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  1. .gitattributes +6 -7
  2. data/math/gsm8k_cot_sc_gpt4o/prediction_temp=0.7_iter=1.json +3 -0
  3. data/math/gsm8k_cot_sc_gpt4o/prediction_temp=0.7_iter=2.json +3 -0
  4. data/math/gsm8k_cot_sc_gpt4o/prediction_temp=0.7_iter=3.json +3 -0
  5. data/math/gsm8k_cot_sc_gpt4o/prediction_temp=0.7_iter=4.json +3 -0
  6. data/math/gsm8k_cot_sc_gpt4o/prediction_temp=0.7_iter=5.json +3 -0
  7. data/math/gsm8k_cot_sc_merged_result.json +3 -0
  8. data/math/gsm8k_cot_sc_qwen/temp=0.7_iter=1.json +0 -0
  9. data/math/gsm8k_cot_sc_qwen/temp=0.7_iter=2.json +0 -0
  10. data/math/gsm8k_cot_sc_qwen/temp=0.7_iter=3.json +0 -0
  11. data/math/gsm8k_cot_sc_qwen/temp=0.7_iter=5.json +0 -0
  12. data/math/test_dump_gsm8k_train_perturbed_with_new_questions_answer_claude_sonnet.json +0 -0
  13. data/math/test_dump_gsm8k_train_perturbed_with_new_questions_answer_few_shot_cot_qwen.json +0 -0
  14. data/math/test_dump_gsm8k_train_perturbed_with_new_questions_answer_llama8b.json +0 -0
  15. data/math/test_dump_gsm8k_train_perturbed_with_new_questions_answer_qwen.json +0 -0
  16. data/math/test_dump_math_train_4o.json +0 -0
  17. data/math/test_dump_math_train_4o_perturbed.json +0 -0
  18. data/math/test_dump_math_train_4o_perturbed_with_new_questions.json +0 -0
  19. out/out.txt +0 -0
  20. out/out1.txt +4 -0
  21. out/out2.txt +4 -0
  22. out/out3.txt +4 -0
  23. out/out4.txt +4 -0
  24. out/out5.txt +4 -0
  25. run.sh +17 -0
  26. run_program.py +209 -43
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1020
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1021
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1024
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1101
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1102
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1103
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1111
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1114
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1115
  99%|█████████▉| 1115/1121 [10:54:45<04:03, 40.61s/it]
 
1
+ cpu-bind=MASK - nlpgpu06, task 0 0 [34210]: mask 0x100000001 set
2
+
3
+ 1121
4
+
5
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6
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7
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8
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9
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10
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11
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12
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13
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14
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15
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16
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17
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18
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19
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20
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21
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22
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23
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24
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25
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26
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27
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28
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29
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30
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31
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32
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33
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34
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35
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36
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37
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38
  3%|▎ | 33/1121 [20:25<11:24:53, 37.77s/it]
39
  3%|▎ | 34/1121 [21:09<11:59:25, 39.71s/it]
40
  3%|▎ | 35/1121 [22:06<13:30:48, 44.80s/it]
41
  3%|▎ | 36/1121 [22:52<13:34:30, 45.04s/it]
42
  3%|▎ | 37/1121 [23:49<14:41:48, 48.81s/it]
43
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44
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45
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46
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47
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48
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49
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50
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51
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52
  4%|▍ | 47/1121 [29:14<9:10:42, 30.77s/it]
53
  4%|▍ | 48/1121 [29:28<7:42:48, 25.88s/it]
54
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55
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56
  5%|▍ | 51/1121 [31:16<9:24:08, 31.63s/it]
57
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58
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59
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60
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61
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62
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63
  5%|▌ | 58/1121 [35:09<10:28:41, 35.49s/it]
64
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65
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66
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67
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68
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69
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70
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71
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72
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73
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74
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75
  6%|▌ | 70/1121 [41:19<8:20:35, 28.58s/it]
76
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77
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78
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79
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80
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81
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out/out2.txt ADDED
@@ -0,0 +1,4 @@
 
 
 
 
0
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1
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2
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3
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4
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5
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6
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7
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8
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9
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10
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1115
  99%|█████████▉| 1115/1121 [10:55:38<04:04, 40.77s/it]
 
