_2, num_heads - closest_power_of_2)
|
| 134 |
+
extra_powers = torch.arange(1, 1 + 2 * num_remaining_heads, 2, device=attention_mask.device, dtype=torch.int32)
|
| 135 |
+
slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0)
|
| 136 |
+
|
| 137 |
+
# Note: alibi will added to the attention bias that will be applied to the query, key product of attention
|
| 138 |
+
# => therefore alibi will have to be of shape (batch_size, num_heads, query_length, key_length)
|
| 139 |
+
# => here we set (batch_size=1, num_heads=num_heads, query_length=1, key_length=max_length)
|
| 140 |
+
# => the query_length dimension will then be broadcasted correctly
|
| 141 |
+
# This is more or less identical to T5's relative position bias:
|
| 142 |
+
# https://github.com/huggingface/transformers/blob/f681437203baa7671de3174b0fa583c349d9d5e1/src/transformers/models/t5/modeling_t5.py#L527
|
| 143 |
+
arange_tensor = ((attention_mask.cumsum(dim=-1) - 1) * attention_mask)[:, None, :]
|
| 144 |
+
alibi = slopes[..., None].bfloat16() * arange_tensor
|
| 145 |
+
return alibi.reshape(batch_size * num_heads, 1, seq_length).to(dtype)
|
| 146 |
+
|
| 147 |
+
|
| 148 |
+
def dropout_add(x: torch.Tensor, residual: torch.Tensor, prob: float, training: bool) -> torch.Tensor:
|
| 149 |
+
out = F.dropout(x, p=prob, training=training)
|
| 150 |
+
out = residual + out
|
| 151 |
+
return out
|
| 152 |
+
|
| 153 |
+
|
| 154 |
+
class Attention(nn.Module):
|
| 155 |
+
def __init__(self, config: RWConfig):
|
| 156 |
+
super().__init__()
|
| 157 |
+
|
| 158 |
+
self.hidden_size = config.hidden_size
|
| 159 |
+
self.num_heads = config.n_head
|
| 160 |
+
self.head_dim = self.hidden_size // self.num_heads
|
| 161 |
+
self.split_size = self.hidden_size
|
| 162 |
+
self.hidden_dropout = config.hidden_dropout
|
| 163 |
+
|
| 164 |
+
if self.head_dim * self.num_heads != self.hidden_size:
|
| 165 |
+
raise ValueError(
|
| 166 |
+
f"`hidden_size` must be divisible by num_heads (got `hidden_size`: {self.hidden_size} and `num_heads`:"
|
| 167 |
+
f" {self.num_heads})."
|
| 168 |
+
)
|
| 169 |
+
|
| 170 |
+
self.maybe_rotary = RotaryEmbedding(config.head_dim) if config.rotary else lambda q, k: (q, k)
|
| 171 |
+
|
| 172 |
+
# Layer-wise attention scaling
|
| 173 |
+
self.inv_norm_factor = 1.0 / math.sqrt(self.head_dim)
|
| 174 |
+
self.beta = self.inv_norm_factor
|
| 175 |
+
|
| 176 |
+
self.query_key_value = Linear(
|
| 177 |
+
self.hidden_size,
|
| 178 |
+
3 * self.hidden_size if not config.multi_query else (self.hidden_size + 2 * self.head_dim),
|
| 179 |
+
bias=config.bias,
|
| 180 |
+
)
|
| 181 |
+
self.multi_query = config.multi_query
|
| 182 |
+
self.dense = Linear(self.hidden_size, self.hidden_size, bias=config.bias)
|
| 183 |
+
self.attention_dropout = nn.Dropout(config.attention_dropout)
|
| 184 |
+
self.num_kv = config.n_head if not self.multi_query else 1
|
| 185 |
+
|
| 186 |
+
def _split_heads(self, fused_qkv: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
| 187 |
+
"""
|
| 188 |
+
Split the last dimension into (num_heads, head_dim) without making any copies, results share same memory
|
| 189 |
+
storage as `fused_qkv`
|
| 190 |
+
|
| 191 |
+
Args:
|
| 192 |
+
fused_qkv (`torch.tensor`, *required*): [batch_size, seq_length, num_heads * 3 * head_dim]
|
| 193 |
+
|
| 194 |
+
Returns:
|
| 195 |
+
query: [batch_size, seq_length, num_heads, head_dim] key: [batch_size, seq_length, num_heads, head_dim]
|
| 196 |
+
value: [batch_size, seq_length, num_heads, head_dim]
|
| 197 |
+
"""
|
| 198 |
+
if not self.multi_query:
|
| 199 |
+
batch_size, seq_length, three_times_hidden_size = fused_qkv.shape
|
| 200 |
+
fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads, 3, self.head_dim)
|
| 201 |
+
return fused_qkv[..., 0, :], fused_qkv[..., 1, :], fused_qkv[..., 2, :]
|
| 202 |
+
else:
|
| 203 |
+
batch_size, seq_length, three_times_hidden_size = fused_qkv.shape
|
| 204 |
+
fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads + 2, self.head_dim)
|
| 205 |
+
return fused_qkv[..., :-2, :], fused_qkv[..., [-2], :], fused_qkv[..., [-1], :]
|
| 206 |
+
|
| 207 |
+
def _merge_heads(self, x: torch.Tensor) -> torch.Tensor:
|
| 208 |
+
"""
|
| 209 |
+
Merge heads together over the last dimenstion
|
| 210 |
+
|
| 211 |
+
Args:
|
| 212 |
+
x: (`torch.tensor`, *required*): [batch_size * num_heads, seq_length, head_dim]
|
| 213 |
+
|
| 214 |
+
Returns:
|
| 215 |
+
torch.tensor: [batch_size, seq_length, num_heads * head_dim]
|
| 216 |
+
"""
|
| 217 |
+
# What we want to achieve is:
|
| 218 |
+
# batch_size * num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads * head_dim
|
| 219 |
+
batch_size_and_num_heads, seq_length, _ = x.shape
|
| 220 |
+
batch_size = batch_size_and_num_heads // self.num_heads
|
| 221 |
+
|
| 222 |
+
# First view to decompose the batch size
|
| 223 |
+
# batch_size * num_heads, seq_length, head_dim -> batch_size, num_heads, seq_length, head_dim
|
| 224 |
+
x = x.view(batch_size, self.num_heads, seq_length, self.head_dim)
|
| 225 |
+
|
| 226 |
+
# batch_size, num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads, head_dim
|
| 227 |
+
x = x.permute(0, 2, 1, 3)
|
| 228 |
+
|
| 229 |
+
# batch_size, seq_length, num_heads, head_dim -> batch_size, seq_length, num_heads * head_dim
|
| 230 |
+
return x.reshape(batch_size, seq_length, self.num_heads * self.head_dim)
|
| 231 |
+
|
| 232 |
+
def forward(
|
| 233 |
+
self,
|
| 234 |
+
hidden_states: torch.Tensor,
|
| 235 |
+
alibi: torch.Tensor,
|
| 236 |
+
attention_mask: torch.Tensor,
|
| 237 |
+
layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
|
| 238 |
+
head_mask: Optional[torch.Tensor] = None,
|
| 239 |
+
use_cache: bool = False,
|
| 240 |
+
output_attentions: bool = False,
|
| 241 |
+
):
|
| 242 |
+
fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size]
|
| 243 |
+
|
| 244 |
+
# 3 x [batch_size, seq_length, num_heads, head_dim]
|
| 245 |
+
(query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)
|
| 246 |
+
|
| 247 |
+
batch_size, q_length, _, _ = query_layer.shape
|
| 248 |
+
|
| 249 |
+
