Add files using upload-large-folder tool

.gitattributes

@@ -89,3 +89,4 @@ venv/lib/python3.10/site-packages/nvidia/cuda_cupti/lib/libnvperf_target.so filt
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+venv/lib/python3.10/site-packages/nvidia/cudnn/lib/libcudnn_ops_train.so.8 filter=lfs diff=lfs merge=lfs -text

ckpts/universal/global_step20/zero/8.mlp.dense_h_to_4h_swiglu.weight/exp_avg.pt

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lm-evaluation-harness/wandb/run-20240514_103117-v2q64sh9/files/config.yaml

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+  value:
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+    cli_version: 0.17.0
+    framework: huggingface
+    huggingface_version: 4.40.2
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+    is_kaggle_kernel: false
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lm-evaluation-harness/wandb/run-20240514_103117-v2q64sh9/files/output.log

@@ -0,0 +1,42 @@
+
+2024-05-14:10:31:17,663 INFO     [__main__.py:251] Verbosity set to INFO
+2024-05-14:10:31:22,060 INFO     [__main__.py:335] Selected Tasks: ['indiccopa-hi']
+2024-05-14:10:31:22,063 INFO     [evaluator.py:131] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234
+2024-05-14:10:31:22,063 INFO     [evaluator.py:177] Initializing hf model, with arguments: {'pretrained': '/data/cronscript/ckpts//hf_ckpt//global_step20'}
+Traceback (most recent call last):
+  File "/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py", line 398, in cached_file
+    resolved_file = hf_hub_download(
+  File "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py", line 106, in _inner_fn
+    validate_repo_id(arg_value)
+  File "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py", line 154, in validate_repo_id
+    raise HFValidationError(
+huggingface_hub.errors.HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/data/cronscript/ckpts//hf_ckpt//global_step20'. Use `repo_type` argument if needed.
+The above exception was the direct cause of the following exception:
+Traceback (most recent call last):
+  File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
+    return _run_code(code, main_globals, None,
+  File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
+    exec(code, run_globals)
+  File "/data/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 417, in <module>
+    cli_evaluate()
+  File "/data/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 341, in cli_evaluate
+    results = evaluator.simple_evaluate(
+  File "/data/cronscript/lm-evaluation-harness/lm_eval/utils.py", line 288, in _wrapper
+    return fn(*args, **kwargs)
+  File "/data/cronscript/lm-evaluation-harness/lm_eval/evaluator.py", line 180, in simple_evaluate
+    lm = lm_eval.api.registry.get_model(model).create_from_arg_string(
+  File "/data/cronscript/lm-evaluation-harness/lm_eval/api/model.py", line 134, in create_from_arg_string
+    return cls(**args, **args2)
+  File "/data/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 190, in __init__
+    self._get_config(
+  File "/data/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 471, in _get_config
+    self._config = transformers.AutoConfig.from_pretrained(
+  File "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py", line 928, in from_pretrained
+    config_dict, unused_kwargs = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
+  File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 631, in get_config_dict
+    config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)
+  File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 686, in _get_config_dict
+    resolved_config_file = cached_file(
+  File "/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py", line 462, in cached_file
+    raise EnvironmentError(
+OSError: Incorrect path_or_model_id: '/data/cronscript/ckpts//hf_ckpt//global_step20'. Please provide either the path to a local folder or the repo_id of a model on the Hub.