1
+ cpu-bind=MASK - nlpgpu10, task 0 0 [23229]: mask 0x100000001 set
2
+
3
+ 1121
4
+
5
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27
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32
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33
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34
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35
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36
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37
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38
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39
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40
  3%|▎ | 35/1121 [21:55<13:29:10, 44.71s/it]
41
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42
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43
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44
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45
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46
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47
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48
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49
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50
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51
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52
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53
  4%|▍ | 48/1121 [29:13<7:37:23, 25.58s/it]
54
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55
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56
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57
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58
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59
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60
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61
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62
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63
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64
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65
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66
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67
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68
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69
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70
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71
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73
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74
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78
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80
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81
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82
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84
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85
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86
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87
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88
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89
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90
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91
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92
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93
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94
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95
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96
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97
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98
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99
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100
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101
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102
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103
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104
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105
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107
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108
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111
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112
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114
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115
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118
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119
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126
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131
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132
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133
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134
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135
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136
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142
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1017
  90%|█████████ | 1012/1121 [9:53:20<58:21, 32.13s/it]
1018
  90%|█████████ | 1013/1121 [9:53:37<49:50, 27.69s/it]
1019
  90%|█████████ | 1014/1121 [9:54:05<49:23, 27.70s/it]
1020
  91%|█████████ | 1015/1121 [9:54:58<1:02:31, 35.40s/it]
1021
  91%|█████████ | 1016/1121 [9:55:21<55:14, 31.56s/it]
1022
  91%|█████████ | 1017/1121 [9:56:01<59:31, 34.34s/it]
1023
  91%|█████████ | 1018/1121 [9:56:16<48:53, 28.48s/it]
1024
  91%|█████████ | 1019/1121 [9:56:42<47:03, 27.68s/it]
1025
  91%|█████████ | 1020/1121 [9:57:33<58:13, 34.59s/it]
1026
  91%|█████████ | 1021/1121 [9:58:15<1:01:22, 36.83s/it]
1027
  91%|█████████ | 1022/1121 [9:58:55<1:02:22, 37.81s/it]
1028
  91%|█████████▏| 1023/1121 [9:59:33<1:01:43, 37.79s/it]
1029