query_layer = query_layer.transpose(1, 2).reshape(batch_size * self.num_heads, q_length, self.head_dim)
|
| 250 |
+
key_layer = key_layer.transpose(1, 2).reshape(
|
| 251 |
+
batch_size * self.num_kv,
|
| 252 |
+
q_length,
|
| 253 |
+
self.head_dim,
|
| 254 |
+
)
|
| 255 |
+
value_layer = value_layer.transpose(1, 2).reshape(batch_size * self.num_kv, q_length, self.head_dim)
|
| 256 |
+
|
| 257 |
+
query_layer, key_layer = self.maybe_rotary(query_layer, key_layer)
|
| 258 |
+
|
| 259 |
+
if layer_past is not None:
|
| 260 |
+
past_key, past_value = layer_past
|
| 261 |
+
# concatenate along seq_length dimension:
|
| 262 |
+
# - key: [batch_size * self.num_heads, head_dim, kv_length]
|
| 263 |
+
# - value: [batch_size * self.num_heads, kv_length, head_dim]
|
| 264 |
+
key_layer = torch.cat((past_key, key_layer), dim=1)
|
| 265 |
+
value_layer = torch.cat((past_value, value_layer), dim=1)
|
| 266 |
+
|
| 267 |
+
_, kv_length, _ = key_layer.shape
|
| 268 |
+
|
| 269 |
+
if use_cache is True:
|
| 270 |
+
present = (key_layer, value_layer)
|
| 271 |
+
else:
|
| 272 |
+
present = None
|
| 273 |
+
|
| 274 |
+
if alibi is None:
|
| 275 |
+
query_layer_ = query_layer.reshape(batch_size, self.num_heads, -1, self.head_dim)
|
| 276 |
+
key_layer_ = key_layer.reshape(batch_size, self.num_kv, -1, self.head_dim)
|
| 277 |
+
value_layer_ = value_layer.reshape(batch_size, self.num_kv, -1, self.head_dim)
|
| 278 |
+
|
| 279 |
+
attn_output = F.scaled_dot_product_attention(
|
| 280 |
+
query_layer_, key_layer_, value_layer_, None, 0.0, is_causal=True
|
| 281 |
+
)
|
| 282 |
+
|
| 283 |
+
x = attn_output.view(batch_size, self.num_heads, q_length, self.head_dim)
|
| 284 |
+
x = x.permute(0, 2, 1, 3)
|
| 285 |
+
attn_output = x.reshape(batch_size, q_length, self.num_heads * self.head_dim)
|
| 286 |
+
|
| 287 |
+
output_tensor = self.dense(attn_output)
|
| 288 |
+
|
| 289 |
+
outputs = (output_tensor, present)
|
| 290 |
+
assert not output_attentions # not supported.
|
| 291 |
+
return outputs
|
| 292 |
+
else:
|
| 293 |
+
attention_mask_float = (attention_mask * 1.0).masked_fill(attention_mask, -1e9).to(torch.bfloat16)
|
| 294 |
+
matmul_result = query_layer @ key_layer.transpose(-1, -2)
|
| 295 |
+
|
| 296 |
+
# change view to [batch_size, num_heads, q_length, kv_length]
|
| 297 |
+
attention_scores = matmul_result.view(batch_size, self.num_heads, q_length, kv_length)
|
| 298 |
+
|
| 299 |
+
# cast attention scores to fp32, compute scaled softmax and cast back to initial dtype - [batch_size, num_heads, q_length, kv_length]
|
| 300 |
+
input_dtype = attention_scores.dtype
|
| 301 |
+
# `float16` has a minimum value of -65504.0, whereas `bfloat16` and `float32` have a minimum value of `-3.4e+38`
|
| 302 |
+
if input_dtype == torch.float16 or input_dtype == torch.bfloat16:
|
| 303 |
+
attention_scores = attention_scores.to(torch.float32)
|
| 304 |
+
# attn_weights = torch.masked_fill(attention_scores, attention_mask, torch.finfo(attention_scores.dtype).min)
|
| 305 |
+
attention_probs = F.softmax(
|
| 306 |
+
(attention_scores + alibi.view(batch_size, self.num_heads, 1, -1)) * self.inv_norm_factor + attention_mask_float,
|
| 307 |
+
dim=-1,
|
| 308 |
+
dtype=hidden_states.dtype,
|
| 309 |
+
)
|
| 310 |
+
# [batch_size, num_heads, q_length, kv_length]
|
| 311 |
+
attention_probs = self.attention_dropout(attention_probs)
|
| 312 |
+
|
| 313 |
+
if head_mask is not None:
|
| 314 |
+
attention_probs = attention_probs * head_mask
|
| 315 |
+
|
| 316 |
+
# change view [batch_size x num_heads, q_length, kv_length]
|
| 317 |
+
attention_probs_reshaped = attention_probs.view(batch_size * self.num_heads, q_length, kv_length)
|
| 318 |
+
|
| 319 |
+
# matmul: [batch_size * num_heads, q_length, head_dim]
|
| 320 |
+
context_layer = attention_probs_reshaped @ value_layer
|
| 321 |
+
|
| 322 |
+
# change view [batch_size, num_heads, q_length, head_dim]
|
| 323 |
+
context_layer = self._merge_heads(context_layer)
|
| 324 |
+
|
| 325 |
+
output_tensor = self.dense(context_layer)
|
| 326 |
+
|
| 327 |
+
outputs = (output_tensor, present)
|
| 328 |
+
if output_attentions:
|
| 329 |
+
outputs += (attention_probs,)
|
| 330 |
+
|
| 331 |
+
return outputs
|
| 332 |
+
|
| 333 |
+
|
| 334 |
+
class MLP(nn.Module):
|
| 335 |
+
def __init__(self, config: RWConfig):
|
| 336 |
+
super().__init__()
|
| 337 |
+
hidden_size = config.hidden_size
|
| 338 |
+
|
| 339 |
+
self.dense_h_to_4h = Linear(hidden_size, 4 * hidden_size, bias=config.bias)
|
| 340 |
+
self.act = nn.GELU()
|
| 341 |
+
self.dense_4h_to_h = Linear(4 * hidden_size, hidden_size, bias=config.bias)
|
| 342 |
+
self.hidden_dropout = config.hidden_dropout
|
| 343 |
+
|
| 344 |
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
| 345 |
+
x = self.act(self.dense_h_to_4h(x))
|
| 346 |
+
x = self.dense_4h_to_h(x)
|
| 347 |
+
return x
|
| 348 |
+
|
| 349 |
+
|
| 350 |
+
class DecoderLayer(nn.Module):
|
| 351 |
+
def __init__(self, config: RWConfig):
|
| 352 |
+
super().__init__()
|
| 353 |
+
hidden_size = config.hidden_size
|
| 354 |
+
|
| 355 |
+
self.input_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
|
| 356 |
+
self.num_heads = config.n_head
|
| 357 |
+
self.self_attention = Attention(config)
|
| 358 |
+
|
| 359 |
+
if not config.parallel_attn:
|
| 360 |
+
# unused if parallel attn
|
| 361 |
+
self.post_attention_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
|
| 362 |
+
|
| 363 |
+
self.mlp = MLP(config)
|
| 364 |
+
|
| 365 |
+
self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm
|
| 366 |
+
self.hidden_dropout = config.hidden_dropout
|
| 367 |
+
|
| 368 |
+
self.config = config
|
| 369 |
+
|
| 370 |
+
def forward(
|
| 371 |
+
self,
|
| 372 |
+
hidden_states: torch.Tensor,
|
| 373 |
+
alibi: torch.Tensor,
|
| 374 |
+
attention_mask: torch.Tensor,
|
| 375 |
+
layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
|
| 376 |
+
head_mask: Optional[torch.Tensor] = None,
|
| 377 |
+
use_cache: bool = False,
|
| 378 |
+
output_attentions: bool = False,
|
| 379 |
+
):
|
| 380 |
+
|
| 381 |
+
layernorm_output = self.input_layernorm(hidden_states)