lm-evaluation-harness/wandb/run-20240514_103117-v2q64sh9/files/requirements.txt

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+pip==23.3.1
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+pluggy==1.4.0
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+pre-commit==3.3.3
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+pyarrow==16.0.0
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+pytest==8.1.1
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+word2number==1.1
+xxhash==3.4.1
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+2024-05-14 10:31:17,048 INFO    MainThread:8781 [wandb_setup.py:_flush():76] Configure stats pid to 8781
+2024-05-14 10:31:17,048 INFO    MainThread:8781 [wandb_setup.py:_flush():76] Loading settings from /root/.config/wandb/settings
+2024-05-14 10:31:17,048 INFO    MainThread:8781 [wandb_setup.py:_flush():76] Loading settings from /data/cronscript/lm-evaluation-harness/wandb/settings
+2024-05-14 10:31:17,048 INFO    MainThread:8781 [wandb_setup.py:_flush():76] Loading settings from environment variables: {}
+2024-05-14 10:31:17,048 INFO    MainThread:8781 [wandb_setup.py:_flush():76] Applying setup settings: {'_disable_service': False}
+2024-05-14 10:31:17,048 WARNING MainThread:8781 [wandb_setup.py:_flush():76] Could not find program at -m lm_eval.__main__
+2024-05-14 10:31:17,048 INFO    MainThread:8781 [wandb_setup.py:_flush():76] Inferring run settings from compute environment: {'program_relpath': None, 'program': '-m lm_eval.__main__'}
+2024-05-14 10:31:17,048 INFO    MainThread:8781 [wandb_setup.py:_flush():76] Applying login settings: {}
+2024-05-14 10:31:17,048 INFO    MainThread:8781 [wandb_init.py:_log_setup():520] Logging user logs to /data/cronscript/lm-evaluation-harness/wandb/run-20240514_103117-v2q64sh9/logs/debug.log
+2024-05-14 10:31:17,048 INFO    MainThread:8781 [wandb_init.py:_log_setup():521] Logging internal logs to /data/cronscript/lm-evaluation-harness/wandb/run-20240514_103117-v2q64sh9/logs/debug-internal.log
+2024-05-14 10:31:17,049 INFO    MainThread:8781 [wandb_init.py:init():560] calling init triggers
+2024-05-14 10:31:17,049 INFO    MainThread:8781 [wandb_init.py:init():567] wandb.init called with sweep_config: {}
+config: {}
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+2024-05-14 10:31:17,049 INFO    MainThread:8781 [wandb_init.py:init():614] setting up manager
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+2024-05-14 10:31:17,437 INFO    MainThread:8781 [wandb_init.py:init():795] starting run threads in backend
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+2024-05-14 10:31:17,660 INFO    MainThread:8781 [wandb_run.py:_redirect():2229] redirect: wrap_raw
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+2024-05-14 10:31:17,662 INFO    MainThread:8781 [wandb_init.py:init():838] run started, returning control to user process

lm-evaluation-harness/wandb/run-20240522_174353-q6n3ywdp/files/config.yaml

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+wandb_version: 1
+
+_wandb:
+  desc: null
+  value:
+    python_version: 3.10.12
+    cli_version: 0.17.0
+    framework: huggingface
+    huggingface_version: 4.41.0
+    is_jupyter_run: false
+    is_kaggle_kernel: false
+    start_time: 1716399833
+    t:
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+      8:
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lm-evaluation-harness/wandb/run-20240522_174353-q6n3ywdp/files/output.log

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+
+2024-05-22:17:43:54,109 INFO     [__main__.py:251] Verbosity set to INFO
+2024-05-22:17:44:02,733 INFO     [__main__.py:335] Selected Tasks: ['arc_easy', 'hellaswag', 'mrpc', 'openbookqa', 'sst2', 'winogrande']
+2024-05-22:17:44:02,734 INFO     [evaluator.py:131] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234
+2024-05-22:17:44:02,735 INFO     [evaluator.py:177] Initializing hf model, with arguments: {'pretrained': '/mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step100'}
+2024-05-22:17:44:05,037 INFO     [huggingface.py:164] Using device 'cuda'
+Traceback (most recent call last):
+  File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
+    return _run_code(code, main_globals, None,
+  File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
+    exec(code, run_globals)
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 417, in <module>
+    cli_evaluate()
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 341, in cli_evaluate
+    results = evaluator.simple_evaluate(
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/utils.py", line 288, in _wrapper
+    return fn(*args, **kwargs)
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/evaluator.py", line 180, in simple_evaluate
+    lm = lm_eval.api.registry.get_model(model).create_from_arg_string(
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/api/model.py", line 134, in create_from_arg_string
+    return cls(**args, **args2)
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 190, in __init__
+    self._get_config(
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 471, in _get_config
+    self._config = transformers.AutoConfig.from_pretrained(
+  File "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py", line 934, in from_pretrained
+    config_dict, unused_kwargs = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
+  File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 632, in get_config_dict
+    config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)
+  File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 689, in _get_config_dict
+    resolved_config_file = cached_file(
+  File "/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py", line 370, in cached_file
+    raise EnvironmentError(
+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_174353-q6n3ywdp/logs/debug-internal.log