  91%|█████████▏| 1024/1121 [10:00:00<55:50, 34.54s/it]
1030
  91%|█████████▏| 1025/1121 [10:00:16<46:26, 29.03s/it]
1031
  92%|█████████▏| 1026/1121 [10:00:50<48:33, 30.67s/it]
1032
  92%|█████████▏| 1027/1121 [10:01:43<58:19, 37.23s/it]
1033
  92%|█████████▏| 1028/1121 [10:02:01<48:44, 31.45s/it]
1034
  92%|█████████▏| 1029/1121 [10:02:35<49:21, 32.19s/it]
1035
  92%|█████████▏| 1030/1121 [10:02:53<42:35, 28.08s/it]
1036
  92%|█████████▏| 1031/1121 [10:03:40<50:23, 33.60s/it]
1037
  92%|█████████▏| 1032/1121 [10:04:04<45:44, 30.84s/it]
1038
  92%|█████████▏| 1033/1121 [10:04:50<51:48, 35.33s/it]
1039
  92%|█████████▏| 1034/1121 [10:05:22<49:57, 34.45s/it]
1040
  92%|█████████▏| 1035/1121 [10:06:02<51:40, 36.05s/it]
1041
  92%|█████████▏| 1036/1121 [10:06:38<51:05, 36.06s/it]
1042
  93%|█████████▎| 1037/1121 [10:07:32<57:53, 41.36s/it]
1043
  93%|█████████▎| 1038/1121 [10:08:31<1:04:26, 46.58s/it]
1044
  93%|█████████▎| 1039/1121 [10:09:21<1:05:04, 47.62s/it]
1045
  93%|█████████▎| 1040/1121 [10:09:49<56:19, 41.72s/it]
1046
  93%|█████████▎| 1041/1121 [10:10:33<56:36, 42.45s/it]
1047
  93%|█████████▎| 1042/1121 [10:11:23<58:51, 44.70s/it]
1048
  93%|█████████▎| 1043/1121 [10:12:03<56:21, 43.35s/it]
1049
  93%|█████████▎| 1044/1121 [10:12:42<53:53, 41.99s/it]
1050
  93%|█████████▎| 1045/1121 [10:13:26<53:58, 42.62s/it]
1051
  93%|█████████▎| 1046/1121 [10:13:56<48:30, 38.80s/it]
1052
  93%|█████████▎| 1047/1121 [10:14:40<50:04, 40.61s/it]
1053
  93%|█████████▎| 1048/1121 [10:15:40<56:25, 46.38s/it]
1054
  94%|█████████▎| 1049/1121 [10:16:19<52:57, 44.13s/it]
1055
  94%|█████████▎| 1050/1121 [10:16:45<45:41, 38.61s/it]
1056
  94%|█████████▍| 1051/1121 [10:17:27<46:22, 39.75s/it]
1057
  94%|█████████▍| 1052/1121 [10:18:15<48:32, 42.21s/it]
1058
  94%|█████████▍| 1053/1121 [10:18:34<39:59, 35.29s/it]
1059
  94%|█████████▍| 1054/1121 [10:19:02<36:44, 32.90s/it]
1060
  94%|█████████▍| 1055/1121 [10:19:17<30:26, 27.67s/it]
1061
  94%|█████████▍| 1056/1121 [10:19:55<33:24, 30.83s/it]
1062
  94%|█████████▍| 1057/1121 [10:20:10<27:43, 25.99s/it]
1063
  94%|█████████▍| 1058/1121 [10:21:04<36:04, 34.36s/it]
1064
  94%|█████████▍| 1059/1121 [10:21:30<32:45, 31.71s/it]
1065
  95%|█████████▍| 1060/1121 [10:21:55<30:20, 29.84s/it]
1066
  95%|█████████▍| 1061/1121 [10:22:11<25:31, 25.53s/it]
1067
  95%|█████████▍| 1062/1121 [10:22:44<27:33, 28.02s/it]
1068
  95%|█████████▍| 1063/1121 [10:23:32<32:42, 33.84s/it]
1069
  95%|█████████▍| 1064/1121 [10:24:32<39:33, 41.63s/it]
1070
  95%|█████████▌| 1065/1121 [10:25:15<39:28, 42.29s/it]
1071
  95%|█████████▌| 1066/1121 [10:25:55<38:03, 41.52s/it]
1072
  95%|█████████▌| 1067/1121 [10:26:23<33:34, 37.31s/it]
1073
  95%|█████████▌| 1068/1121 [10:27:03<33:50, 38.32s/it]
1074
  95%|█████████▌| 1069/1121 [10:27:22<28:02, 32.36s/it]
1075
  95%|█████████▌| 1070/1121 [10:28:04<29:56, 35.23s/it]
1076
  96%|█████████▌| 1071/1121 [10:28:59<34:17, 41.16s/it]
1077
  96%|█████████▌| 1072/1121 [10:29:12<26:42, 32.70s/it]
1078
  96%|█████████▌| 1073/1121 [10:29:34<23:34, 29.47s/it]
1079
  96%|█████████▌| 1074/1121 [10:30:21<27:17, 34.84s/it]
1080
  96%|█████████▌| 1075/1121 [10:31:22<32:40, 42.61s/it]
1081
  96%|█████████▌| 1076/1121 [10:31:54<29:42, 39.62s/it]
1082
  96%|█████████▌| 1077/1121 [10:32:22<26:30, 36.15s/it]
1083
  96%|█████████▌| 1078/1121 [10:33:25<31:30, 43.96s/it]
1084
  96%|█████████▋| 1079/1121 [10:34:08<30:36, 43.72s/it]
1085
  96%|█████████▋| 1080/1121 [10:34:30<25:32, 37.38s/it]
1086
  96%|█████████▋| 1081/1121 [10:35:03<23:53, 35.83s/it]
1087
  97%|█████████▋| 1082/1121 [10:35:42<23:54, 36.79s/it]
1088
  97%|█████████▋| 1083/1121 [10:36:37<26:54, 42.47s/it]
1089
  97%|█████████▋| 1084/1121 [10:37:38<29:37, 48.03s/it]
1090
  97%|█████████▋| 1085/1121 [10:38:37<30:45, 51.26s/it]
1091
  97%|█████████▋| 1086/1121 [10:39:10<26:38, 45.68s/it]
1092
  97%|█████████▋| 1087/1121 [10:39:39<23:09, 40.87s/it]
1093
  97%|█████████▋| 1088/1121 [10:39:59<18:59, 34.54s/it]
1094
  97%|█████████▋| 1089/1121 [10:40:39<19:18, 36.19s/it]
1095
  97%|█████████▋| 1090/1121 [10:41:18<19:04, 36.91s/it]
1096
  97%|█████████▋| 1091/1121 [10:41:51<17:52, 35.75s/it]
1097
  97%|█████████▋| 1092/1121 [10:42:27<17:21, 35.93s/it]
1098
  98%|█████████▊| 1093/1121 [10:42:53<15:17, 32.77s/it]
1099
  98%|█████████▊| 1094/1121 [10:43:41<16:53, 37.55s/it]
1100
  98%|█████████▊| 1095/1121 [10:44:28<17:30, 40.39s/it]
1101
  98%|█████████▊| 1096/1121 [10:44:59<15:36, 37.48s/it]
1102
  98%|█████████▊| 1097/1121 [10:45:36<14:53, 37.24s/it]
1103
  98%|█████████▊| 1098/1121 [10:46:32<16:28, 42.99s/it]
1104
  98%|█████████▊| 1099/1121 [10:46:49<12:56, 35.29s/it]
1105
  98%|█████████▊| 1100/1121 [10:47:13<11:09, 31.87s/it]
1106
  98%|█████████▊| 1101/1121 [10:47:29<09:00, 27.01s/it]
1107
  98%|█████████▊| 1102/1121 [10:47:41<07:08, 22.55s/it]
1108
  98%|█████████▊| 1103/1121 [10:48:09<07:15, 24.17s/it]
1109
  98%|█████████▊| 1104/1121 [10:48:46<07:54, 27.91s/it]
1110
  99%|█████████▊| 1105/1121 [10:49:18<07:45, 29.11s/it]
1111
  99%|█████████▊| 1106/1121 [10:50:12<09:10, 36.67s/it]
1112
  99%|█████████▉| 1107/1121 [10:51:10<10:05, 43.22s/it]
1113
  99%|█████████▉| 1108/1121 [10:51:32<07:57, 36.76s/it]
1114
  99%|█████████▉| 1109/1121 [10:52:04<07:02, 35.23s/it]
1115
  99%|█████████▉| 1110/1121 [10:52:32<06:04, 33.09s/it]
1116
  99%|█████████▉| 1111/1121 [10:52:52<04:51, 29.14s/it]
1117
  99%|█████████▉| 1112/1121 [10:53:21<04:21, 29.02s/it]
1118
  99%|█████████▉| 1113/1121 [10:53:59<04:14, 31.85s/it]
1119
  99%|█████████▉| 1114/1121 [10:54:49<04:20, 37.19s/it]
1120
  99%|█████████▉| 1115/1121 [10:55:38<04:04, 40.77s/it]
out/out3.txt ADDED
@@ -0,0 +1,4 @@
 