|
| 382 |
+
residual = hidden_states
|
| 383 |
+
|
| 384 |
+
# Self attention.
|
| 385 |
+
attn_outputs = self.self_attention(
|
| 386 |
+
layernorm_output,
|
| 387 |
+
layer_past=layer_past,
|
| 388 |
+
attention_mask=attention_mask,
|
| 389 |
+
alibi=alibi,
|
| 390 |
+
head_mask=head_mask,
|
| 391 |
+
use_cache=use_cache,
|
| 392 |
+
output_attentions=output_attentions,
|
| 393 |
+
)
|
| 394 |
+
|
| 395 |
+
attention_output = attn_outputs[0]
|
| 396 |
+
|
| 397 |
+
if not self.config.parallel_attn:
|
| 398 |
+
residual = dropout_add(attention_output, residual, self.config.attention_dropout, training=self.training)
|
| 399 |
+
layernorm_output = self.post_attention_layernorm(residual)
|
| 400 |
+
|
| 401 |
+
outputs = attn_outputs[1:]
|
| 402 |
+
|
| 403 |
+
# MLP.
|
| 404 |
+
mlp_output = self.mlp(layernorm_output)
|
| 405 |
+
|
| 406 |
+
if self.config.parallel_attn:
|
| 407 |
+
mlp_output += attention_output
|
| 408 |
+
|
| 409 |
+
output = dropout_add(mlp_output, residual, self.config.hidden_dropout, training=self.training)
|
| 410 |
+
|
| 411 |
+
if use_cache:
|
| 412 |
+
outputs = (output,) + outputs
|
| 413 |
+
else:
|
| 414 |
+
outputs = (output,) + outputs[1:]
|
| 415 |
+
|
| 416 |
+
return outputs # hidden_states, present, attentions
|
| 417 |
+
|
| 418 |
+
|
| 419 |
+
class RWPreTrainedModel(PreTrainedModel):
|
| 420 |
+
_keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"]
|
| 421 |
+
"""
|
| 422 |
+
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
| 423 |
+
models.
|
| 424 |
+
"""
|
| 425 |
+
|
| 426 |
+
config_class = RWConfig
|
| 427 |
+
base_model_prefix = "transformer"
|
| 428 |
+
supports_gradient_checkpointing = True
|
| 429 |
+
_no_split_modules = ["DecoderLayer"]
|
| 430 |
+
|
| 431 |
+
def __init__(self, *inputs, **kwargs):
|
| 432 |
+
super().__init__(*inputs, **kwargs)
|
| 433 |
+
|
| 434 |
+
def _init_weights(self, module: nn.Module):
|
| 435 |
+
"""Initialize the weights."""
|
| 436 |
+
if isinstance(module, nn.Linear) or isinstance(module, Linear):
|
| 437 |
+
# Slightly different from the TF version which uses truncated_normal for initialization
|
| 438 |
+
# cf https://github.com/pytorch/pytorch/pull/5617
|
| 439 |
+
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
| 440 |
+
if module.bias is not None:
|
| 441 |
+
module.bias.data.zero_()
|
| 442 |
+
elif isinstance(module, nn.Embedding):
|
| 443 |
+
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
| 444 |
+
if module.padding_idx is not None:
|
| 445 |
+
module.weight.data[module.padding_idx].zero_()
|
| 446 |
+
elif isinstance(module, LayerNorm):
|
| 447 |
+
module.bias.data.zero_()
|
| 448 |
+
module.weight.data.fill_(1.0)
|
| 449 |
+
|
| 450 |
+
def _set_gradient_checkpointing(self, module: nn.Module, value: bool = False):
|
| 451 |
+
if isinstance(module, RWModel):
|
| 452 |
+
module.gradient_checkpointing = value
|
| 453 |
+
|
| 454 |
+
@staticmethod
|
| 455 |
+
def _convert_to_standard_cache(
|
| 456 |
+
past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]], batch_size: int
|
| 457 |
+
) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]:
|
| 458 |
+
"""
|
| 459 |
+
Standardizes the format of the cache so as to match most implementations, i.e. to tuple(tuple([batch_size,
|
| 460 |
+
num_heads, ...]))