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+2024-05-22 17:43:53,707 INFO    SenderThread:800 [dir_watcher.py:__init__():211] watching files in: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240522_174353-q6n3ywdp/files
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+2024-05-22 17:43:53,832 DEBUG   HandlerThread:800 [system_info.py:__init__():26] System info init
+2024-05-22 17:43:53,832 DEBUG   HandlerThread:800 [system_info.py:__init__():41] System info init done
+2024-05-22 17:43:53,832 INFO    HandlerThread:800 [system_monitor.py:start():194] Starting system monitor
+2024-05-22 17:43:53,832 INFO    SystemMonitor:800 [system_monitor.py:_start():158] Starting system asset monitoring threads
+2024-05-22 17:43:53,832 INFO    HandlerThread:800 [system_monitor.py:probe():214] Collecting system info
+2024-05-22 17:43:53,840 INFO    SystemMonitor:800 [interfaces.py:start():188] Started cpu monitoring
+2024-05-22 17:43:53,840 INFO    SystemMonitor:800 [interfaces.py:start():188] Started disk monitoring
+2024-05-22 17:43:53,842 INFO    SystemMonitor:800 [interfaces.py:start():188] Started memory monitoring
+2024-05-22 17:43:53,842 INFO    SystemMonitor:800 [interfaces.py:start():188] Started network monitoring
+2024-05-22 17:43:53,906 DEBUG   HandlerThread:800 [system_info.py:probe():150] Probing system
+2024-05-22 17:43:53,909 DEBUG   HandlerThread:800 [system_info.py:_probe_git():135] Probing git
+2024-05-22 17:43:53,919 ERROR   HandlerThread:800 [gitlib.py:root():92] git root error: Cmd('git') failed due to: exit code(128)
+  cmdline: git rev-parse --show-toplevel
+  stderr: 'fatal: detected dubious ownership in repository at '/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness'
+To add an exception for this directory, call:
+
+	git config --global --add safe.directory /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness'
+2024-05-22 17:43:53,920 DEBUG   HandlerThread:800 [system_info.py:_probe_git():143] Probing git done
+2024-05-22 17:43:53,920 DEBUG   HandlerThread:800 [system_info.py:probe():198] Probing system done
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lm-evaluation-harness/wandb/run-20240522_185537-khb0dhn0/files/output.log

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+
+2024-05-22:18:55:38,331 INFO     [__main__.py:251] Verbosity set to INFO
+2024-05-22:18:55:46,805 INFO     [__main__.py:335] Selected Tasks: ['arc_easy', 'hellaswag', 'mrpc', 'openbookqa', 'sst2', 'winogrande']
+2024-05-22:18:55:46,806 INFO     [evaluator.py:131] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234
+2024-05-22:18:55:46,807 INFO     [evaluator.py:177] Initializing hf model, with arguments: {'pretrained': '/mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step2000'}
+2024-05-22:18:55:49,108 INFO     [huggingface.py:164] Using device 'cuda'
+Traceback (most recent call last):
+  File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
+    return _run_code(code, main_globals, None,
+  File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
+    exec(code, run_globals)
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 417, in <module>
+    cli_evaluate()
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 341, in cli_evaluate
+    results = evaluator.simple_evaluate(
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/utils.py", line 288, in _wrapper
+    return fn(*args, **kwargs)
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/evaluator.py", line 180, in simple_evaluate
+    lm = lm_eval.api.registry.get_model(model).create_from_arg_string(
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/api/model.py", line 134, in create_from_arg_string
+    return cls(**args, **args2)
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 190, in __init__
+    self._get_config(
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 471, in _get_config
+    self._config = transformers.AutoConfig.from_pretrained(
+  File "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py", line 934, in from_pretrained
+    config_dict, unused_kwargs = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
+  File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 632, in get_config_dict
+    config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)
+  File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 689, in _get_config_dict
+    resolved_config_file = cached_file(
+  File "/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py", line 370, in cached_file
+    raise EnvironmentError(
+OSError: /mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step2000 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_step2000/tree/main' for available files.

lm-evaluation-harness/wandb/run-20240522_185537-khb0dhn0/files/requirements.txt

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+oauthlib==3.2.2
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+pandas==2.0.1
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+pathvalidate==3.2.0
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+perfetto==0.7.0
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+pip==22.0.2
+pip==23.3.1
+platformdirs==4.2.1
+pluggy==1.5.0
+portalocker==2.8.2
+pre-commit==3.3.3
+pretty-errors==1.2.25
+protobuf==3.20.3
+psutil==5.9.8
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+pydantic==1.10.13
+pynvml==8.0.4
+pytablewriter==1.2.0
+pytest==8.2.0
+python-dateutil==2.9.0.post0
+pytorch-lightning==2.2.4
+pytz==2024.1
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+requests-oauthlib==2.0.0
+requests==2.31.0
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+torchdata==0.7.1+5e6f7b7
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+torchvision==0.17.0+b2383d4
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+tzdata==2024.1
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lm-evaluation-harness/wandb/run-20240522_185909-bhoogr15/files/output.log

