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peacock-data-public-datasets-idc-cronscript

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+
2
+ 2024-05-14:10:29:58,611 INFO [__main__.py:251] Verbosity set to INFO
3
+ 2024-05-14:10:30:03,656 INFO [__main__.py:335] Selected Tasks: ['indiccopa-hi']
4
+ 2024-05-14:10:30:03,660 INFO [evaluator.py:131] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234
5
+ 2024-05-14:10:30:03,661 INFO [evaluator.py:177] Initializing hf model, with arguments: {'pretrained': '/data/cronscript/ckpts//hf_ckpt//global_step100'}
6
+ /usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/gpu_migration/core/register.py:145: UserWarning: "hpu:X" notation is not supported by Gaudi PyTorch intergration bridge. Please change to "hpu" without index (Triggered internally at /npu-stack/pytorch-integration/pytorch_helpers/lazy_to_backend.cpp:53.)
7
+ return func(*args, **kwargs)
8
+ /usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/gpu_migration/torch/cuda/memory.py:36: UserWarning: No need to call empty_cache on HPU. It manages the memory internally in an effcient way.
9
+ warnings.warn(
10
+ [2024-05-14 10:30:12,308] [INFO] [real_accelerator.py:178:get_accelerator] Setting ds_accelerator to hpu (auto detect)
11
+ /usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/hpu/__init__.py:158: UserWarning: torch.hpu.setDeterministic is deprecated and will be removed in next release. Please use torch.use_deterministic_algorithms instead.
12
+ warnings.warn(
13
+ You are using the default legacy behaviour of the <class 'transformers.models.llama.tokenization_llama.LlamaTokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565
14
+ 2024-05-14:10:30:12,692 WARNING [task.py:763] [Task: indiccopa-hi] metric acc is defined, but aggregation is not. using default aggregation=mean
15
+ 2024-05-14:10:30:12,693 WARNING [task.py:775] [Task: indiccopa-hi] metric acc is defined, but higher_is_better is not. using default higher_is_better=True
16
+ /usr/local/lib/python3.10/dist-packages/datasets/load.py:1486: FutureWarning: The repository for ai4bharat/IndicCOPA contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/ai4bharat/IndicCOPA
17
+ You can avoid this message in future by passing the argument `trust_remote_code=True`.
18
+ Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.
19
+ warnings.warn(
20
+ Downloading builder script: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.30k/2.30k [00:00<00:00, 10.0MB/s]
21
+ Downloading readme: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.89k/2.89k [00:00<00:00, 20.9MB/s]
22
+ Downloading data: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 152k/152k [00:00<00:00, 39.3MB/s]
23
+ Generating test split: 449 examples [00:00, 23050.70 examples/s]
24
+ 2024-05-14:10:30:14,992 WARNING [task.py:322] [Task: indiccopa-hi] has_training_docs and has_validation_docs are False, using test_docs as fewshot_docs but this is not recommended.
25
+ 2024-05-14:10:30:14,992 WARNING [task.py:322] [Task: indiccopa-hi] has_training_docs and has_validation_docs are False, using test_docs as fewshot_docs but this is not recommended.
26
+ 2024-05-14:10:30:15,012 INFO [task.py:395] Building contexts for indiccopa-hi on rank 1...
27
+ 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 56/56 [00:00<00:00, 115819.05it/s]
28
+ Passed argument batch_size = auto:1. Detecting largest batch size
29
+ Determined largest batch size: 64
30
+ 2024-05-14:10:30:16,730 INFO [evaluator.py:379] Running loglikelihood requests
31
+ /usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/gpu_migration/torch/cuda/memory.py:36: UserWarning: No need to call empty_cache on HPU. It manages the memory internally in an effcient way.