|
| 461 |
+
"""
|
| 462 |
+
batch_size_times_num_heads, head_dim, seq_length = past_key_value[0][0].shape
|
| 463 |
+
num_heads = batch_size_times_num_heads // batch_size
|
| 464 |
+
# key: [batch_size * num_heads, head_dim, seq_length] -> [batch_size, num_heads, head_dim, seq_length]
|
| 465 |
+
# value: [batch_size * num_heads, seq_length, head_dim] -> [batch_size, num_heads, seq_length, head_dim]
|
| 466 |
+
return tuple(
|
| 467 |
+
(
|
| 468 |
+
layer_past[0].view(batch_size, num_heads, head_dim, seq_length),
|
| 469 |
+
layer_past[1].view(batch_size, num_heads, seq_length, head_dim),
|
| 470 |
+
)
|
| 471 |
+
for layer_past in past_key_value
|
| 472 |
+
)
|
| 473 |
+
|
| 474 |
+
@staticmethod
|
| 475 |
+
def _convert_to_rw_cache(
|
| 476 |
+
past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]]
|
| 477 |
+
) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]:
|
| 478 |
+
batch_size, num_heads, head_dim, seq_length = past_key_value[0][0].shape
|
| 479 |
+
batch_size_times_num_heads = batch_size * num_heads
|
| 480 |
+
# key: [batch_size, num_heads, head_dim, seq_length] -> [batch_size * num_heads, head_dim, seq_length]
|
| 481 |
+
# value: [batch_size, num_heads, seq_length, head_dim] -> [batch_size * num_heads, seq_length, head_dim]
|
| 482 |
+
return tuple(
|
| 483 |
+
(
|
| 484 |
+
layer_past[0].view(batch_size_times_num_heads, head_dim, seq_length),
|
| 485 |
+
layer_past[1].view(batch_size_times_num_heads, seq_length, head_dim),
|
| 486 |
+
)
|
| 487 |
+
for layer_past in past_key_value
|
| 488 |
+
)
|
| 489 |
+
|
| 490 |
+
|
| 491 |
+
class RWModel(RWPreTrainedModel):
|
| 492 |
+
def __init__(self, config: RWConfig):
|
| 493 |
+
super().__init__(config)
|
| 494 |
+
|
| 495 |
+
self.embed_dim = config.hidden_size
|
| 496 |
+
self.num_heads = config.n_head
|
| 497 |
+
self.alibi = config.alibi
|
| 498 |
+
|
| 499 |
+
# Embedding + LN Embedding
|
| 500 |
+
self.word_embeddings = nn.Embedding(config.vocab_size, self.embed_dim)
|
| 501 |
+
|
| 502 |
+
# Transformer blocks
|
| 503 |
+
self.h = nn.ModuleList([DecoderLayer(config) for _ in range(config.num_hidden_layers)])
|
| 504 |
+
|
| 505 |
+
# Final Layer Norm
|
| 506 |
+
self.ln_f = LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
|
| 507 |
+
|
| 508 |
+
self.gradient_checkpointing = False
|
| 509 |
+
|
| 510 |
+
# Initialize weights and apply final processing
|
| 511 |
+
self.post_init()
|
| 512 |
+
|
| 513 |
+
def get_input_embeddings(self):
|
| 514 |
+
return self.word_embeddings
|
| 515 |
+
|
| 516 |
+
def _prepare_attn_mask(
|
| 517 |
+
self, attention_mask: torch.Tensor, input_shape: Tuple[int, int], past_key_values_length: int
|
| 518 |
+
) -> torch.BoolTensor:
|
| 519 |
+
# create causal mask
|
| 520 |
+
# [batch_size, seq_length] -> [batch_size, 1, tgt_length, src_length]
|
| 521 |
+
combined_attention_mask = None
|
| 522 |
+
device = attention_mask.device
|
| 523 |
+
_, src_length = input_shape
|
| 524 |
+
|
| 525 |
+
if src_length > 1:
|
| 526 |
+
combined_attention_mask = _make_causal_mask(
|
| 527 |
+
input_shape, device=device, past_key_values_length=past_key_values_length
|
| 528 |
+
)
|
| 529 |
+
|
| 530 |
+
# [batch_size, seq_length] -> [batch_size, 1, tgt_length, src_length]
|
| 531 |
+
expanded_attn_mask = _expand_mask(attention_mask, tgt_length=src_length)
|
| 532 |
+
combined_attention_mask = (
|
| 533 |
+
expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask | combined_attention_mask
|
| 534 |
+
)
|
| 535 |
+
|
| 536 |
+
return combined_attention_mask
|
| 537 |
+
|
| 538 |
+
def set_input_embeddings(self, new_embeddings: torch.Tensor):
|
| 539 |
+
self.word_embeddings = new_embeddings
|
| 540 |
+
|
| 541 |
+
def forward(
|
| 542 |
+
self,
|
| 543 |
+
input_ids: Optional[torch.LongTensor] = None,
|
| 544 |
+
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
|
| 545 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 546 |
+
head_mask: Optional[torch.LongTensor] = None,
|
| 547 |
+
inputs_embeds: Optional[torch.LongTensor] = None,
|
| 548 |
+
use_cache: Optional[bool] = None,
|
| 549 |
+
output_attentions: Optional[bool] = None,
|
| 550 |
+
output_hidden_states: Optional[bool] = None,
|
| 551 |
+
return_dict: Optional[bool] = None,
|
| 552 |
+
**deprecated_arguments,
|
| 553 |
+
) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]:
|
| 554 |
+
if deprecated_arguments.pop("position_ids", False) is not False:
|
| 555 |
+
# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
|
| 556 |
+
warnings.warn(
|
| 557 |
+
"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
|
| 558 |
+
" passing `position_ids`.",
|
| 559 |
+
FutureWarning,
|
| 560 |
+
)
|
| 561 |
+
if len(deprecated_arguments) > 0:
|
| 562 |
+
raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
|
| 563 |
+
|
| 564 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
| 565 |
+
output_hidden_states = (
|
| 566 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
| 567 |
+
)
|
| 568 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
| 569 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
| 570 |
+
|
| 571 |
+
if input_ids is not None and inputs_embeds is not None:
|
| 572 |
+
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
| 573 |
+
elif input_ids is not None:
|
| 574 |
+
batch_size, seq_length = input_ids.shape
|
| 575 |
+
elif inputs_embeds is not None:
|
| 576 |
+
batch_size, seq_length, _ = inputs_embeds.shape
|
| 577 |
+
else:
|
| 578 |
+
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
| 579 |
+
|
| 580 |
+
if past_key_values is None:
|
| 581 |
+
past_key_values = tuple([None] * len(self.h))
|
| 582 |
+
|
| 583 |
+
# Prepare head mask if needed
|
| 584 |
+
# 1.0 in head_mask indicate we keep the head
|
| 585 |
+
# attention_probs has shape batch_size x num_heads x N x N
|
| 586 |
+
# head_mask has shape n_layer x batch x num_heads x N x N
|
| 587 |
+
head_mask = self.get_head_mask(head_mask, self.config.n_layer)
|
| 588 |
+
|
| 589 |
+
if inputs_embeds is None:
|
| 590 |
+
inputs_embeds = self.word_embeddings(input_ids)
|
| 591 |
+
|
| 592 |
+
hidden_states = inputs_embeds
|
| 593 |
+
|
| 594 |
+
presents = () if use_cache else None
|
| 595 |
+
all_self_attentions = () if output_attentions else None
|
| 596 |
+
all_hidden_states = () if output_hidden_states else None
|
| 597 |
+
|
| 598 |
+
# Compute alibi tensor: check build_alibi_tensor documentation
|
| 599 |
+
seq_length_with_past = seq_length
|
| 600 |
+
past_key_values_length = 0
|
| 601 |
+
if past_key_values[0] is not None:
|
| 602 |
+
past_key_values_length = past_key_values[0][0].shape[2]
|
| 603 |
+
seq_length_with_past = seq_length_with_past + past_key_values_length
|
| 604 |
+
if attention_mask is None:
|
| 605 |
+
attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)
|
| 606 |
+
else:
|
| 607 |
+
attention_mask = attention_mask.to(hidden_states.device)
|
| 608 |
+
|
| 609 |
+
if self.alibi:
|
| 610 |
+
alibi = build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype)
|
| 611 |
+
else:
|
| 612 |
+
alibi = None
|
| 613 |
+
|
| 614 |
+
causal_mask = self._prepare_attn_mask(
|
| 615 |
+
attention_mask,
|
| 616 |
+
input_shape=(batch_size, seq_length),
|
| 617 |
+