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+
+2024-05-22:18:59:09,922 INFO     [__main__.py:251] Verbosity set to INFO
+2024-05-22:18:59:18,441 INFO     [__main__.py:335] Selected Tasks: ['arc_easy', 'hellaswag', 'mrpc', 'openbookqa', 'sst2', 'winogrande']
+2024-05-22:18:59:18,442 INFO     [evaluator.py:131] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234
+2024-05-22:18:59:18,442 INFO     [evaluator.py:177] Initializing hf model, with arguments: {'pretrained': '/mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step30000'}
+2024-05-22:18:59:20,782 INFO     [huggingface.py:164] Using device 'cuda'
+Traceback (most recent call last):
+  File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
+    return _run_code(code, main_globals, None,
+  File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
+    exec(code, run_globals)
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 417, in <module>
+    cli_evaluate()
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 341, in cli_evaluate
+    results = evaluator.simple_evaluate(
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/utils.py", line 288, in _wrapper
+    return fn(*args, **kwargs)
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/evaluator.py", line 180, in simple_evaluate
+    lm = lm_eval.api.registry.get_model(model).create_from_arg_string(
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/api/model.py", line 134, in create_from_arg_string
+    return cls(**args, **args2)
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 190, in __init__
+    self._get_config(
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 471, in _get_config
+    self._config = transformers.AutoConfig.from_pretrained(
+  File "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py", line 934, in from_pretrained
+    config_dict, unused_kwargs = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
+  File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 632, in get_config_dict
+    config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)
+  File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 689, in _get_config_dict
+    resolved_config_file = cached_file(
+  File "/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py", line 370, in cached_file
+    raise EnvironmentError(
+OSError: /mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step30000 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_step30000/tree/main' for available files.

lm-evaluation-harness/wandb/run-20240522_185909-bhoogr15/files/requirements.txt

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+GitPython==3.1.43
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+datasets==2.19.1
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+dill==0.3.8
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+docker-pycreds==0.4.0
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+evaluate==0.4.2
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+expecttest==0.2.1
+filelock==3.14.0
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+fsspec==2024.3.1
+gitdb==4.0.11
+google-auth-oauthlib==0.4.6
+google-auth==2.29.0
+grpcio==1.63.0
+habana-media-loader==1.15.1.15
+habana-pyhlml==1.15.1.15
+habana-torch-dataloader==1.15.1.15
+habana-torch-plugin==1.15.1.15
+habana_gpu_migration==1.15.1.15
+habana_quantization_toolkit==1.15.1.15
+hjson==3.1.0
+huggingface-hub==0.23.1
+identify==2.5.36
+idna==3.7
+iniconfig==2.0.0
+joblib==1.4.2
+jsonlines==4.0.0
+lightning-habana==1.4.0
+lightning-utilities==0.11.2
+lightning==2.2.0.post0
+lm_eval==0.4.2
+lm_eval==0.4.2
+lm_eval==0.4.2
+lxml==5.2.2
+mbstrdecoder==1.1.3
+more-itertools==10.2.0
+mpi4py==3.1.4
+mpmath==1.3.0
+multidict==6.0.5
+multiprocess==0.70.16
+networkx==3.3
+ninja==1.11.1.1
+nltk==3.8.1
+nodeenv==1.8.0
+numexpr==2.10.0
+numpy==1.23.5
+oauthlib==3.2.2
+packaging==24.0
+pandas==2.0.1
+pathspec==0.12.1
+pathvalidate==3.2.0
+peft==0.11.1
+perfetto==0.7.0
+pillow==10.3.0
+pip==22.0.2
+pip==23.3.1
+platformdirs==4.2.1
+pluggy==1.5.0
+portalocker==2.8.2
+pre-commit==3.3.3
+pretty-errors==1.2.25
+protobuf==3.20.3
+psutil==5.9.8
+py-cpuinfo==9.0.0
+pyarrow-hotfix==0.6
+pyarrow==16.1.0
+pyasn1==0.6.0
+pyasn1_modules==0.4.0
+pybind11==2.10.4
+pycparser==2.22
+pydantic==1.10.13
+pynvml==8.0.4
+pytablewriter==1.2.0
+pytest==8.2.0
+python-dateutil==2.9.0.post0
+pytorch-lightning==2.2.4
+pytz==2024.1
+regex==2023.5.5
+requests-oauthlib==2.0.0
+requests==2.31.0
+rouge_score==0.1.2
+rsa==4.9
+sacrebleu==2.4.2
+safetensors==0.4.3
+scikit-learn==1.5.0
+scipy==1.13.0
+sentencepiece==0.2.0
+sentry-sdk==2.2.1
+setproctitle==1.3.3
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+six==1.16.0
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+sympy==1.12
+tabledata==1.3.3
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+tcolorpy==0.1.6
+tdqm==0.0.1
+tensorboard-data-server==0.6.1
+tensorboard-plugin-wit==1.8.1
+tensorboard==2.11.2
+threadpoolctl==3.5.0
+tokenizers==0.19.1
+tomli==2.0.1
+torch==2.2.0a0+git8964477
+torch_tb_profiler==0.4.0
+torchaudio==2.2.0+08901ad
+torchdata==0.7.1+5e6f7b7
+torchmetrics==1.4.0
+torchtext==0.17.0+400da5c
+torchvision==0.17.0+b2383d4
+tqdm-multiprocess==0.0.11
+tqdm==4.66.4
+transformers==4.41.0
+typepy==1.3.2
+typing_extensions==4.11.0
+tzdata==2024.1
+urllib3==1.26.18
+virtualenv==20.26.1
+wandb==0.17.0
+wheel==0.37.1
+wheel==0.43.0
+word2number==1.1
+xxhash==3.4.1
+yamllint==1.35.1
+yarl==1.9.4
+zstandard==0.22.0