32
+ warnings.warn(
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+ framework: huggingface
9
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lm-evaluation-harness/wandb/run-20240522_173609-hwmcfdil/files/output.log ADDED
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1
+
2
+ 2024-05-22:17:36:10,052 INFO [__main__.py:251] Verbosity set to INFO
3
+ 2024-05-22:17:36:18,537 INFO [__main__.py:335] Selected Tasks: ['arc_easy', 'hellaswag', 'mrpc', 'openbookqa', 'sst2', 'winogrande']
4
+ 2024-05-22:17:36:18,538 INFO [evaluator.py:131] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234
5
+ 2024-05-22:17:36:18,538 INFO [evaluator.py:177] Initializing hf model, with arguments: {'pretrained': '/mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step100'}
6
+ 2024-05-22:17:36:20,826 INFO [huggingface.py:164] Using device 'cuda'
7
+ Traceback (most recent call last):
8
+ File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
9
+ return _run_code(code, main_globals, None,
10
+ File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
11
+ exec(code, run_globals)
12
+ File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 417, in <module>
13
+ cli_evaluate()
14
+ File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 341, in cli_evaluate
15
+ results = evaluator.simple_evaluate(
16
+ File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/utils.py", line 288, in _wrapper
17
+ return fn(*args, **kwargs)
18
+ File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/evaluator.py", line 180, in simple_evaluate
19
+ lm = lm_eval.api.registry.get_model(model).create_from_arg_string(
20
+ File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/api/model.py", line 134, in create_from_arg_string
21
+ return cls(**args, **args2)
22
+ File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 190, in __init__
23
+ self._get_config(
24
+ File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 471, in _get_config
25
+ self._config = transformers.AutoConfig.from_pretrained(
26
+ File "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py", line 934, in from_pretrained
27
+ config_dict, unused_kwargs = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
28
+ File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 632, in get_config_dict
29
+ config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)
30
+ File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 689, in _get_config_dict
31
+ resolved_config_file = cached_file(
32
+ File "/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py", line 370, in cached_file
33
+ raise EnvironmentError(
34
+ OSError: /mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step100 does not appear to have a file named config.json. Checkout 'https://huggingface.co//mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step100/tree/main' for available files.
lm-evaluation-harness/wandb/run-20240522_173609-hwmcfdil/files/requirements.txt ADDED
@@ -0,0 +1,155 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ DataProperty==1.0.1
2
+ GitPython==3.1.43
3
+ Jinja2==3.1.4
4
+ Markdown==3.6
5
+ MarkupSafe==2.1.5
6
+ Pillow-SIMD==7.0.0.post3
7
+ PyYAML==6.0
8
+ Werkzeug==3.0.3
9
+ absl-py==2.1.0
10
+ accelerate==0.30.1
11
+ aiohttp==3.9.5
12
+ aiosignal==1.3.1
13
+ async-timeout==4.0.3
14
+ attrs==23.2.0
15
+ av==9.2.0
16
+ cachetools==5.3.3
17
+ certifi==2024.2.2
18
+ cffi==1.15.1
19
+ cfgv==3.4.0
20
+ chardet==5.2.0
21
+ charset-normalizer==3.3.2
22
+ click==8.1.7
23
+ cmake==3.29.2
24
+ colorama==0.4.6
25
+ datasets==2.19.1
26
+ deepspeed==0.12.4+hpu.synapse.v1.15.1
27
+ dill==0.3.8
28
+ distlib==0.3.8
29
+ docker-pycreds==0.4.0
30
+ einops==0.8.0
31
+ evaluate==0.4.2
32
+ exceptiongroup==1.2.1
33
+ expecttest==0.2.1
34
+ filelock==3.14.0
35
+ frozenlist==1.4.1
36
+ fsspec==2024.3.1
37
+ gitdb==4.0.11
38
+ google-auth-oauthlib==0.4.6
39
+ google-auth==2.29.0
40
+ grpcio==1.63.0
41
+ habana-media-loader==1.15.1.15
42
+ habana-pyhlml==1.15.1.15
43
+ habana-torch-dataloader==1.15.1.15
44
+ habana-torch-plugin==1.15.1.15
45
+ habana_gpu_migration==1.15.1.15
46
+ habana_quantization_toolkit==1.15.1.15
47
+ hjson==3.1.0
48
+ huggingface-hub==0.23.1
49
+ identify==2.5.36
50
+ idna==3.7
51
+ iniconfig==2.0.0
52
+ joblib==1.4.2
53
+ jsonlines==4.0.0
54
+ lightning-habana==1.4.0
55