past_key_values_length=past_key_values_length,
|
| 618 |
+
)
|
| 619 |
+
|
| 620 |
+
for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
|
| 621 |
+
|
| 622 |
+
if output_hidden_states:
|
| 623 |
+
all_hidden_states = all_hidden_states + (hidden_states,)
|
| 624 |
+
|
| 625 |
+
if self.gradient_checkpointing and self.training:
|
| 626 |
+
|
| 627 |
+
if use_cache:
|
| 628 |
+
logger.warning(
|
| 629 |
+
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
| 630 |
+
)
|
| 631 |
+
use_cache = False
|
| 632 |
+
|
| 633 |
+
def create_custom_forward(module):
|
| 634 |
+
def custom_forward(*inputs):
|
| 635 |
+
# None for past_key_value
|
| 636 |
+
return module(*inputs, use_cache=use_cache, output_attentions=output_attentions)
|
| 637 |
+
|
| 638 |
+
return custom_forward
|
| 639 |
+
|
| 640 |
+
outputs = torch.utils.checkpoint.checkpoint(
|
| 641 |
+
create_custom_forward(block),
|
| 642 |
+
hidden_states,
|
| 643 |
+
alibi,
|
| 644 |
+
causal_mask,
|
| 645 |
+
head_mask[i],
|
| 646 |
+
)
|
| 647 |
+
else:
|
| 648 |
+
outputs = block(
|
| 649 |
+
hidden_states,
|
| 650 |
+
layer_past=layer_past,
|
| 651 |
+
attention_mask=causal_mask,
|
| 652 |
+
head_mask=head_mask[i],
|
| 653 |
+
use_cache=use_cache,
|
| 654 |
+
output_attentions=output_attentions,
|
| 655 |
+
alibi=alibi,
|
| 656 |
+
)
|
| 657 |
+
|
| 658 |
+
hidden_states = outputs[0]
|
| 659 |
+
if use_cache is True:
|
| 660 |
+
presents = presents + (outputs[1],)
|
| 661 |
+
|
| 662 |
+
if output_attentions:
|
| 663 |
+
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
|
| 664 |
+
|
| 665 |
+
# Add last hidden state
|
| 666 |
+
hidden_states = self.ln_f(hidden_states)
|
| 667 |
+
|
| 668 |
+
if output_hidden_states:
|
| 669 |
+
all_hidden_states = all_hidden_states + (hidden_states,)
|
| 670 |
+
|
| 671 |
+
if not return_dict:
|
| 672 |
+
return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
|
| 673 |
+
|
| 674 |
+
return BaseModelOutputWithPastAndCrossAttentions(
|
| 675 |
+
last_hidden_state=hidden_states,
|
| 676 |
+
past_key_values=presents,
|
| 677 |
+
hidden_states=all_hidden_states,
|
| 678 |
+
attentions=all_self_attentions,
|
| 679 |
+
)
|
| 680 |
+
|
| 681 |
+
|
| 682 |
+
class RWForCausalLM(RWPreTrainedModel):
|
| 683 |
+
_keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"]
|
| 684 |
+
|
| 685 |
+
def __init__(self, config: RWConfig):
|
| 686 |
+
super().__init__(config)
|
| 687 |
+
self.transformer = RWModel(config)
|
| 688 |
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
| 689 |
+
|
| 690 |
+
# Initialize weights and apply final processing
|
| 691 |
+
self.post_init()
|
| 692 |
+
|
| 693 |
+
def get_output_embeddings(self):
|
| 694 |
+
return self.lm_head
|
| 695 |
+
|
| 696 |
+
def set_output_embeddings(self, new_embeddings: torch.Tensor):
|
| 697 |
+
self.lm_head = new_embeddings
|
| 698 |
+
|
| 699 |
+
def prepare_inputs_for_generation(
|
| 700 |
+
self,
|
| 701 |
+
input_ids: torch.LongTensor,
|
| 702 |
+
past: Optional[torch.Tensor] = None,
|
| 703 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 704 |
+
**kwargs,
|
| 705 |
+
) -> dict:
|
| 706 |
+
# only last token for input_ids if past is not None
|
| 707 |
+
if past:
|
| 708 |
+
input_ids = input_ids[:, -1].unsqueeze(-1)
|
| 709 |
+
|
| 710 |
+
# the cache may be in the stardard format (e.g. in contrastive search), convert to our's format if needed
|
| 711 |
+
if past[0][0].shape[0] == input_ids.shape[0]:
|
| 712 |
+
past = self._convert_to_rw_cache(past)
|
| 713 |
+
|
| 714 |
+
return {
|
| 715 |
+
"input_ids": input_ids,
|
| 716 |
+
"past_key_values": past,
|
| 717 |
+
"use_cache": kwargs.get("use_cache"),
|
| 718 |
+
"attention_mask": attention_mask,
|
| 719 |
+
}
|
| 720 |
+
|
| 721 |
+
def forward(
|
| 722 |
+
self,
|
| 723 |
+
input_ids: Optional[torch.LongTensor] = None,
|
| 724 |
+
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
|
| 725 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 726 |
+
head_mask: Optional[torch.Tensor] = None,
|
| 727 |
+
inputs_embeds: Optional[torch.Tensor] = None,
|
| 728 |
+
labels: Optional[torch.Tensor] = None,
|
| 729 |
+
use_cache: Optional[bool] = None,
|
| 730 |
+
output_attentions: Optional[bool] = None,
|
| 731 |
+
output_hidden_states: Optional[bool] = None,
|
| 732 |
+
return_dict: Optional[bool] = None,
|
| 733 |
+
**deprecated_arguments,
|
| 734 |
+
) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
|
| 735 |
+
r"""
|
| 736 |
+
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
| 737 |
+
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
|
| 738 |
+
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
|
| 739 |
+
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
|
| 740 |
+
"""
|
| 741 |
+
if deprecated_arguments.pop("position_ids", False) is not False:
|
| 742 |
+
# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
|
| 743 |
+
warnings.warn(
|
| 744 |
+
"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
|
| 745 |
+
" passing `position_ids`.",
|
| 746 |
+
FutureWarning,
|
| 747 |
+
)
|
| 748 |
+
if len(deprecated_arguments) > 0:
|
| 749 |
+
raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
|
| 750 |
+
|
| 751 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
| 752 |
+
|
| 753 |
+
transformer_outputs = self.transformer(
|
| 754 |
+
input_ids,
|
| 755 |
+
past_key_values=past_key_values,
|
| 756 |
+
attention_mask=attention_mask,
|
| 757 |
+
head_mask=head_mask,
|
| 758 |
+
inputs_embeds=inputs_embeds,
|
| 759 |
+
use_cache=use_cache,
|
| 760 |
+
output_attentions=output_attentions,
|
| 761 |
+
output_hidden_states=output_hidden_states,
|
| 762 |
+
return_dict=return_dict,
|
| 763 |
+
)
|
| 764 |
+
hidden_states = transformer_outputs[0]
|
| 765 |
+
|
| 766 |
+
lm_logits = self.lm_head(hidden_states)
|
| 767 |
+
|
| 768 |
+
loss = None
|
| 769 |
+
if labels is not None:
|
| 770 |
+
# Shift so that tokens < n predict n
|
| 771 |
+
shift_logits = lm_logits[..., :-1, :].contiguous()
|
| 772 |
+
shift_labels = labels[..., 1:].contiguous()
|
| 773 |
+
batch_size, seq_length, vocab_size = shift_logits.shape
|
| 774 |
+
# Flatten the tokens
|
| 775 |
+
loss_fct = CrossEntropyLoss()
|
| 776 |
+
loss = loss_fct(
|
| 777 |
+
shift_logits.view(batch_size * seq_length, vocab_size), shift_labels.view(batch_size * seq_length)
|
| 778 |
+
)
|
| 779 |
+
|
| 780 |
+
if not return_dict:
|
| 781 |
+
output = (lm_logits,) + transformer_outputs[1:]
|
| 782 |
+
return ((loss,) + output) if loss is not None else output
|
| 783 |
+
|
| 784 |
+
return CausalLMOutputWithCrossAttentions(
|
| 785 |
+
loss=loss,
|
| 786 |
+
logits=lm_logits,
|
| 787 |
+
past_key_values=transformer_outputs.past_key_values,
|
| 788 |
+
hidden_states=transformer_outputs.hidden_states,
|
| 789 |
+
attentions=transformer_outputs.attentions,
|
| 790 |
+
)
|
| 791 |
+
|
| 792 |
+
def _reorder_cache(
|
| 793 |
+
self, past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor
|
| 794 |
+
) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]:
|
| 795 |
+
"""
|
| 796 |
+
This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
|
| 797 |
+
[`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
|
| 798 |
+
beam_idx at every generation step.