 
 
 
0
  0%| | 0/1121 [00:00<?, ?it/s]
1
  0%| | 1/1121 [00:40<12:41:04, 40.77s/it]
2
  0%| | 2/1121 [01:07<10:11:12, 32.77s/it]
3
  0%| | 3/1121 [01:52<11:52:47, 38.25s/it]
4
  0%| | 4/1121 [02:34<12:17:56, 39.64s/it]
5
  0%| | 5/1121 [02:54<10:08:41, 32.73s/it]
6
  1%| | 6/1121 [03:18<9:10:16, 29.61s/it]
7
  1%| | 7/1121 [04:17<12:09:27, 39.29s/it]
8
  1%| | 8/1121 [04:53<11:47:41, 38.15s/it]
9
  1%| | 9/1121 [05:43<12:56:58, 41.92s/it]
10
  1%| | 10/1121 [06:41<14:26:26, 46.79s/it]
11
  1%| | 11/1121 [07:16<13:17:34, 43.11s/it]
12
  1%| | 12/1121 [07:39<11:25:41, 37.10s/it]
13
  1%| | 13/1121 [08:23<12:05:30, 39.29s/it]
14
  1%| | 14/1121 [08:57<11:34:42, 37.65s/it]
15
  1%|▏ | 15/1121 [09:42<12:11:56, 39.71s/it]
16
  1%|▏ | 16/1121 [10:05<10:38:18, 34.66s/it]
17
  2%|▏ | 17/1121 [10:57<12:17:45, 40.10s/it]
18
  2%|▏ | 18/1121 [12:00<14:21:09, 46.84s/it]
19
  2%|▏ | 19/1121 [12:27<12:33:02, 41.00s/it]
20
  2%|▏ | 20/1121 [13:13<12:59:40, 42.49s/it]
21
  2%|▏ | 21/1121 [13:33<10:51:42, 35.55s/it]
22
  2%|▏ | 22/1121 [13:57<9:51:13, 32.28s/it]
23
  2%|▏ | 23/1121 [15:02<12:51:26, 42.16s/it]
24
  2%|▏ | 24/1121 [15:44<12:47:05, 41.96s/it]
25
  2%|▏ | 25/1121 [16:05<10:52:57, 35.75s/it]
26
  2%|▏ | 26/1121 [16:42<10:58:21, 36.07s/it]
27
  2%|▏ | 27/1121 [17:33<12:18:03, 40.48s/it]
28
  2%|▏ | 28/1121 [17:49<10:07:29, 33.35s/it]
29
  3%|▎ | 29/1121 [18:11<9:04:42, 29.93s/it]
30
  3%|▎ | 30/1121 [19:01<10:50:19, 35.77s/it]
31
  3%|▎ | 31/1121 [19:52<12:11:30, 40.27s/it]
32
  3%|▎ | 32/1121 [20:19<11:02:09, 36.48s/it]
33
  3%|▎ | 33/1121 [21:05<11:52:49, 39.31s/it]
34
  3%|▎ | 34/1121 [21:50<12:24:27, 41.09s/it]
35
  3%|▎ | 35/1121 [22:48<13:51:16, 45.93s/it]
36
  3%|▎ | 36/1121 [23:34<13:55:35, 46.21s/it]
37
  3%|▎ | 37/1121 [24:34<15:08:27, 50.28s/it]
38
  3%|▎ | 38/1121 [25:19<14:38:47, 48.69s/it]
39
  3%|▎ | 39/1121 [25:40<12:09:11, 40.44s/it]
40
  4%|▎ | 40/1121 [26:44<14:11:50, 47.28s/it]
41
  4%|▎ | 41/1121 [27:06<11:55:08, 39.73s/it]
42
  4%|▎ | 42/1121 [27:37<11:07:40, 37.13s/it]
43
  4%|▍ | 43/1121 [27:59<9:46:13, 32.63s/it]
44
  4%|▍ | 44/1121 [28:33<9:53:05, 33.04s/it]
45
  4%|▍ | 45/1121 [29:13<10:29:02, 35.08s/it]
46
  4%|▍ | 46/1121 [29:36<9:22:44, 31.41s/it]
47
  4%|▍ | 47/1121 [30:08<9:27:18, 31.69s/it]
48
  4%|▍ | 48/1121 [30:23<7:58:01, 26.73s/it]
49
  4%|▍ | 49/1121 [30:53<8:15:19, 27.72s/it]
50
  4%|▍ | 50/1121 [31:55<11:16:30, 37.90s/it]
51
  5%|▍ | 51/1121 [32:15<9:42:17, 32.65s/it]
52
  5%|▍ | 52/1121 [32:39<8:55:09, 30.04s/it]
53
  5%|▍ | 53/1121 [33:23<10:10:01, 34.27s/it]
54
  5%|▍ | 54/1121 [33:42<8:43:51, 29.46s/it]
55
  5%|▍ | 55/1121 [34:07<8:21:53, 28.25s/it]
56
  5%|▍ | 56/1121 [34:53<9:57:31, 33.66s/it]
57
  5%|▌ | 57/1121 [35:40<11:05:39, 37.54s/it]
58
  5%|▌ | 58/1121 [36:14<10:45:05, 36.41s/it]
59
  5%|▌ | 59/1121 [36:46<10:21:04, 35.09s/it]
60
  5%|▌ | 60/1121 [37:36<11:40:14, 39.60s/it]
61
  5%|▌ | 61/1121 [38:24<12:25:49, 42.22s/it]
62
  6%|▌ | 62/1121 [38:43<10:22:22, 35.26s/it]
63
  6%|▌ | 63/1121 [39:04<9:07:33, 31.05s/it]
64
  6%|▌ | 64/1121 [39:20<7:46:47, 26.50s/it]
65
  6%|▌ | 65/1121 [39:47<7:50:26, 26.73s/it]
66
  6%|▌ | 66/1121 [40:34<9:33:52, 32.64s/it]
67
  6%|▌ | 67/1121 [41:02<9:11:08, 31.37s/it]
68
  6%|▌ | 68/1121 [41:37<9:25:42, 32.23s/it]
69
  6%|▌ | 69/1121 [42:12<9:43:19, 33.27s/it]
70
  6%|▌ | 70/1121 [42:32<8:33:34, 29.32s/it]
71
  6%|▋ | 71/1121 [43:37<11:36:02, 39.77s/it]
72
  6%|▋ | 72/1121 [44:13<11:15:49, 38.66s/it]
73
  7%|▋ | 73/1121 [44:39<10:10:14, 34.94s/it]
74
  7%|▋ | 74/1121 [45:40<12:24:38, 42.67s/it]
75
  7%|▋ | 75/1121 [46:27<12:47:20, 44.02s/it]
76
  7%|▋ | 76/1121 [47:23<13:51:59, 47.77s/it]
77
  7%|▋ | 77/1121 [48:06<13:27:19, 46.40s/it]
78
  7%|▋ | 78/1121 [48:26<11:05:04, 38.26s/it]
79
  7%|▋ | 79/1121 [48:40<8:57:03, 30.92s/it]
80
  7%|▋ | 80/1121 [49:21<9:51:09, 34.07s/it]
81
  7%|▋ | 81/1121 [49:39<8:29:42, 29.41s/it]
82
  7%|▋ | 82/1121 [50:32<10:31:46, 36.48s/it]
83
  7%|▋ | 83/1121 [51:15<11:02:50, 38.31s/it]
84
  7%|▋ | 84/1121 [52:03<11:49:39, 41.06s/it]
85
  8%|▊ | 85/1121 [52:37<11:13:34, 39.01s/it]
86
  8%|▊ | 86/1121 [53:12<10:54:16, 37.93s/it]
87
  8%|▊ | 87/1121 [53:49<10:47:46, 37.59s/it]
88
  8%|▊ | 88/1121 [54:10<9:21:47, 32.63s/it]
89
  8%|▊ | 89/1121 [54:46<9:38:56, 33.66s/it]
90
  8%|▊ | 90/1121 [55:33<10:48:17, 37.73s/it]
91
  8%|▊ | 91/1121 [56:15<11:10:07, 39.04s/it]
92
  8%|▊ | 92/1121 [56:38<9:46:49, 34.22s/it]
93
  8%|▊ | 93/1121 [57:14<9:51:14, 34.51s/it]
94
  8%|▊ | 94/1121 [58:03<11:07:02, 38.97s/it]
95
  8%|▊ | 95/1121 [58:32<10:13:30, 35.88s/it]
96
  9%|▊ | 96/1121 [58:52<8:54:53, 31.31s/it]
97
  9%|▊ | 97/1121 [59:33<9:42:14, 34.12s/it]
98
  9%|▊ | 98/1121 [1:00:35<12:02:38, 42.38s/it]
99
  9%|▉ | 99/1121 [1:01:25<12:45:20, 44.93s/it]
100
  9%|▉ | 100/1121 [1:02:11<12:48:55, 45.19s/it]
101
  9%|▉ | 101/1121 [1:02:56<12:44:49, 44.99s/it]
102
  9%|▉ | 102/1121 [1:03:55<13:56:31, 49.26s/it]
103
  9%|▉ | 103/1121 [1:04:51<14:28:52, 51.21s/it]
104
  9%|▉ | 104/1121 [1:05:37<14:03:52, 49.79s/it]
105
  9%|▉ | 105/1121 [1:05:50<10:54:34, 38.66s/it]
106
  9%|▉ | 106/1121 [1:06:11<9:23:30, 33.31s/it]
107
  10%|▉ | 107/1121 [1:07:05<11:09:25, 39.61s/it]
108
  10%|▉ | 108/1121 [1:07:28<9:45:23, 34.67s/it]
109
  10%|▉ | 109/1121 [1:07:57<9:12:41, 32.77s/it]
110
  10%|▉ | 110/1121 [1:08:47<10:39:30, 37.95s/it]
111
  10%|▉ | 111/1121 [1:09:26<10:44:09, 38.27s/it]
112
  10%|▉ | 112/1121 [1:10:32<13:06:27, 46.77s/it]
113
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1115
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1
+ cpu-bind=MASK - nlpgpu10, task 0 0 [23251]: mask 0x200000002 set
2
+
3
+ 1121
4
+
5
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11
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17
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20
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22
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27
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28
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30
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32
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33
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34
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35
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36
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37
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38
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39
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40
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41
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42
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44
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45
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46
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47
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48
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49
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50
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1103
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  98%|█████████▊| 1099/1121 [11:09:47<13:08, 35.86s/it]
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1111
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  99%|█████████▉| 1113/1121 [11:17:02<04:17, 32.20s/it]
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  99%|█████████▉| 1114/1121 [11:17:52<04:24, 37.76s/it]
1120
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out/out4.txt ADDED
@@ -0,0 +1,4 @@
 