+ lightning-utilities==0.11.2
56
+ lightning==2.2.0.post0
57
+ lm_eval==0.4.2
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+ {"columns": ["Tasks", "Version", "Filter", "num_fewshot", "Metric", "Value", "Stderr"], "data": [["winogrande", 1.0, "none", 0, "acc", "0.5043409629044988", "0.0141"], ["sst2", 1.0, "none", 0, "acc", "0.4701834862385321", "0.0169"], ["piqa", 1.0, "none", 0, "acc", "0.5239390642002176", "0.0117"], ["piqa", 1.0, "none", 0, "acc_norm", "0.5016322089227421", "0.0117"], ["mrpc", 1.0, "none", 0, "acc", "0.3161764705882353", "0.0230"], ["mrpc", 1.0, "none", 0, "f1", "0.0", "0.0000"], ["indic_boolq_hi", 1.0, "none", 0, "acc", "0.6217125382262997", "0.0085"], ["indic_arc_easy_hi", 1.0, "none", 0, "acc", "0.24873737373737373", "0.0089"], ["indic_arc_challenge_hi", 1.0, "none", 0, "acc", "0.21245733788395904", "0.0120"], ["copa", 1.0, "none", 0, "acc", "0.64", "0.0482"], ["boolq", 2.0, "none", 0, "acc", "0.3785932721712538", "0.0085"], ["arc_easy", 1.0, "none", 0, "acc", "0.25757575757575757", "0.0090"], ["arc_easy", 1.0, "none", 0, "acc_norm", "0.2622053872053872", "0.0090"]]}
lm-evaluation-harness/wandb/run-20240605_062502-fvj55jrc/files/output.log ADDED
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+ 2024-06-05:06:25:02,823 INFO [__main__.py:251] Verbosity set to INFO
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+ 2024-06-05:06:25:11,480 INFO [__main__.py:335] Selected Tasks: ['arc_easy', 'boolq', 'copa', 'indic_arc_challenge_hi', 'indic_arc_easy_hi', 'indic_boolq_hi', 'mrpc', 'piqa', 'sst2', 'winogrande']
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+ 2024-06-05:06:25:11,481 INFO [evaluator.py:131] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234
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+ 2024-06-05:06:25:11,481 INFO [evaluator.py:177] Initializing hf model, with arguments: {'pretrained': '/mnt/weka/peacock/experiments/llama/eval/checkpoint-enhibn/llamav2-3b/hf/global_step100000', 'tokenizer': '/mnt/weka/peacock/tokenization/trained-tokenizer/enhiben_50k_hf/ConvertedTokenizer'}
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+ 2024-06-05:06:25:13,786 INFO [huggingface.py:164] Using device 'cuda'
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+ /usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/gpu_migration/torch/cuda/memory.py:36: UserWarning: No need to call empty_cache on HPU. It manages the memory internally in an effcient way.
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+ warnings.warn(
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+ Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
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+ Downloading readme: 100%|██████████| 9.00k/9.00k [00:00<00:00, 8.70MB/s]
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+ Downloading data: 100%|██████████| 331k/331k [00:01<00:00, 190kB/s]
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+ Downloading data: 100%|██████████| 86.1k/86.1k [00:00<00:00, 549kB/s]
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+ Generating train split: 100%|██████████| 2251/2251 [00:00<00:00, 94186.79 examples/s]
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+ Generating test split: 100%|██████████| 2376/2376 [00:00<00:00, 337281.83 examples/s]
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+ Generating validation split: 100%|██████████| 570/570 [00:00<00:00, 152354.91 examples/s]
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+ 2024-06-05:06:25:42,662 WARNING [task.py:763] [Task: boolq] metric acc is defined, but aggregation is not. using default aggregation=mean
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+ 2024-06-05:06:25:42,662 WARNING [task.py:775] [Task: boolq] metric acc is defined, but higher_is_better is not. using default higher_is_better=True
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+ /usr/local/lib/python3.10/dist-packages/datasets/load.py:1491: FutureWarning: The repository for super_glue contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/super_glue
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+ You can avoid this message in future by passing the argument `trust_remote_code=True`.
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+ Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.