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+2024-05-22 18:59:09,727 ERROR   HandlerThread:3847 [gitlib.py:root():92] git root error: Cmd('git') failed due to: exit code(128)
+  cmdline: git rev-parse --show-toplevel
+  stderr: 'fatal: detected dubious ownership in repository at '/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness'
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+
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+      dataset_name: ARC-Easy
+      training_split: train
+      validation_split: validation
+      test_split: test
+      doc_to_text: 'Question: {{question}}
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+        Answer:'
+      doc_to_target: '{{choices.label.index(answerKey)}}'
+      doc_to_choice: '{{choices.text}}'
+      description: ''
+      target_delimiter: ' '
+      fewshot_delimiter: '
+
+
+        '
+      num_fewshot: 0
+      metric_list:
+      - metric: acc
+        aggregation: mean
+        higher_is_better: true
+      - metric: acc_norm
+        aggregation: mean
+        higher_is_better: true
+      output_type: multiple_choice
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+      should_decontaminate: true
+      doc_to_decontamination_query: 'Question: {{question}}
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+        Answer:'
+      metadata:
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+      dataset_name: boolq
+      training_split: train
+      validation_split: validation
+      doc_to_text: '{{passage}}
+
+        Question: {{question}}?
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+        Answer:'
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+      doc_to_choice:
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+      - 'yes'
+      description: ''
+      target_delimiter: ' '
+      fewshot_delimiter: '
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+
+        '
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+      metric_list:
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+      repeats: 1
+      should_decontaminate: true
+      doc_to_decontamination_query: passage
+      metadata:
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+      dataset_path: super_glue
+      dataset_name: copa
+      training_split: train
+      validation_split: validation
+      doc_to_text: "def doc_to_text(doc):\n    # Drop the period\n    connector =\
+        \ {\n        \"cause\": \"because\",\n        \"effect\": \"therefore\",\n\
+        \    }[doc[\"question\"]]\n    return doc[\"premise\"].strip()[:-1] + f\"\
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+      doc_to_target: "def doc_to_target(doc):\n    correct_choice = doc[\"choice1\"\
+        ] if doc[\"label\"] == 0 else doc[\"choice2\"]\n    # Connect the sentences\n\
+        \    return \" \" + convert_choice(correct_choice)\n"
+      doc_to_choice: "def doc_to_choice(doc):\n    return [\" \" + convert_choice(doc[\"\
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+      description: ''
+      target_delimiter: ' '
+      fewshot_delimiter: '
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+
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+      metric_list:
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+      output_type: multiple_choice
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+      metadata:
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+      dataset_name: mrpc
+      training_split: train
+      validation_split: validation
+      doc_to_text: 'Sentence 1: {{sentence1}}
+
+        Sentence 2: {{sentence2}}
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+        Question: Do both sentences mean the same thing?
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+        Answer:'
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+
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+      metadata:
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+      training_split: train
+      validation_split: validation