 
 
 
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32
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33
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35
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36
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48
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62
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78
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1
+ cpu-bind=MASK - nlpgpu10, task 0 0 [56119]: mask 0x100000001 set
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out/out5.txt ADDED
@@ -0,0 +1,4 @@
 
 
 
 
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1115
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1
+ cpu-bind=MASK - nlpgpu05, task 0 0 [31297]: mask 0x10000000100 set
2
+
3
+ 1121
4
+
5
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11
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17
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20
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21
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22
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24
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27
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28
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30
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31
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32
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33
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34
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35
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36
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37
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38
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39
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40
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41
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42
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43
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44
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45
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run.sh ADDED
@@ -0,0 +1,17 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ #SBATCH --job-name=event
3
+ #SBATCH --output=/shared/xdyu/msr/reasoning_eval/out/out.txt
4
+ #SBATCH --partition=p_nlp
5
+ #SBATCH --constraint=48GBgpu
6
+ #SBATCH --nodes=1
7
+ #SBATCH --gpus=4
8
+ #SBATCH --cpus-per-task=1
9
+ #SBATCH --mem=200GB
10
+
11
+ cd /shared/xdyu/msr/reasoning_eval
12
+
13
+ export HF_HOME="/shared/.cache/transformers"
14
+ export PYTHONPATH="/shared/xdyu/msr/reasoning_eval"
15
+
16
+ python run_program.py
17
+
run_program.py CHANGED
@@ -5,6 +5,18 @@ from io import StringIO
5
  from contextlib import redirect_stdout
6
  from openai import OpenAI
7
  from tqdm import tqdm
 
 
 
 
 
 
 
 
 
 
 
 
8
 
9
 
10
  client = OpenAI(
@@ -14,6 +26,36 @@ client = OpenAI(
14
  )
15
 
16
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
17
  program_data = []
18
 
19
  PROMPT_DICT = {
@@ -26,7 +68,7 @@ PROMPT_DICT = {
26
  "Question: :\n{question}\n\nOld Parameters:\n{parameters}\n\nNew Parameters:\n{new_parameters}\n\nNew Question:"
27
  ),
28
  "prompt_answer_question": (
29
- "Answer the math question below. Only output the answer without units.\n\n"
30
  "Question:\n{question}\n\nAnswer:"
31
  ),
32
  "prompt_answer_question_few_shot_cot": (
@@ -86,11 +128,13 @@ def clear_parameter_output(response):
86
  response = response.replace('Parameters:', '').replace('```', '').replace('- ', '').strip().split('\n')
87
  for idx, para in enumerate(response):
88
  para.replace(str(idx)+'. ', '').strip()
 