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+ warnings.warn(
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+ Downloading builder script: 100%|██████████| 30.7k/30.7k [00:00<00:00, 5.73MB/s]
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+ Downloading readme: 100%|██████████| 18.2k/18.2k [00:00<00:00, 30.0MB/s]
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+ Downloading data: 100%|██████████| 4.12M/4.12M [00:00<00:00, 14.7MB/s]
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+ Generating train split: 100%|██████████| 9427/9427 [00:00<00:00, 22339.43 examples/s]
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+ Generating validation split: 100%|██████████| 3270/3270 [00:00<00:00, 22580.06 examples/s]
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+ Generating test split: 100%|██████████| 3245/3245 [00:00<00:00, 23484.30 examples/s]
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+ 2024-06-05:06:25:47,185 WARNING [task.py:763] [Task: copa] metric acc is defined, but aggregation is not. using default aggregation=mean
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+ 2024-06-05:06:25:47,186 WARNING [task.py:775] [Task: copa] metric acc is defined, but higher_is_better is not. using default higher_is_better=True
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+ Downloading data: 100%|██████████| 44.0k/44.0k [00:00<00:00, 1.22MB/s]
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+ Generating train split: 100%|██████████| 400/400 [00:00<00:00, 16219.43 examples/s]
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+ Generating validation split: 100%|██████████| 100/100 [00:00<00:00, 12558.92 examples/s]
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+ Generating test split: 100%|██████████| 500/500 [00:00<00:00, 17216.44 examples/s]
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+ Downloading readme: 100%|██████████| 2.09k/2.09k [00:00<00:00, 5.00MB/s]
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+ Downloading data: 100%|██████████| 1.73M/1.73M [00:00<00:00, 5.21MB/s]
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+ Downloading data: 100%|██████████| 1.84M/1.84M [00:00<00:00, 5.05MB/s]
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+ Downloading data: 100%|██████████| 473k/473k [00:00<00:00, 3.82MB/s]
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+ Generating train split: 1119 examples [00:00, 21441.94 examples/s]
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+ Generating test split: 1172 examples [00:00, 23457.81 examples/s]
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+ Generating validation split: 299 examples [00:00, 9661.69 examples/s]
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+ 2024-06-05:06:25:53,640 WARNING [task.py:322] [Task: indic_arc_challenge_hi] has_training_docs and has_validation_docs are False, using test_docs as fewshot_docs but this is not recommended.
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+ 2024-06-05:06:25:53,641 WARNING [task.py:322] [Task: indic_arc_challenge_hi] has_training_docs and has_validation_docs are False, using test_docs as fewshot_docs but this is not recommended.
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+ Downloading readme: 100%|██████████| 2.19k/2.19k [00:00<00:00, 734kB/s]
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+ Downloading data: 100%|██████████| 3.18M/3.18M [00:00<00:00, 10.7MB/s]
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+ Downloading data: 100%|███████���██| 3.37M/3.37M [00:00<00:00, 6.30MB/s]
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+ Downloading data: 100%|██████████| 808k/808k [00:00<00:00, 5.66MB/s]
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+ Generating train split: 100%|██████████| 2251/2251 [00:00<00:00, 31416.18 examples/s]
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+ Generating test split: 100%|██████████| 2376/2376 [00:00<00:00, 34218.52 examples/s]
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+ Generating validation split: 100%|██████████| 570/570 [00:00<00:00, 15785.86 examples/s]
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+ 2024-06-05:06:25:58,052 WARNING [task.py:322] [Task: indic_arc_easy_hi] has_training_docs and has_validation_docs are False, using test_docs as fewshot_docs but this is not recommended.
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+ 2024-06-05:06:25:58,053 WARNING [task.py:322] [Task: indic_arc_easy_hi] has_training_docs and has_validation_docs are False, using test_docs as fewshot_docs but this is not recommended.