|
| 799 |
+
|
| 800 |
+
Output shares the same memory storage as `past`.
|
| 801 |
+
"""
|
| 802 |
+
standardized_past = self._convert_to_standard_cache(past, batch_size=len(beam_idx))
|
| 803 |
+
|
| 804 |
+
# Get a copy of `beam_idx` on all the devices where we need those indices.
|
| 805 |
+
device_to_beam_idx = {
|
| 806 |
+
past_state.device: beam_idx.to(past_state.device) for layer_past in past for past_state in layer_past
|
| 807 |
+
}
|
| 808 |
+
reordered_past = tuple(
|
| 809 |
+
(
|
| 810 |
+
layer_past[0].index_select(0, device_to_beam_idx[layer_past[0].device]),
|
| 811 |
+
layer_past[1].index_select(0, device_to_beam_idx[layer_past[0].device]),
|
| 812 |
+
)
|
| 813 |
+
for layer_past in standardized_past
|
| 814 |
+
)
|
| 815 |
+
return self._convert_to_rw_cache(reordered_past)
|
| 816 |
+
|
| 817 |
+
|
| 818 |
+
class RWForSequenceClassification(RWPreTrainedModel):
|
| 819 |
+
_keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"]
|
| 820 |
+
|
| 821 |
+
def __init__(self, config: RWConfig):
|
| 822 |
+
super().__init__(config)
|
| 823 |
+
self.num_labels = config.num_labels
|
| 824 |
+
self.transformer = RWModel(config)
|
| 825 |
+
self.score = nn.Linear(config.hidden_size, config.num_labels, bias=False)
|
| 826 |
+
|
| 827 |
+
# Initialize weights and apply final processing
|
| 828 |
+
self.post_init()
|
| 829 |
+
|
| 830 |
+
def forward(
|
| 831 |
+
self,
|
| 832 |
+
input_ids: Optional[torch.LongTensor] = None,
|
| 833 |
+
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
|
| 834 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 835 |
+
head_mask: Optional[torch.Tensor] = None,
|
| 836 |
+
inputs_embeds: Optional[torch.Tensor] = None,
|
| 837 |
+
labels: Optional[torch.Tensor] = None,
|
| 838 |
+
use_cache: Optional[bool] = None,
|
| 839 |
+
output_attentions: Optional[bool] = None,
|
| 840 |
+
output_hidden_states: Optional[bool] = None,
|
| 841 |
+
return_dict: Optional[bool] = None,
|
| 842 |
+
**deprecated_arguments,
|
| 843 |
+
) -> Union[Tuple[torch.Tensor], SequenceClassifierOutputWithPast]:
|
| 844 |
+
r"""
|
| 845 |
+
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
| 846 |
+
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
| 847 |
+
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
| 848 |
+
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
| 849 |
+
"""
|
| 850 |
+
if deprecated_arguments.pop("position_ids", False) is not False:
|
| 851 |
+
# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
|
| 852 |
+
warnings.warn(
|
| 853 |
+
"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
|
| 854 |
+
" passing `position_ids`.",
|
| 855 |
+
FutureWarning,
|
| 856 |
+
)
|
| 857 |
+
if len(deprecated_arguments) > 0:
|
| 858 |
+
raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
|
| 859 |
+
|
| 860 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
| 861 |
+
|
| 862 |
+
transformer_outputs = self.transformer(
|
| 863 |
+
input_ids,
|
| 864 |
+
past_key_values=past_key_values,
|
| 865 |
+
attention_mask=attention_mask,
|
| 866 |
+
head_mask=head_mask,
|
| 867 |
+
inputs_embeds=inputs_embeds,
|
| 868 |
+
use_cache=use_cache,
|
| 869 |
+
output_attentions=output_attentions,
|
| 870 |
+
output_hidden_states=output_hidden_states,
|
| 871 |
+
return_dict=return_dict,
|
| 872 |
+
)
|
| 873 |
+
|
| 874 |
+
hidden_states = transformer_outputs[0]
|
| 875 |
+
logits = self.score(hidden_states)
|
| 876 |
+
|
| 877 |
+
if input_ids is not None:
|
| 878 |
+
batch_size = input_ids.shape[0]
|
| 879 |
+
else:
|
| 880 |
+
batch_size = inputs_embeds.shape[0]
|
| 881 |
+
|
| 882 |
+
if self.config.pad_token_id is None and batch_size != 1:
|
| 883 |
+
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
|
| 884 |
+
if self.config.pad_token_id is None:
|
| 885 |
+
sequence_lengths = -1
|
| 886 |
+
else:
|
| 887 |
+
if input_ids is not None:
|
| 888 |
+
sequence_lengths = torch.ne(input_ids, self.config.pad_token_id).sum(dim=-1) - 1
|
| 889 |
+
else:
|
| 890 |
+
sequence_lengths = -1
|
| 891 |
+
logger.warning(
|
| 892 |
+
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
|
| 893 |
+
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
|
| 894 |
+
)
|
| 895 |
+
|
| 896 |
+
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
|
| 897 |
+
|
| 898 |
+
loss = None
|
| 899 |
+
if labels is not None:
|
| 900 |
+
if self.config.problem_type is None:
|
| 901 |
+
if self.num_labels == 1:
|
| 902 |
+
self.config.problem_type = "regression"
|
| 903 |
+
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
| 904 |
+
self.config.problem_type = "single_label_classification"
|
| 905 |
+
else:
|
| 906 |
+
self.config.problem_type = "multi_label_classification"
|
| 907 |
+
|
| 908 |
+
if self.config.problem_type == "regression":
|
| 909 |
+
loss_fct = MSELoss()
|
| 910 |
+
if self.num_labels == 1:
|
| 911 |
+
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
|
| 912 |
+
else:
|
| 913 |
+
loss = loss_fct(pooled_logits, labels)
|
| 914 |
+
elif self.config.problem_type == "single_label_classification":
|
| 915 |
+
loss_fct = CrossEntropyLoss()
|
| 916 |
+
loss = loss_fct(pooled_logits, labels)
|
| 917 |
+
elif self.config.problem_type == "multi_label_classification":
|
| 918 |
+
loss_fct = BCEWithLogitsLoss()
|
| 919 |
+
loss = loss_fct(pooled_logits, labels)
|
| 920 |
+
if not return_dict:
|
| 921 |
+
output = (pooled_logits,) + transformer_outputs[1:]
|
| 922 |
+