89
  response = [x for x in response if len(x) > 0]
90
  return response
91
 
92
  def generate_new_parameter_value():
93
- infile = open('/shared/xdyu/msr/reasoning_hallucination/data/math/test_dump_gsm8k_train_perturbed.json', 'r')
 
94
  program_data = json.load(infile)
95
 
96
  correct_counter = 0
@@ -98,8 +142,15 @@ def generate_new_parameter_value():
98
 
99
  correct_case = []
100
 
101
- for case in program_data:
102
- program = case['candidate_programs'][0]
 
 
 
 
 
 
 
103
  program += call_function_with_args(case['parameters'])
104
  # print(program)
105
  try:
@@ -115,36 +166,44 @@ def generate_new_parameter_value():
115
  # print(case['answer'])
116
  except Exception as e:
117
  print(e)
118
- print(program)
119
  compile_fail_counter += 1
 
120
  print(correct_counter)
121
 
 
122
  for case in tqdm(correct_case):
123
  if 'new_answers' in case and len(case['new_answers']) == 5:
124
  continue
125
- response = perturb_parameter_value(case)
126
- new_values = clear_parameter_output(response.choices[0].message.content)
127
- if len(new_values) == 6:
128
- new_values = new_values[1:]
129
- case['new_parameters'] = new_values
 
 
130
  try:
131
  case['new_answers'] = []
132
  for parameter in case['new_parameters']:
133
  program = case['candidate_programs'][0]
134
  program += call_function_with_args(parameter)
135
  f = StringIO()
 
 
 
136
  with redirect_stdout(f):
137
  exec(program)
138
  s = f.getvalue().strip()
139
  case['new_answers'].append(s)
140
  except Exception as e:
141
  print(e)
142
- print(new_values)
143
  continue
144
  # break
145
- outfile = open('data/math/test_dump_gsm8k_train_perturbed.json', 'w')
146
  json.dump(correct_case, outfile, indent=4)
147
 
 
148
 
149
  def rewrite_question(case):
150
  prompt_template = PROMPT_DICT['prompt_rewrite_question']
@@ -162,7 +221,8 @@ def rewrite_question(case):
162
 
163
 
164
  def update_question_with_new_parameters():
165
- infile = open('/shared/xdyu/msr/reasoning_hallucination/data/math/test_dump_gsm8k_train_perturbed.json', 'r')
 
166
  program_data = json.load(infile)
167
  print(len(program_data))
168
  for case in tqdm(program_data):
@@ -174,58 +234,162 @@ def update_question_with_new_parameters():
174
  case['new_questions'] = new_values
175
  # print(case)
176
  # break
177
- outfile = open('data/math/test_dump_gsm8k_train_perturbed_with_new_questions.json', 'w')
178
  json.dump(program_data, outfile, indent=4)
179
 
180
 
181
- def call_answer_question(question):
182
- prompt_template = PROMPT_DICT['prompt_answer_question_few_shot_cot']
 
183
  prompt = prompt_template.format_map(
184
  {"question": question}
185
  )
186
  # print(prompt)
187
- response = client.chat.completions.create(
188
- model="gpt-4o",
189
- messages=[
190
- {"role": "system", "content": "You are a helpful assistant."},
191
- {"role": "user", "content": prompt}
192
- ],
193
- temperature=0,
194
- max_tokens=300,
195
- top_p=1
196
- )
197
- return response
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
198
 
199
 
200
- def answer_question():
201
- infile = open('/shared/xdyu/msr/reasoning_hallucination/data/math/test_dump_gsm8k_train_perturbed_with_new_questions.json', 'r')
202
  program_data = json.load(infile)
203
  print(len(program_data))
204
  for case in tqdm(program_data):
205
- response = call_answer_question(case['question'])
206
- case['prediction'] = response.choices[0].message.content
207
  # print(case['prediction'])
208
  case['new_prediction'] = []
209
  for question in case['new_questions']:
210
- response = call_answer_question(question)
211
- case['new_prediction'].append(response.choices[0].message.content)
212
  # print(case)
213
  # break
214
  # print(case)
215
  # break
216
- outfile = open('data/math/test_dump_gsm8k_train_perturbed_with_new_questions_answer_few_shot_cot.json', 'w')
 
217
  json.dump(program_data, outfile, indent=4)
218
 
219
 
220
  def parse_answer(answer):
221
- if 'answer is' in answer:
222
- answer = answer.split('answer is')[-1].strip()
 
 
 
 
 
 
 
223
  else:
224
- answer = answer.split(' ')[-1]
225
- if len(answer) > 0 and answer[-1] == '.':
226
- answer = answer[0:-1]
227
- answer = answer.replace('$','').replace(',','').strip()
228
- return answer
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
229
 
230
  def evaluator(infile_path):
231
  infile = open(infile_path, 'r')
@@ -272,8 +436,10 @@ def evaluator(infile_path):
272
  def main():
273
  # generate_new_parameter_value()
274
  # update_question_with_new_parameters()
275
- # answer_question()
276
- evaluator('data/math/test_dump_gsm8k_train_perturbed_with_new_questions_answer_few_shot_cot.json')
 
 
277
 
278
 
279
  if __name__ == "__main__":
 
5
  from contextlib import redirect_stdout
6
  from openai import OpenAI
7
  from tqdm import tqdm
8
+ import os
9
+ import re
10
+ import anthropic
11
+ import math
12
+ from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
13
+ import torch
14
+
15
+
16
+ claude_client = anthropic.Anthropic(
17
+ # defaults to os.environ.get("ANTHROPIC_API_KEY")
18
+ api_key="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",
19
+ )
20
 