+      doc_to_text: 'Question: {{goal}}
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+        Answer:'
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+      doc_to_choice: '{{[sol1, sol2]}}'
+      description: ''
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+
+        '
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+      should_decontaminate: true
+      doc_to_decontamination_query: goal
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+      dataset_path: glue
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+      validation_split: validation
+      doc_to_text: '{{sentence}}
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+        Question: Is this sentence positive or negative?
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+        Answer:'
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+      description: ''
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+
+        '
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+    winogrande:
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+      dataset_name: winogrande_xl
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+      validation_split: validation
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+      doc_to_target: "def doc_to_target(doc):\n    idx = doc[\"sentence\"].index(\"\
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+      doc_to_choice: "def doc_to_choice(doc):\n    idx = doc[\"sentence\"].index(\"\
+        _\")\n    options = [doc[\"option1\"], doc[\"option2\"]]\n    return [doc[\"\
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+
+        '
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+    model_args: pretrained=/mnt/weka/peacock/experiments/llama/eval/checkpoint-english/llamav2-3b/hf/global_step50000,tokenizer=/mnt/weka/peacock/tokenization/trained-tokenizer/enhiben_50k_hf/ConvertedTokenizer
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+2024-05-30:12:59:05,996 INFO     [__main__.py:335] Selected Tasks: ['arc_easy', 'boolq', 'copa', 'mrpc', 'piqa', 'sst2', 'winogrande']
+2024-05-30:12:59:05,997 INFO     [evaluator.py:131] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234
+2024-05-30:12:59:05,997 INFO     [evaluator.py:177] Initializing hf model, with arguments: {'pretrained': '/mnt/weka/peacock/experiments/llama/eval/checkpoint-english/llamav2-3b/hf/global_step50000', 'tokenizer': '/mnt/weka/peacock/tokenization/trained-tokenizer/enhiben_50k_hf/ConvertedTokenizer'}
+2024-05-30:12:59:08,342 INFO     [huggingface.py:164] Using device 'cuda'
+/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.
+  warnings.warn(
+Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
+Downloading readme: 100%|██████████| 9.00k/9.00k [00:00<00:00, 6.54MB/s]
+Downloading data: 100%|██████████| 331k/331k [00:00<00:00, 2.06MB/s]
+Downloading data: 100%|██████████| 346k/346k [00:00<00:00, 4.29MB/s]
+Downloading data: 100%|██████████| 86.1k/86.1k [00:00<00:00, 1.17MB/s]
+Generating train split: 100%|██████████| 2251/2251 [00:00<00:00, 48935.29 examples/s]
+Generating test split: 100%|██████████| 2376/2376 [00:00<00:00, 318341.04 examples/s]
+Generating validation split: 100%|██████████| 570/570 [00:00<00:00, 153904.55 examples/s]
+2024-05-30:12:59:38,975 WARNING  [task.py:763] [Task: boolq] metric acc is defined, but aggregation is not. using default aggregation=mean
+2024-05-30:12:59:38,975 WARNING  [task.py:775] [Task: boolq] metric acc is defined, but higher_is_better is not. using default higher_is_better=True
+/usr/local/lib/python3.10/dist-packages/datasets/load.py:1486: 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
+You can avoid this message in future by passing the argument `trust_remote_code=True`.
+Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.
+  warnings.warn(
+Downloading builder script: 100%|██████████| 30.7k/30.7k [00:00<00:00, 39.0MB/s]
+Downloading readme: 100%|██████████| 18.2k/18.2k [00:00<00:00, 31.0MB/s]
+Downloading data: 100%|██████████| 4.12M/4.12M [00:00<00:00, 22.1MB/s]
+Generating train split: 100%|██████████| 9427/9427 [00:00<00:00, 22032.88 examples/s]