return ((loss,) + output) if loss is not None else output
|
| 923 |
+
|
| 924 |
+
return SequenceClassifierOutputWithPast(
|
| 925 |
+
loss=loss,
|
| 926 |
+
logits=pooled_logits,
|
| 927 |
+
past_key_values=transformer_outputs.past_key_values,
|
| 928 |
+
hidden_states=transformer_outputs.hidden_states,
|
| 929 |
+
attentions=transformer_outputs.attentions,
|
| 930 |
+
)
|
| 931 |
+
|
| 932 |
+
|
| 933 |
+
class RWForTokenClassification(RWPreTrainedModel):
|
| 934 |
+
_keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"]
|
| 935 |
+
|
| 936 |
+
def __init__(self, config: RWConfig):
|
| 937 |
+
super().__init__(config)
|
| 938 |
+
self.num_labels = config.num_labels
|
| 939 |
+
|
| 940 |
+
self.transformer = RWModel(config)
|
| 941 |
+
if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None:
|
| 942 |
+
classifier_dropout = config.classifier_dropout
|
| 943 |
+
elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None:
|
| 944 |
+
classifier_dropout = config.hidden_dropout
|
| 945 |
+
else:
|
| 946 |
+
classifier_dropout = 0.1
|
| 947 |
+
self.dropout = nn.Dropout(classifier_dropout)
|
| 948 |
+
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
| 949 |
+
|
| 950 |
+
# Initialize weights and apply final processing
|
| 951 |
+
self.post_init()
|
| 952 |
+
|
| 953 |
+
def forward(
|
| 954 |
+
self,
|
| 955 |
+
input_ids: Optional[torch.LongTensor] = None,
|
| 956 |
+
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
|
| 957 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 958 |
+
head_mask: Optional[torch.Tensor] = None,
|
| 959 |
+
inputs_embeds: Optional[torch.Tensor] = None,
|
| 960 |
+
labels: Optional[torch.Tensor] = None,
|
| 961 |
+
use_cache: Optional[bool] = None,
|
| 962 |
+
output_attentions: Optional[bool] = None,
|
| 963 |
+
output_hidden_states: Optional[bool] = None,
|
| 964 |
+
return_dict: Optional[bool] = None,
|
| 965 |
+
**deprecated_arguments,
|
| 966 |
+
) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
|
| 967 |
+
r"""
|
| 968 |
+
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
| 969 |
+
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
| 970 |
+
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
| 971 |
+
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
| 972 |
+
"""
|
| 973 |
+
if deprecated_arguments.pop("position_ids", False) is not False:
|
| 974 |
+
# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
|
| 975 |
+
warnings.warn(
|
| 976 |
+
"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
|
| 977 |
+
" passing `position_ids`.",
|
| 978 |
+
FutureWarning,
|
| 979 |
+
)
|
| 980 |
+
if len(deprecated_arguments) > 0:
|
| 981 |
+
raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
|
| 982 |
+
|
| 983 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
| 984 |
+
|
| 985 |
+
transformer_outputs = self.transformer(
|
| 986 |
+
input_ids,
|
| 987 |
+
past_key_values=past_key_values,
|
| 988 |
+
attention_mask=attention_mask,
|
| 989 |
+
head_mask=head_mask,
|
| 990 |
+
inputs_embeds=inputs_embeds,
|
| 991 |
+
use_cache=use_cache,
|
| 992 |
+
output_attentions=output_attentions,
|
| 993 |
+
output_hidden_states=output_hidden_states,
|
| 994 |
+
return_dict=return_dict,
|
| 995 |
+
)
|
| 996 |
+
|
| 997 |
+
hidden_states = transformer_outputs[0]
|
| 998 |
+
hidden_states = self.dropout(hidden_states)
|
| 999 |
+
logits = self.classifier(hidden_states)
|
| 1000 |
+
|
| 1001 |
+
loss = None
|
| 1002 |
+
if labels is not None:
|
| 1003 |
+
batch_size, seq_length = labels.shape
|
| 1004 |
+
loss_fct = CrossEntropyLoss()
|
| 1005 |
+
loss = loss_fct(logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length))
|
| 1006 |
+
|
| 1007 |
+
if not return_dict:
|
| 1008 |
+
output = (logits,) + transformer_outputs[2:]
|
| 1009 |
+
return ((loss,) + output) if loss is not None else output
|
| 1010 |
+
|
| 1011 |
+
return TokenClassifierOutput(
|
| 1012 |
+
loss=loss,
|
| 1013 |
+
logits=logits,
|
| 1014 |
+
hidden_states=transformer_outputs.hidden_states,
|
| 1015 |
+
attentions=transformer_outputs.attentions,
|
| 1016 |
+
)
|
| 1017 |
+
|
| 1018 |
+
|
| 1019 |
+
class RWForQuestionAnswering(RWPreTrainedModel):
|
| 1020 |
+
_keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"]
|
| 1021 |
+
|
| 1022 |
+
def __init__(self, config):
|
| 1023 |
+
super().__init__(config)
|
| 1024 |
+
self.transformer = RWModel(config)
|
| 1025 |
+
self.qa_outputs = nn.Linear(config.hidden_size, 2)
|
| 1026 |
+
|
| 1027 |
+
# Initialize weights and apply final processing
|
| 1028 |
+
self.post_init()
|
| 1029 |
+
|
| 1030 |
+
def forward(
|
| 1031 |
+
self,
|
| 1032 |
+
input_ids: Optional[torch.LongTensor] = None,
|
| 1033 |
+
attention_mask: Optional[torch.FloatTensor] = None,
|
| 1034 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 1035 |
+
head_mask: Optional[torch.FloatTensor] = None,
|
| 1036 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 1037 |
+
start_positions: Optional[torch.LongTensor] = None,
|
| 1038 |
+
end_positions: Optional[torch.LongTensor] = None,
|
| 1039 |
+
output_attentions: Optional[bool] = None,
|
| 1040 |
+
output_hidden_states: Optional[bool] = None,
|
| 1041 |
+
return_dict: Optional[bool] = None,
|
| 1042 |
+
) -> Union[Tuple, QuestionAnsweringModelOutput]:
|
| 1043 |
+
r"""
|
| 1044 |
+
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
| 1045 |
+
Labels for position (index) of the start of the labelled span for computing the token classification loss.