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+ Downloading readme: 100%|██████████| 1.53k/1.53k [00:00<00:00, 3.95MB/s]
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+ Downloading data: 100%|██████████| 22.1M/22.1M [00:00<00:00, 27.2MB/s]
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+ Downloading data: 100%|██████████| 7.55M/7.55M [00:00<00:00, 29.4MB/s]
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+ Generating train split: 100%|██████████| 9427/9427 [00:00<00:00, 37071.66 examples/s]
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+ Generating validation split: 100%|██████████| 3270/3270 [00:00<00:00, 38738.19 examples/s]
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+ 2024-06-05:06:26:06,678 WARNING [task.py:763] [Task: mrpc] metric acc is defined, but aggregation is not. using default aggregation=mean
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+ 2024-06-05:06:26:06,679 WARNING [task.py:775] [Task: mrpc] metric acc is defined, but higher_is_better is not. using default higher_is_better=True
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+ 2024-06-05:06:26:06,679 WARNING [task.py:763] [Task: mrpc] metric f1 is defined, but aggregation is not. using default aggregation=f1
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+ 2024-06-05:06:26:06,679 WARNING [task.py:775] [Task: mrpc] metric f1 is defined, but higher_is_better is not. using default higher_is_better=True
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+ Downloading readme: 100%|██████████| 35.3k/35.3k [00:00<00:00, 500kB/s]
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+ Downloading data: 100%|██████████| 649k/649k [00:00<00:00, 2.16MB/s]
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+ Downloading data: 100%|██████████| 308k/308k [00:00<00:00, 1.07MB/s]
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+ Generating train split: 100%|██████████| 3668/3668 [00:00<00:00, 417359.53 examples/s]
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+ Generating validation split: 100%|██████████| 408/408 [00:00<00:00, 178686.02 examples/s]
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+ Generating test split: 100%|██████████| 1725/1725 [00:00<00:00, 391111.65 examples/s]
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+ /usr/local/lib/python3.10/dist-packages/datasets/load.py:1491: FutureWarning: The repository for piqa contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/piqa
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+ You can avoid this message in future by passing the argument `trust_remote_code=True`.
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+ Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.
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+ warnings.warn(
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+ Downloading builder script: 100%|██████████| 5.36k/5.36k [00:00<00:00, 11.5MB/s]
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+ Downloading readme: 100%|██████████| 8.41k/8.41k [00:00<00:00, 17.6MB/s]
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+ Downloading data: 100%|██████████| 1.82M/1.82M [00:00<00:00, 4.01MB/s]
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+ Downloading data: 100%|██████████| 815k/815k [00:00<00:00, 58.6MB/s]
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+ Generating train split: 100%|██████████| 16113/16113 [00:00<00:00, 24041.60 examples/s]
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+ Generating test split: 100%|██████████| 3084/3084 [00:00<00:00, 19688.93 examples/s]
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+ Generating validation split: 100%|██████████| 1838/1838 [00:00<00:00, 24041.60 examples/s]
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+ 2024-06-05:06:26:21,013 WARNING [task.py:763] [Task: sst2] metric acc is defined, but aggregation is not. using default aggregation=mean
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+ 2024-06-05:06:26:21,014 WARNING [task.py:775] [Task: sst2] metric acc is defined, but higher_is_better is not. using default higher_is_better=True
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+ Downloading data: 100%|██████████| 3.11M/3.11M [00:00<00:00, 8.47MB/s]
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+ Downloading data: 100%|██████████| 72.8k/72.8k [00:00<00:00, 256kB/s]
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+ Downloading data: 100%|██████████| 148k/148k [00:00<00:00, 529kB/s]
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+ Generating train split: 100%|██████████| 67349/67349 [00:00<00:00, 1428481.32 examples/s]
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+ Generating validation split: 100%|██████████| 872/872 [00:00<00:00, 370185.54 examples/s]
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+ Generating test split: 100%|██████████| 1821/1821 [00:00<00:00, 557464.97 examples/s]
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+ /usr/local/lib/python3.10/dist-packages/datasets/load.py:1491: FutureWarning: The repository for winogrande contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/winogrande
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+ You can avoid this message in future by passing the argument `trust_remote_code=True`.
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+ Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.
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+ warnings.warn(
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+ Downloading builder script: 100%|██████████| 5.65k/5.65k [00:00<00:00, 12.0MB/s]
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+ Downloading readme: 100%|██████████| 9.97k/9.97k [00:00<00:00, 20.4MB/s]
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+ Downloading data: 100%|██████████| 3.40M/3.40M [00:00<00:00, 6.68MB/s]
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+ Generating train split: 100%|██████████| 40398/40398 [00:01<00:00, 24503.24 examples/s]
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+ Generating test split: 100%|██████████| 1767/1767 [00:00<00:00, 23255.41 examples/s]
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+ Generating validation split: 100%|██████████| 1267/1267 [00:00<00:00, 24119.75 examples/s]
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+ 2024-06-05:06:26:36,145 INFO [task.py:395] Building contexts for winogrande on rank 0...