+Generating validation split: 100%|██████████| 3270/3270 [00:00<00:00, 22471.11 examples/s]
+Generating test split: 100%|██████████| 3245/3245 [00:00<00:00, 23530.59 examples/s]
+2024-05-30:12:59:42,793 WARNING  [task.py:763] [Task: copa] metric acc is defined, but aggregation is not. using default aggregation=mean
+2024-05-30:12:59:42,793 WARNING  [task.py:775] [Task: copa] metric acc is defined, but higher_is_better is not. using default higher_is_better=True
+Downloading data: 100%|██████████| 44.0k/44.0k [00:00<00:00, 34.4MB/s]
+Generating train split: 100%|██████████| 400/400 [00:00<00:00, 16331.21 examples/s]
+Generating validation split: 100%|██████████| 100/100 [00:00<00:00, 12680.04 examples/s]
+Generating test split: 100%|██████████| 500/500 [00:00<00:00, 17231.15 examples/s]
+2024-05-30:12:59:44,865 WARNING  [task.py:763] [Task: mrpc] metric acc is defined, but aggregation is not. using default aggregation=mean
+2024-05-30:12:59:44,866 WARNING  [task.py:775] [Task: mrpc] metric acc is defined, but higher_is_better is not. using default higher_is_better=True
+2024-05-30:12:59:44,866 WARNING  [task.py:763] [Task: mrpc] metric f1 is defined, but aggregation is not. using default aggregation=f1
+2024-05-30:12:59:44,867 WARNING  [task.py:775] [Task: mrpc] metric f1 is defined, but higher_is_better is not. using default higher_is_better=True
+Downloading readme: 100%|██████████| 35.3k/35.3k [00:00<00:00, 631kB/s]
+Downloading data: 100%|██████████| 649k/649k [00:00<00:00, 4.35MB/s]
+Downloading data: 100%|██████████| 75.7k/75.7k [00:00<00:00, 441kB/s]
+Downloading data: 100%|██████████| 308k/308k [00:00<00:00, 2.05MB/s]
+Generating train split: 100%|██████████| 3668/3668 [00:00<00:00, 396074.12 examples/s]
+Generating validation split: 100%|██████████| 408/408 [00:00<00:00, 176565.83 examples/s]
+Generating test split: 100%|██████████| 1725/1725 [00:00<00:00, 381904.16 examples/s]
+/usr/local/lib/python3.10/dist-packages/datasets/load.py:1486: 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
+You can avoid this message in future by passing the argument `trust_remote_code=True`.
+Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.
+  warnings.warn(
+Downloading builder script: 100%|██████████| 5.36k/5.36k [00:00<00:00, 11.5MB/s]
+Downloading readme: 100%|██████████| 8.41k/8.41k [00:00<00:00, 18.0MB/s]
+Downloading data: 100%|██████████| 1.82M/1.82M [00:00<00:00, 4.03MB/s]
+Downloading data: 100%|██████████| 815k/815k [00:00<00:00, 20.3MB/s]
+Generating train split: 100%|██████████| 16113/16113 [00:00<00:00, 23924.69 examples/s]
+Generating test split: 100%|██████████| 3084/3084 [00:00<00:00, 24061.75 examples/s]
+Generating validation split: 100%|██████████| 1838/1838 [00:00<00:00, 23899.46 examples/s]
+2024-05-30:12:59:56,508 WARNING  [task.py:763] [Task: sst2] metric acc is defined, but aggregation is not. using default aggregation=mean
+2024-05-30:12:59:56,508 WARNING  [task.py:775] [Task: sst2] metric acc is defined, but higher_is_better is not. using default higher_is_better=True
+Downloading data: 100%|██████████| 3.11M/3.11M [00:00<00:00, 20.5MB/s]
+Downloading data: 100%|██████████| 72.8k/72.8k [00:00<00:00, 492kB/s]
+Downloading data: 100%|██████████| 148k/148k [00:00<00:00, 970kB/s]
+Generating train split: 100%|██████████| 67349/67349 [00:00<00:00, 1381972.95 examples/s]
+Generating validation split: 100%|██████████| 872/872 [00:00<00:00, 396770.78 examples/s]
+Generating test split: 100%|██████████| 1821/1821 [00:00<00:00, 539242.28 examples/s]
+/usr/local/lib/python3.10/dist-packages/datasets/load.py:1486: 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
+You can avoid this message in future by passing the argument `trust_remote_code=True`.
+Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.
+  warnings.warn(
+Downloading builder script: 100%|██████████| 5.65k/5.65k [00:00<00:00, 12.6MB/s]
+Downloading readme: 100%|██████████| 9.97k/9.97k [00:00<00:00, 17.0MB/s]
+Downloading data: 100%|██████████| 3.40M/3.40M [00:00<00:00, 7.07MB/s]
