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/accounts/projects/binyu/timothygao/.local/lib/python3.12/site-packages/transformers/utils/hub.py:127: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.
warnings.warn(
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
question ... meta_info
0 A 4670-g (10-lb 5-oz) male newborn is delivere... ... step2
1 A 66-year-old man comes to the physician for a... ... step2
2 A 40-year-old man comes to the physician becau... ... step2
3 A 4-year-old boy is brought to the emergency d... ... step2
4 A 9-year-old girl is brought to the emergency ... ... step2
[5 rows x 4 columns]
Traceback (most recent call last):
File "/accounts/projects/binyu/timothygao/Benign-Perturbation-Attack/sensitivity_v_accuracy.py", line 32, in <module>
model = AutoModelForCausalLM.from_pretrained(model_name, cache_dir=cache_dir, torch_dtype=torch.float16, device_map="auto")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/accounts/projects/binyu/timothygao/.local/lib/python3.12/site-packages/transformers/models/auto/auto_factory.py", line 564, in from_pretrained
return model_class.from_pretrained(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/accounts/projects/binyu/timothygao/.local/lib/python3.12/site-packages/transformers/modeling_utils.py", line 3199, in from_pretrained
raise ImportError(
ImportError: Using `low_cpu_mem_usage=True` or a `device_map` requires Accelerate: `pip install accelerate`
/accounts/projects/binyu/timothygao/.local/lib/python3.11/site-packages/transformers/utils/hub.py:127: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.
warnings.warn(
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
question ... meta_info
0 A 4670-g (10-lb 5-oz) male newborn is delivere... ... step2
1 A 66-year-old man comes to the physician for a... ... step2
2 A 40-year-old man comes to the physician becau... ... step2
3 A 4-year-old boy is brought to the emergency d... ... step2
4 A 9-year-old girl is brought to the emergency ... ... step2
[5 rows x 4 columns]
Loading checkpoint shards: 0%| | 0/4 [00:00<?, ?it/s]
Loading checkpoint shards: 25%|██▌ | 1/4 [00:04<00:12, 4.06s/it]
Loading checkpoint shards: 50%|█████ | 2/4 [00:08<00:08, 4.09s/it]
Loading checkpoint shards: 75%|███████▌ | 3/4 [00:12<00:04, 4.04s/it]
Loading checkpoint shards: 100%|██████████| 4/4 [00:13<00:00, 2.84s/it]
Loading checkpoint shards: 100%|██████████| 4/4 [00:13<00:00, 3.29s/it]
0%| | 0/5000 [00:00<?, ?it/s]We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)
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2%|▏ | 79/5000 [03:04<3:08:34, 2.30s/it, Correct %=58.23%]
2%|▏ | 79/5000 [03:06<3:08:34, 2.30s/it, Correct %=57.50%]
2%|▏ | 80/5000 [03:06<3:04:58, 2.26s/it, Correct %=57.50%]
2%|▏ | 80/5000 [03:08<3:04:58, 2.26s/it, Correct %=58.02%]
2%|▏ | 81/5000 [03:08<2:49:48, 2.07s/it, Correct %=58.02%]
2%|▏ | 81/5000 [03:09<2:49:48, 2.07s/it, Correct %=58.54%]
2%|▏ | 82/5000 [03:09<2:34:10, 1.88s/it, Correct %=58.54%]
2%|▏ | 82/5000 [03:12<2:34:10, 1.88s/it, Correct %=59.04%]
2%|▏ | 83/5000 [03:12<2:51:41, 2.10s/it, Correct %=59.04%]