|
| 1046 |
+
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
| 1047 |
+
are not taken into account for computing the loss.
|
| 1048 |
+
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
| 1049 |
+
Labels for position (index) of the end of the labelled span for computing the token classification loss.
|
| 1050 |
+
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
| 1051 |
+
are not taken into account for computing the loss.
|
| 1052 |
+
"""
|
| 1053 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
| 1054 |
+
|
| 1055 |
+
outputs = self.transformer(
|
| 1056 |
+
input_ids,
|
| 1057 |
+
attention_mask=attention_mask,
|
| 1058 |
+
position_ids=position_ids,
|
| 1059 |
+
head_mask=head_mask,
|
| 1060 |
+
inputs_embeds=inputs_embeds,
|
| 1061 |
+
output_attentions=output_attentions,
|
| 1062 |
+
output_hidden_states=output_hidden_states,
|
| 1063 |
+
return_dict=return_dict,
|
| 1064 |
+
)
|
| 1065 |
+
|
| 1066 |
+
sequence_output = outputs[0]
|
| 1067 |
+
|
| 1068 |
+
logits = self.qa_outputs(sequence_output)
|
| 1069 |
+
start_logits, end_logits = logits.split(1, dim=-1)
|
| 1070 |
+
start_logits = start_logits.squeeze(-1).contiguous()
|
| 1071 |
+
end_logits = end_logits.squeeze(-1).contiguous()
|
| 1072 |
+
|
| 1073 |
+
total_loss = None
|
| 1074 |
+
if start_positions is not None and end_positions is not None:
|
| 1075 |
+
# If we are on multi-GPU, split add a dimension
|
| 1076 |
+
if len(start_positions.size()) > 1:
|
| 1077 |
+
start_positions = start_positions.squeeze(-1)
|
| 1078 |
+
if len(end_positions.size()) > 1:
|
| 1079 |
+
end_positions = end_positions.squeeze(-1)
|
| 1080 |
+
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
| 1081 |
+
ignored_index = start_logits.size(1)
|
| 1082 |
+
start_positions = start_positions.clamp(0, ignored_index)
|
| 1083 |
+
end_positions = end_positions.clamp(0, ignored_index)
|
| 1084 |
+
|
| 1085 |
+
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
|
| 1086 |
+
start_loss = loss_fct(start_logits, start_positions)
|
| 1087 |
+
end_loss = loss_fct(end_logits, end_positions)
|
| 1088 |
+
total_loss = (start_loss + end_loss) / 2
|
| 1089 |
+
|
| 1090 |
+
if not return_dict:
|
| 1091 |
+
output = (start_logits, end_logits) + outputs[2:]
|
| 1092 |
+
return ((total_loss,) + output) if total_loss is not None else output
|
| 1093 |
+
|
| 1094 |
+
return QuestionAnsweringModelOutput(
|
| 1095 |
+
loss=total_loss,
|
| 1096 |
+
start_logits=start_logits,
|
| 1097 |
+
end_logits=end_logits,
|
| 1098 |
+
hidden_states=outputs.hidden_states,
|
| 1099 |
+
attentions=outputs.attentions,
|
| 1100 |
+
)
|
pytorch_model.bin
ADDED
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| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:bc6957902d07d7a210613c1caa9f18f809699ee4cd0c31de0de5be119efeee39
|
| 3 |
+
size 13709266611
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special_tokens_map.json
ADDED
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| 1 |
+
{
|
| 2 |
+
"bos_token": {
|
| 3 |
+
"content": "<|endoftext|>",
|
| 4 |
+
"lstrip": false,
|
| 5 |
+
"normalized": true,
|
| 6 |
+
"rstrip": false,
|
| 7 |
+
"single_word": false
|
| 8 |
+
},
|
| 9 |
+
"eos_token": {
|
| 10 |
+
"content": "<|endoftext|>",
|
| 11 |
+
"lstrip": false,
|
| 12 |
+
"normalized": true,
|
| 13 |
+
"rstrip": false,
|
| 14 |
+
"single_word": false
|
| 15 |
+
},
|
| 16 |
+
"pad_token": "<|endoftext|>",
|
| 17 |
+
"unk_token": {
|
| 18 |
+
"content": "<|endoftext|>",
|
| 19 |
+
"lstrip": false,
|
| 20 |
+
"normalized": true,
|
| 21 |
+
"rstrip": false,
|
| 22 |
+
"single_word": false
|
| 23 |
+
}
|
| 24 |
+
}
|
tokenizer.json
ADDED
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tokenizer_config.json
ADDED
|
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|
| 1 |
+
{
|
| 2 |
+
"add_bos_token": false,
|
| 3 |
+
"add_prefix_space": false,
|
| 4 |
+
"bos_token": {
|
| 5 |
+
"__type": "AddedToken",
|
| 6 |
+
"content": "<|endoftext|>",
|
| 7 |
+
"lstrip": false,
|
| 8 |
+
"normalized": true,
|
| 9 |
+
"rstrip": false,
|
| 10 |
+
"single_word": false
|
| 11 |
+
},
|
| 12 |
+
"clean_up_tokenization_spaces": true,
|
| 13 |
+
"eos_token": {
|
| 14 |
+
"__type": "AddedToken",
|
| 15 |
+
"content": "<|endoftext|>",
|
| 16 |
+
"lstrip": false,
|
| 17 |
+
"normalized": true,
|
| 18 |
+
"rstrip": false,
|
| 19 |
+
"single_word": false
|
| 20 |
+
},
|
| 21 |
+
"errors": "replace",
|
| 22 |
+
"model_max_length": 2048,
|
| 23 |
+
"pad_token": null,
|
| 24 |
+
"tokenizer_class": "GPT2Tokenizer",
|
| 25 |
+
"unk_token": {
|
| 26 |
+
"__type": "AddedToken",
|
| 27 |
+
"content": "<|endoftext|>",
|
| 28 |
+
"lstrip": false,
|
| 29 |
+
"normalized": true,
|
| 30 |
+
"rstrip": false,
|
| 31 |
+
"single_word": false
|
| 32 |
+
}
|
| 33 |
+
}
|
train_results.json
ADDED
|
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"epoch": 1.0,
|
| 3 |
+
"train_loss": 0.2576859601399347,
|
| 4 |
+
"train_runtime": 150388.7494,
|
| 5 |
+
"train_samples": 26705019,
|
| 6 |
+
"train_samples_per_second": 177.573,
|
| 7 |
+
"train_steps_per_second": 22.197
|
| 8 |
+
}
|
trainer_state.json
ADDED
|
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|
|
|
training_args.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:3e3730b770a7a69a97a94adeeead1c692defea9bd7b7bf89372f99be11a0b762
|
| 3 |
+
size 4987
|
vocab.json
ADDED
|
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|
|
|