21
 
22
  client = OpenAI(
 
26
  )
27
 
28
 
29
+ torch.random.manual_seed(0)
30
+
31
+ # phi_model = AutoModelForCausalLM.from_pretrained(
32
+ # "microsoft/Phi-3.5-MoE-instruct",
33
+ # device_map="cuda",
34
+ # torch_dtype="auto",
35
+ # trust_remote_code=True,
36
+ # )
37
+ #
38
+ # phi_tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3.5-MoE-instruct")
39
+
40
+ # qwen_model_name = "Qwen/Qwen2.5-7B-Instruct"
41
+ #
42
+ # qwen_model = AutoModelForCausalLM.from_pretrained(
43
+ # qwen_model_name,
44
+ # torch_dtype="auto",
45
+ # device_map="auto"
46
+ # )
47
+ # qwen_tokenizer = AutoTokenizer.from_pretrained(qwen_model_name)
48
+
49
+ llama_pipeline = pipeline(
50
+ "text-generation",
51
+ model="meta-llama/Meta-Llama-3.1-8B-Instruct",
52
+ model_kwargs={"torch_dtype": torch.bfloat16},
53
+ device_map="auto",
54
+ )
55
+
56
+
57
+
58
+
59
  program_data = []
60
 
61
  PROMPT_DICT = {
 
68
  "Question: :\n{question}\n\nOld Parameters:\n{parameters}\n\nNew Parameters:\n{new_parameters}\n\nNew Question:"
69
  ),
70
  "prompt_answer_question": (
71
+ "Answer the math question below. Only output the answer without units and any context words.\n\n"
72
  "Question:\n{question}\n\nAnswer:"
73
  ),
74
  "prompt_answer_question_few_shot_cot": (
 
128
  response = response.replace('Parameters:', '').replace('```', '').replace('- ', '').strip().split('\n')
129
  for idx, para in enumerate(response):
130
  para.replace(str(idx)+'. ', '').strip()
131
+ para.replace(str(idx+1) + '. ', '').strip()
132
  response = [x for x in response if len(x) > 0]
133
  return response
134
 
135
  def generate_new_parameter_value():
136
+ # infile = open('/shared/xdyu/msr/reasoning_hallucination/data/math/test_dump_gsm8k_train_perturbed.json', 'r')
137
+ infile = open('/shared/xdyu/msr/reasoning_eval/data/math/test_dump_math_train_4o_perturbed.json', 'r')
138
  program_data = json.load(infile)
139
 
140
  correct_counter = 0
 
142
 
143
  correct_case = []
144
 
145
+ for idx, case in enumerate(program_data):
146
+ if idx in [306]:
147
+ continue
148
+ # program = case['candidate_programs'][0]
149
+ if len(case['selected_programs']) == 0:
150
+ continue
151
+ program = case['selected_programs'][0]
152
+ if 'math.' in program:
153
+ program = 'import math\n\n' + program
154
  program += call_function_with_args(case['parameters'])
155
  # print(program)
156
  try:
 
166
  # print(case['answer'])
167
  except Exception as e:
168
  print(e)
169
+ print(case)
170
  compile_fail_counter += 1
171
+ print(len(program_data))
172
  print(correct_counter)
173
 
174
+ dead_loop_counter = 0
175
  for case in tqdm(correct_case):
176
  if 'new_answers' in case and len(case['new_answers']) == 5:
177
  continue
178
+ if 'new_parameters' not in case or ('new_parameters' in case and 'new_answers' in case and len(case['new_answers']) < 5):
179
+ response = perturb_parameter_value(case)
180
+ new_values = clear_parameter_output(response.choices[0].message.content)
181
+ if len(new_values) == 6:
182
+ new_values = new_values[1:]
183
+ case['new_parameters'] = new_values
184
+
185
  try:
186
  case['new_answers'] = []
187
  for parameter in case['new_parameters']:
188
  program = case['candidate_programs'][0]
189
  program += call_function_with_args(parameter)
190
  f = StringIO()
191
+ if 'while True:' in program:
192
+ dead_loop_counter += 1
193
+ continue
194
  with redirect_stdout(f):
195
  exec(program)
196
  s = f.getvalue().strip()
197
  case['new_answers'].append(s)
198
  except Exception as e:
199
  print(e)
200
+ print(case['new_parameters'])
201
  continue
202
  # break
203
+ outfile = open('data/math/test_dump_math_train_4o_perturbed.json', 'w')
204
  json.dump(correct_case, outfile, indent=4)
205
 
206
+ print(dead_loop_counter)
207
 
208
  def rewrite_question(case):
209
  prompt_template = PROMPT_DICT['prompt_rewrite_question']
 
221
 
222
 
223
  def update_question_with_new_parameters():
224
+ # infile = open('/shared/xdyu/msr/reasoning_hallucination/data/math/test_dump_gsm8k_train_perturbed.json', 'r')
225
+ infile = open('/shared/xdyu/msr/reasoning_eval/data/math/test_dump_math_train_4o_perturbed.json', 'r')
226
  program_data = json.load(infile)
227
  print(len(program_data))
228
  for case in tqdm(program_data):
 
234
  case['new_questions'] = new_values
235
  # print(case)
236
  # break
237
+ outfile = open('data/math/test_dump_math_train_4o_perturbed_with_new_questions.json', 'w')
238
  json.dump(program_data, outfile, indent=4)
239
 