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+ 100%|██████████| 1267/1267 [00:00<00:00, 68521.48it/s]
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+ 2024-06-05:06:26:36,227 INFO [task.py:395] Building contexts for sst2 on rank 0...
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+ 100%|██████████| 872/872 [00:00<00:00, 2567.24it/s]
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+ 2024-06-05:06:26:36,596 INFO [task.py:395] Building contexts for piqa on rank 0...
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+ 100%|██████████| 1838/1838 [00:01<00:00, 1091.83it/s]
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+ 2024-06-05:06:26:38,354 INFO [task.py:395] Building contexts for mrpc on rank 0...
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+ 100%|██████████| 408/408 [00:00<00:00, 1854.45it/s]
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+ 2024-06-05:06:26:38,592 INFO [task.py:395] Building contexts for indic_boolq_hi on rank 0...
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+ 100%|██████████| 3270/3270 [00:00<00:00, 3707.04it/s]
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+ 2024-06-05:06:26:39,646 INFO [task.py:395] Building contexts for indic_arc_easy_hi on rank 0...
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+ 100%|██████████| 2376/2376 [00:02<00:00, 1045.99it/s]
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+ 2024-06-05:06:26:42,158 INFO [task.py:395] Building contexts for indic_arc_challenge_hi on rank 0...
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+ 100%|██████████| 1172/1172 [00:01<00:00, 1128.61it/s]
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+ 2024-06-05:06:26:43,314 INFO [task.py:395] Building contexts for copa on rank 0...
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+ 100%|██████████| 100/100 [00:00<00:00, 63015.38it/s]
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+ 2024-06-05:06:26:43,323 INFO [task.py:395] Building contexts for boolq on rank 0...
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+ 100%|██████████| 3270/3270 [00:01<00:00, 2010.56it/s]
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+ 2024-06-05:06:26:45,082 INFO [task.py:395] Building contexts for arc_easy on rank 0...
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+ 100%|██████████| 2376/2376 [00:02<00:00, 1158.69it/s]
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+ 2024-06-05:06:26:47,279 INFO [evaluator.py:379] Running loglikelihood requests
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+ Token indices sequence length is longer than the specified maximum sequence length for this model (1333 > 1024). Running this sequence through the model will result in indexing errors
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+ Running loglikelihood requests: 0%| | 0/45739 [00:00<?, ?it/s]
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+ Passed argument batch_size = auto:1. Detecting largest batch size
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+ Running loglikelihood requests: 100%|██████████| 45739/45739 [1:16:15<00:00, 10.00it/s]
809
+ 0%| | 0/100 [00:00<?, ?it/s]
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+
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+
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+ 100%|██████████| 100/100 [02:07<00:00, 1.28s/it]
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+ hf (pretrained=/mnt/weka/peacock/experiments/llama/eval/checkpoint-enhibn/llamav2-3b/hf/global_step100000,tokenizer=/mnt/weka/peacock/tokenization/trained-tokenizer/enhiben_50k_hf/ConvertedTokenizer), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: auto (64)
815
+ | Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