2%|▏ | 83/5000 [03:13<2:51:41, 2.10s/it, Correct %=59.52%]
2%|▏ | 84/5000 [03:13<2:37:21, 1.92s/it, Correct %=59.52%]
2%|▏ | 84/5000 [03:17<2:37:21, 1.92s/it, Correct %=58.82%]
2%|▏ | 85/5000 [03:17<3:22:52, 2.48s/it, Correct %=58.82%]
2%|▏ | 85/5000 [03:20<3:22:52, 2.48s/it, Correct %=59.30%]
2%|▏ | 86/5000 [03:20<3:41:21, 2.70s/it, Correct %=59.30%]
2%|▏ | 86/5000 [03:23<3:41:21, 2.70s/it, Correct %=59.77%]
2%|▏ | 87/5000 [03:23<3:29:07, 2.55s/it, Correct %=59.77%]
2%|▏ | 87/5000 [03:25<3:29:07, 2.55s/it, Correct %=60.23%]
2%|▏ | 88/5000 [03:25<3:32:46, 2.60s/it, Correct %=60.23%]
2%|▏ | 88/5000 [03:27<3:32:46, 2.60s/it, Correct %=60.67%]
2%|▏ | 89/5000 [03:27<3:20:08, 2.45s/it, Correct %=60.67%]
2%|▏ | 89/5000 [03:30<3:20:08, 2.45s/it, Correct %=61.11%]
2%|▏ | 90/5000 [03:30<3:33:09, 2.60s/it, Correct %=61.11%]
2%|▏ | 90/5000 [03:32<3:33:09, 2.60s/it, Correct %=61.54%]
2%|▏ | 91/5000 [03:32<3:11:55, 2.35s/it, Correct %=61.54%]
2%|▏ | 91/5000 [03:35<3:11:55, 2.35s/it, Correct %=60.87%]
2%|▏ | 92/5000 [03:35<3:37:28, 2.66s/it, Correct %=60.87%]
2%|▏ | 92/5000 [03:37<3:37:28, 2.66s/it, Correct %=60.22%]
2%|▏ | 93/5000 [03:37<3:10:36, 2.33s/it, Correct %=60.22%]
2%|▏ | 93/5000 [03:43<3:10:36, 2.33s/it, Correct %=59.57%]
2%|▏ | 94/5000 [03:43<4:41:18, 3.44s/it, Correct %=59.57%]
2%|▏ | 94/5000 [03:44<4:41:18, 3.44s/it, Correct %=58.95%]
2%|▏ | 95/5000 [03:44<3:52:01, 2.84s/it, Correct %=58.95%]
2%|▏ | 95/5000 [03:47<3:52:01, 2.84s/it, Correct %=59.38%]
2%|▏ | 96/5000 [03:47<3:45:51, 2.76s/it, Correct %=59.38%]
2%|▏ | 96/5000 [03:50<3:45:51, 2.76s/it, Correct %=58.76%]
2%|▏ | 97/5000 [03:50<3:42:13, 2.72s/it, Correct %=58.76%]
2%|▏ | 97/5000 [03:52<3:42:13, 2.72s/it, Correct %=58.16%]
2%|▏ | 98/5000 [03:52<3:27:55, 2.55s/it, Correct %=58.16%]
2%|▏ | 98/5000 [03:54<3:27:55, 2.55s/it, Correct %=58.59%]
2%|▏ | 99/5000 [03:54<3:14:28, 2.38s/it, Correct %=58.59%]
2%|▏ | 99/5000 [03:56<3:14:28, 2.38s/it, Correct %=59.00%]
2%|▏ | 100/5000 [03:56<3:05:58, 2.28s/it, Correct %=59.00%]
2%|▏ | 100/5000 [03:59<3:05:58, 2.28s/it, Correct %=59.41%]
2%|▏ | 101/5000 [03:59<3:28:15, 2.55s/it, Correct %=59.41%]
2%|▏ | 101/5000 [04:01<3:28:15, 2.55s/it, Correct %=59.80%]
2%|▏ | 102/5000 [04:01<3:08:45, 2.31s/it, Correct %=59.80%]
2%|▏ | 102/5000 [04:03<3:08:45, 2.31s/it, Correct %=60.19%]
2%|▏ | 103/5000 [04:03<2:55:47, 2.15s/it, Correct %=60.19%]
2%|▏ | 103/5000 [04:05<2:55:47, 2.15s/it, Correct %=60.58%]
2%|▏ | 104/5000 [04:05<3:04:13, 2.26s/it, Correct %=60.58%]
2%|▏ | 104/5000 [04:08<3:04:13, 2.26s/it, Correct %=60.00%]
2%|▏ | 105/5000 [04:08<3:28:34, 2.56s/it, Correct %=60.00%]
2%|▏ | 105/5000 [04:10<3:28:34, 2.56s/it, Correct %=59.43%]
2%|▏ | 106/5000 [04:10<3:12:49, 2.36s/it, Correct %=59.43%]
2%|▏ | 106/5000 [04:12<3:12:49, 2.36s/it, Correct %=59.81%]
2%|▏ | 107/5000 [04:12<2:55:45, 2.16s/it, Correct %=59.81%]
2%|▏ | 107/5000 [04:14<2:55:45, 2.16s/it, Correct %=60.19%]
2%|▏ | 108/5000 [04:14<2:46:56, 2.05s/it, Correct %=60.19%]
2%|▏ | 108/5000 [04:17<2:46:56, 2.05s/it, Correct %=60.55%]
2%|▏ | 109/5000 [04:17<3:28:05, 2.55s/it, Correct %=60.55%]