240
 
241
+ def call_answer_question(question, model_name='gpt'):
242
+ prompt_template = PROMPT_DICT['prompt_answer_question']
243
+ # prompt_template = PROMPT_DICT['prompt_answer_question_few_shot_cot']
244
  prompt = prompt_template.format_map(
245
  {"question": question}
246
  )
247
  # print(prompt)
248
+ if model_name == 'gpt':
249
+ response = client.chat.completions.create(
250
+ model="gpt-4o",
251
+ messages=[
252
+ {"role": "system", "content": "You are a helpful assistant."},
253
+ {"role": "user", "content": prompt}
254
+ ],
255
+ temperature=0,
256
+ max_tokens=300,
257
+ top_p=1
258
+ )
259
+ return response.choices[0].message.content
260
+ else:
261
+ if model_name == 'claude':
262
+ message = claude_client.messages.create(
263
+ model="claude-3-5-sonnet-20240620",
264
+ max_tokens=300,
265
+ messages=[
266
+ {"role": "user", "content": prompt}
267
+ ],
268
+ temperature=0,
269
+ top_p=1
270
+ )
271
+ return message.content[0].text
272
+ # if model_name == 'phi':
273
+ # messages = [
274
+ # {"role": "system", "content": "You are a helpful AI assistant."},
275
+ # {"role": "user", "content": prompt}
276
+ # ]
277
+ #
278
+ # pipe = pipeline(
279
+ # "text-generation",
280
+ # model=phi_model,
281
+ # tokenizer=phi_tokenizer,
282
+ # )
283
+ #
284
+ # generation_args = {
285
+ # "max_new_tokens": 300,
286
+ # "return_full_text": False,
287
+ # "temperature": 0.0,
288
+ # "do_sample": False,
289
+ # }
290
+ #
291
+ # output = pipe(messages, **generation_args)
292
+ # print(output[0]['generated_text'])
293
+ # if model_name == 'qwen':
294
+ # messages = [
295
+ # {"role": "system", "content": "You are a helpful assistant."},
296
+ # {"role": "user", "content": prompt}
297
+ # ]
298
+ # text = qwen_tokenizer.apply_chat_template(
299
+ # messages,
300
+ # tokenize=False,
301
+ # add_generation_prompt=True
302
+ # )
303
+ # model_inputs = qwen_tokenizer([text], return_tensors="pt").to(qwen_model.device)
304
+ #
305
+ # generated_ids = qwen_model.generate(
306
+ # **model_inputs,
307
+ # max_new_tokens=300,
308
+ # temperature=0.7
309
+ # )
310
+ # generated_ids = [
311
+ # output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
312
+ # ]
313
+ #
314
+ # response = qwen_tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
315
+ # return response
316
+ if model_name == 'llama':
317
+ messages = [
318
+ {"role": "system", "content": "You are a helpful assistant."},
319
+ {"role": "user", "content": prompt},
320
+ ]
321
+ outputs = llama_pipeline(
322
+ messages,
323
+ max_new_tokens=300,
324
+ temperature=0.00001
325
+ )
326
+ # print(outputs[0]["generated_text"][-1])
327
+ return outputs[0]["generated_text"][-1]['content']
328
 
329
 
330
+ def answer_question(model_name='gpt'):
331
+ infile = open('data/math/test_dump_gsm8k_train_perturbed_with_new_questions.json', 'r')
332
  program_data = json.load(infile)
333
  print(len(program_data))
334
  for case in tqdm(program_data):
335
+ response = call_answer_question(case['question'], model_name=model_name)
336
+ case['prediction'] = response
337
  # print(case['prediction'])
338
  case['new_prediction'] = []
339
  for question in case['new_questions']:
340
+ response = call_answer_question(question, model_name=model_name)
341
+ case['new_prediction'].append(response)
342
  # print(case)
343
  # break
344
  # print(case)
345
  # break
346
+ outfile = open('data/math/test_dump_gsm8k_train_perturbed_with_new_questions_answer_llama8b.json', 'w')
347
+ # outfile = open('data/math/gsm8k_cot_sc_qwen/temp=0.7_iter=4.json', 'w')
348
  json.dump(program_data, outfile, indent=4)
349
 
350
 
351
  def parse_answer(answer):
352
+ if type(answer) is not list:
353
+ if 'answer is' in answer:
354
+ answer = answer.split('answer is')[-1].strip()
355
+ else:
356
+ answer = answer.split(' ')[-1]
357
+ if len(answer) > 0 and answer[-1] == '.':
358
+ answer = answer[0:-1]
359
+ answer = re.sub("[^\d\.]", "", answer)
360
+ return answer
361
  else:
362
+ answer_freq = {}
363
+ for x in answer:
364
+ parsed_answer = parse_answer(x)
365
+ if parsed_answer not in answer_freq:
366
+ answer_freq[parsed_answer] = 0
367
+ answer_freq[parsed_answer] += 1
368
+ answer_freq = sorted(answer_freq.items(), key=lambda item: item[1], reverse=True)
369
+ return answer_freq[0][0]
370
+
371
+
372
+ def collect_self_consistency_result(infile_path):
373
+ folder_data_list = []
374
+ for file in os.listdir(infile_path):
375
+ infile = open(infile_path + '/' + file, 'r')
376
+ file_data = json.load(infile)
377
+ folder_data_list.append(file_data)
378
+ merged_data = folder_data_list[0]
379
+ for idx, case in enumerate(merged_data):
380
+ prediction = []
381
+ for file_data in folder_data_list:
382
+ prediction.append(file_data[idx]['prediction'])
383
+ case['prediction'] = prediction
384
+ for q_idx in range(0, len(case['new_prediction'])):
385
+ new_prediction = []
386
+ for file_data in folder_data_list:
387
+ new_prediction.append(file_data[idx]['new_prediction'][q_idx])
388
+ case['new_prediction'][q_idx] = new_prediction
389
+ outfile = open(infile_path+'_merged_result.json', 'w')
390
+ json.dump(merged_data, outfile, indent=4)
391
+
392
+
393
 
394
  def evaluator(infile_path):
395
  infile = open(infile_path, 'r')
 
436
  def main():
437
  # generate_new_parameter_value()
438
  # update_question_with_new_parameters()
439
+ answer_question(model_name='llama')
440
+ # collect_self_consistency_result('data/math/gsm8k_cot_sc')
441
+ # evaluator('data/math/gsm8k_cot_sc_merged_result.json')
442
+ # evaluator('data/math/test_dump_gsm8k_train_perturbed_with_new_questions_answer_few_shot_cot_qwen.json')
443
 
444
 
445
  if __name__ == "__main__":