816
+ |----------------------|------:|------|-----:|--------|-----:|---|-----:|
817
+ |winogrande | 1|none | 0|acc |0.5043|± |0.0141|
818
+ |sst2 | 1|none | 0|acc |0.4702|± |0.0169|
819
+ |piqa | 1|none | 0|acc |0.5239|± |0.0117|
820
+ | | |none | 0|acc_norm|0.5016|± |0.0117|
821
+ |mrpc | 1|none | 0|acc |0.3162|± |0.0230|
822
+ | | |none | 0|f1 |0.0000|± |0.0000|
823
+ |indic_boolq_hi | 1|none | 0|acc |0.6217|± |0.0085|
824
+ |indic_arc_easy_hi | 1|none | 0|acc |0.2487|± |0.0089|
825
+ |indic_arc_challenge_hi| 1|none | 0|acc |0.2125|± |0.0120|
826
+ |copa | 1|none | 0|acc |0.6400|± |0.0482|
827
+ |boolq | 2|none | 0|acc |0.3786|± |0.0085|
828
+ |arc_easy | 1|none | 0|acc |0.2576|± |0.0090|
829
+ | | |none | 0|acc_norm|0.2622|± |0.0090|
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141
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lm-evaluation-harness/wandb/run-20240605_062502-fvj55jrc/files/wandb-metadata.json ADDED
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13
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14
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+ {"winogrande/alias": "winogrande", "sst2/alias": "sst2", "piqa/alias": "piqa", "mrpc/alias": "mrpc", "indic_boolq_hi/alias": "indic_boolq_hi", "indic_arc_easy_hi/alias": "indic_arc_easy_hi", "indic_arc_challenge_hi/alias": "indic_arc_challenge_hi", "copa/alias": "copa", "boolq/alias": "boolq", "arc_easy/alias": "arc_easy", "winogrande/acc": 0.5043409629044988, "winogrande/acc_stderr": 0.014051956064076884, "sst2/acc": 0.4701834862385321, "sst2/acc_stderr": 0.016911703415318852, "piqa/acc": 0.5239390642002176, "piqa/acc_stderr": 0.01165244562107926, "piqa/acc_norm": 0.5016322089227421, "piqa/acc_norm_stderr": 0.011665762007194871, "mrpc/acc": 0.3161764705882353, "mrpc/acc_stderr": 0.023048336668420193, "mrpc/f1": 0.0, "mrpc/f1_stderr": 0.0, "indic_boolq_hi/acc": 0.6217125382262997, "indic_boolq_hi/acc_stderr": 0.008482001133930994, "indic_arc_easy_hi/acc": 0.24873737373737373, "indic_arc_easy_hi/acc_stderr": 0.008870224411653797, "indic_arc_challenge_hi/acc": 0.21245733788395904, "indic_arc_challenge_hi/acc_stderr": 0.011953482906582952, "copa/acc": 0.64, "copa/acc_stderr": 0.048241815132442176, "boolq/acc": 0.3785932721712538, "boolq/acc_stderr": 0.008483341718024479, "arc_easy/acc": 0.25757575757575757, "arc_easy/acc_stderr": 0.008973187820215217, "arc_easy/acc_norm": 0.2622053872053872, "arc_easy/acc_norm_stderr": 0.009025197991724814, "_timestamp": 1717573573.6817842, "_runtime": 4871.475170135498, "_step": 1, "evaluation/eval_results": {"_type": "table-file", "sha256": "c39128d55f7290eec15d3fd47e451623ea8df782e0ca56b6308021438f438c4b", "size": 981, "artifact_path": "wandb-client-artifact://12ziyy33vv10gvg607fcndxqgkrkopsvtpzdewoq2jaffd7d7tyfvj9vlozwwebdidfpeoagq7cv2xp99ys14b5odtzv1iqernnja9gls8aogdqh654yzx1423j65lfq/evaluation/eval_results.table.json", "_latest_artifact_path": "wandb-client-artifact://ct51pmo9ejhp8auy3oc7hxyul0zynx2qi0xwcgwokpgn2gijycb4ty3rcwozvpwnbkji0tbu2705mx6tkydjnv88s7bitfykbiqo4b1guwdbujc17s8pq6ywq5alzuze:latest/evaluation/eval_results.table.json", "path": "media/table/evaluation/eval_results_1_c39128d55f7290eec15d.table.json", "ncols": 7, "nrows": 13}, "_wandb": {"runtime": 4871}}
venv/lib/python3.10/site-packages/triton/compiler/__init__.py ADDED
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1
+ from .compiler import (CompiledKernel, ASTSource, compile, AttrsDescriptor)
2
+ from .errors import CompilationError
3
+
4
+ __all__ = [
5
+ "compile", "ASTSource", "AttrsDescriptor", "CompiledKernel", "CompilationError", "get_arch_default_num_warps",
6
+ "get_arch_default_num_stages"
7
+ ]
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