2%|▏ | 109/5000 [04:20<3:28:05, 2.55s/it, Correct %=60.91%]
2%|▏ | 110/5000 [04:20<3:21:56, 2.48s/it, Correct %=60.91%]
2%|▏ | 110/5000 [04:23<3:21:56, 2.48s/it, Correct %=60.36%]
2%|▏ | 111/5000 [04:23<3:39:28, 2.69s/it, Correct %=60.36%]
2%|▏ | 111/5000 [04:26<3:39:28, 2.69s/it, Correct %=59.82%]
2%|▏ | 112/5000 [04:26<3:44:34, 2.76s/it, Correct %=59.82%]
2%|▏ | 112/5000 [04:28<3:44:34, 2.76s/it, Correct %=59.29%]
2%|▏ | 113/5000 [04:28<3:25:50, 2.53s/it, Correct %=59.29%]
2%|▏ | 113/5000 [04:29<3:25:50, 2.53s/it, Correct %=59.65%]
2%|▏ | 114/5000 [04:29<2:59:55, 2.21s/it, Correct %=59.65%]
2%|▏ | 114/5000 [04:32<2:59:55, 2.21s/it, Correct %=59.13%]
2%|▏ | 115/5000 [04:32<3:00:58, 2.22s/it, Correct %=59.13%]
2%|▏ | 115/5000 [04:35<3:00:58, 2.22s/it, Correct %=59.48%]
2%|▏ | 116/5000 [04:35<3:32:13, 2.61s/it, Correct %=59.48%]
2%|▏ | 116/5000 [04:38<3:32:13, 2.61s/it, Correct %=58.97%]
2%|▏ | 117/5000 [04:38<3:34:37, 2.64s/it, Correct %=58.97%]
2%|▏ | 117/5000 [04:40<3:34:37, 2.64s/it, Correct %=59.32%]
2%|▏ | 118/5000 [04:40<3:23:07, 2.50s/it, Correct %=59.32%]
2%|▏ | 118/5000 [04:42<3:23:07, 2.50s/it, Correct %=59.66%]
2%|▏ | 119/5000 [04:42<3:05:15, 2.28s/it, Correct %=59.66%]
2%|▏ | 119/5000 [04:45<3:05:15, 2.28s/it, Correct %=60.00%]
2%|▏ | 120/5000 [04:45<3:26:41, 2.54s/it, Correct %=60.00%]
2%|▏ | 120/5000 [04:48<3:26:41, 2.54s/it, Correct %=60.33%]
2%|▏ | 121/5000 [04:48<3:45:04, 2.77s/it, Correct %=60.33%]
2%|▏ | 121/5000 [04:50<3:45:04, 2.77s/it, Correct %=60.66%]
2%|▏ | 122/5000 [04:50<3:27:47, 2.56s/it, Correct %=60.66%]
2%|▏ | 122/5000 [04:52<3:27:47, 2.56s/it, Correct %=60.98%]
2%|▏ | 123/5000 [04:52<3:01:30, 2.23s/it, Correct %=60.98%]
2%|▏ | 123/5000 [04:55<3:01:30, 2.23s/it, Correct %=61.29%]
2%|▏ | 124/5000 [04:55<3:17:31, 2.43s/it, Correct %=61.29%]
2%|▏ | 124/5000 [04:57<3:17:31, 2.43s/it, Correct %=61.60%]
2%|▎ | 125/5000 [04:57<3:14:19, 2.39s/it, Correct %=61.60%]
2%|▎ | 125/5000 [05:00<3:14:19, 2.39s/it, Correct %=61.90%]
3%|▎ | 126/5000 [05:00<3:32:04, 2.61s/it, Correct %=61.90%]
3%|▎ | 126/5000 [05:02<3:32:04, 2.61s/it, Correct %=62.20%]
3%|▎ | 127/5000 [05:02<3:07:34, 2.31s/it, Correct %=62.20%]
3%|▎ | 127/5000 [05:04<3:07:34, 2.31s/it, Correct %=61.72%]
3%|▎ | 128/5000 [05:04<3:04:49, 2.28s/it, Correct %=61.72%]
3%|▎ | 128/5000 [05:05<3:04:49, 2.28s/it, Correct %=61.24%]
3%|▎ | 129/5000 [05:05<2:49:08, 2.08s/it, Correct %=61.24%]
3%|▎ | 129/5000 [05:08<2:49:08, 2.08s/it, Correct %=60.77%]
3%|▎ | 130/5000 [05:08<2:53:11, 2.13s/it, Correct %=60.77%]
3%|▎ | 130/5000 [05:12<2:53:11, 2.13s/it, Correct %=60.31%]
3%|▎ | 131/5000 [05:12<3:36:29, 2.67s/it, Correct %=60.31%]
3%|▎ | 131/5000 [05:15<3:36:29, 2.67s/it, Correct %=59.85%]
3%|▎ | 132/5000 [05:15<3:42:15, 2.74s/it, Correct %=59.85%]
3%|▎ | 132/5000 [05:18<3:42:15, 2.74s/it, Correct %=59.40%]
3%|▎ | 133/5000 [05:18<4:10:46, 3.09s/it, Correct %=59.40%]
3%|▎ | 133/5000 [05:20<4:10:46, 3.09s/it, Correct %=59.70%]
3%|▎ | 134/5000 [05:20<3:35:10, 2.65s/it, Correct %=59.70%]
3%|▎ | 134/5000 [05:22<3:35:10, 2.65s/it, Correct %=59.26%]
3%|▎ | 135/5000 [05:22<3:28:48, 2.58s/it, Correct %=59.26%]
3%|▎ | 135/5000 [05:25<3:28:48, 2.58s/it, Correct %=59.56%]
3%|▎ | 136/5000 [05:25<3:27:13, 2.56s/it, Correct %=59.56%]
3%|▎ | 136/5000 [05:27<3:27:13, 2.56s/it, Correct %=59.85%]
3%|▎ | 137/5000 [05:27<3:09:48, 2.34s/it, Correct %=59.85%]
3%|▎ | 137/5000 [05:29<3:09:48, 2.34s/it, Correct %=59.42%]
3%|▎ | 138/5000 [05:29<3:04:08, 2.27s/it, Correct %=59.42%]
3%|▎ | 138/5000 [05:31<3:04:08, 2.27s/it, Correct %=58.99%]
3%|▎ | 139/5000 [05:31<2:55:40, 2.17s/it, Correct %=58.99%]
3%|▎ | 139/5000 [05:35<2:55:40, 2.17s/it, Correct %=59.29%]
3%|▎ | 140/5000 [05:35<3:39:01, 2.70s/it, Correct %=59.29%]
3%|▎ | 140/5000 [05:37<3:39:01, 2.70s/it, Correct %=58.87%]
3%|▎ | 141/5000 [05:37<3:18:30, 2.45s/it, Correct %=58.87%]
3%|▎ | 141/5000 [05:43<3:17:04, 2.43s/it, Correct %=58.87%]
0.1 True
0.79 False
0.0 True
0.29 True
0.66 True
0.0 False
0.83 True
0.72 False
0.15 False
0.0 True
0.0 False
0.98 True
0.0 True
0.98 False
0.86 True
0.0 False
0.43 False
0.15 True
0.15 True
0.84 True
0.85 True
0.93 False
0.99 False
0.99 True
0.02 False
0.98 True
0.0 False
0.0 True
0.87 True
0.99 False
0.93 False
0.71 False
0.96 True
0.94 True
0.2 True
0.98 False
0.0 True
0.49 True
0.98 False
0.47 True
0.98 False
0.77 False
0.13 True
0.3 True
0.96 True
0.98 True
0.91 False
0.96 True
0.98 False
0.56 True
0.0 False
0.5 True
0.03 False
0.0 True
0.0 False
0.84 True
0.58 True
0.0 False
0.01 True
0.0 True
1.0 False
0.04 True
0.97 True
0.86 False
0.94 True
0.88 True
0.51 True
0.0 True
0.96 False
0.73 True
0.0 True
0.09 True
0.0 True
0.0 False
0.99 False
0.55 True
0.01 False
0.02 False
0.0 False
0.1 False
0.84 True
0.0 True
0.92 True
0.0 True
0.69 False
0.43 True
0.0 True
0.89 True
0.0 True
0.98 True
0.41 True
0.85 False
0.05 False
0.74 False
0.94 False
0.98 True
0.97 False
0.0 False
0.01 True
0.0 True
0.78 True
0.07 True
0.13 True
0.0 True
0.98 False
0.18 False
0.0 True
0.0 True
0.09 True
0.04 True
0.19 False
0.0 False
0.05 False
0.02 True
0.02 False
0.91 True
0.98 False
0.97 True
0.36 True
0.96 True
0.61 True
0.01 True
0.77 True
0.98 True
0.99 True
0.98 True
0.04 True
0.98 False
0.64 False
0.0 False
0.87 False
0.01 False
0.0 False
0.39 True
1.0 False
0.03 True
0.0 True
0.0 False
0.56 False
0.98 True
0.07 False
Traceback (most recent call last):
File "/accounts/projects/binyu/timothygao/Benign-Perturbation-Attack/sensitivity_v_accuracy.py", line 120, in <module>
perturbed_responses, _ = get_next_token(perturbed_prompts)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/accounts/projects/binyu/timothygao/Benign-Perturbation-Attack/sensitivity_v_accuracy.py", line 49, in get_next_token
outputs = model(**inputs)
^^^^^^^^^^^^^^^
File "/scratch/users/timothygao/conda/envs/semantic_uncertainty/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/scratch/users/timothygao/conda/envs/semantic_uncertainty/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/accounts/projects/binyu/timothygao/.local/lib/python3.11/site-packages/transformers/models/llama/modeling_llama.py", line 1194, in forward
logits = logits.float()
^^^^^^^^^^^^^^
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 27.90 GiB. GPU