+Generating train split: 100%|██████████| 40398/40398 [00:01<00:00, 24432.85 examples/s]
+Generating test split: 100%|██████████| 1767/1767 [00:00<00:00, 24237.32 examples/s]
+Generating validation split: 100%|██████████| 1267/1267 [00:00<00:00, 24016.30 examples/s]
+2024-05-30:13:00:09,192 INFO     [task.py:395] Building contexts for winogrande on rank 0...
+100%|██████████| 1267/1267 [00:00<00:00, 69282.60it/s]
+2024-05-30:13:00:09,276 INFO     [task.py:395] Building contexts for sst2 on rank 0...
+100%|██████████| 872/872 [00:00<00:00, 2554.17it/s]
+2024-05-30:13:00:09,646 INFO     [task.py:395] Building contexts for piqa on rank 0...
+100%|██████████| 1838/1838 [00:01<00:00, 1082.63it/s]
+2024-05-30:13:00:11,418 INFO     [task.py:395] Building contexts for mrpc on rank 0...
+100%|██████████| 408/408 [00:00<00:00, 1864.53it/s]
+2024-05-30:13:00:11,655 INFO     [task.py:395] Building contexts for copa on rank 0...
+100%|██████████| 100/100 [00:00<00:00, 60288.98it/s]
+2024-05-30:13:00:11,664 INFO     [task.py:395] Building contexts for boolq on rank 0...
+100%|██████████| 3270/3270 [00:01<00:00, 1941.42it/s]
+2024-05-30:13:00:13,487 INFO     [task.py:395] Building contexts for arc_easy on rank 0...
+100%|██████████| 2376/2376 [00:02<00:00, 1065.20it/s]
+2024-05-30:13:00:15,863 INFO     [evaluator.py:379] Running loglikelihood requests
+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
+Running loglikelihood requests:   0%|          | 0/25011 [00:00<?, ?it/s]
+Passed argument batch_size = auto:1. Detecting largest batch size
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+Running loglikelihood requests: 100%|██████████| 25011/25011 [55:52<00:00,  7.46it/s]
+bootstrapping for stddev: f1_score
+
+
+
+100%|██████████| 100/100 [02:11<00:00,  1.32s/it]
+hf (pretrained=/mnt/weka/peacock/experiments/llama/eval/checkpoint-english/llamav2-3b/hf/global_step50000,tokenizer=/mnt/weka/peacock/tokenization/trained-tokenizer/enhiben_50k_hf/ConvertedTokenizer), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: auto (64)
+|  Tasks   |Version|Filter|n-shot| Metric |Value |   |Stderr|
+|----------|------:|------|-----:|--------|-----:|---|-----:|
+|winogrande|      1|none  |     0|acc     |0.5296|±  |0.0140|
+|sst2      |      1|none  |     0|acc     |0.4828|±  |0.0169|
+|piqa      |      1|none  |     0|acc     |0.5049|±  |0.0117|
+|          |       |none  |     0|acc_norm|0.4924|±  |0.0117|
+|mrpc      |      1|none  |     0|acc     |0.3162|±  |0.0230|
+|          |       |none  |     0|f1      |0.0000|±  |0.0000|
+|copa      |      1|none  |     0|acc     |0.5800|±  |0.0496|
+|boolq     |      2|none  |     0|acc     |0.3786|±  |0.0085|
+|arc_easy  |      1|none  |     0|acc     |0.2605|±  |0.0090|
+|          |       |none  |     0|acc_norm|0.2630|±  |0.0090|

lm-evaluation-harness/wandb/run-20240530_125856-v5b29ywz/files/requirements.txt

@@ -0,0 +1,154 @@
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+GitPython==3.1.43
+Jinja2==3.1.4
+Markdown==3.6
+MarkupSafe==2.1.5
+Pillow-SIMD==7.0.0.post3
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+dill==0.3.8
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+grpcio==1.63.0
+habana-media-loader==1.15.1.15
+habana-pyhlml==1.15.1.15
+habana-torch-dataloader==1.15.1.15
+habana-torch-plugin==1.15.1.15
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+numpy==1.23.5
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+pathvalidate==3.2.0
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+pip==23.3.1
+platformdirs==4.2.1
+pluggy==1.5.0
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+pytz==2024.1
+regex==2023.5.5
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+requests==2.31.0
+rouge_score==0.1.2
+rsa==4.9
+sacrebleu==2.4.2
+safetensors==0.4.3
+scikit-learn==1.5.0
+scipy==1.13.1
+sentencepiece==0.2.0
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+torch==2.2.0a0+git8964477
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lm-evaluation-harness/wandb/run-20240530_125856-v5b29ywz/files/wandb-metadata.json

@@ -0,0 +1,850 @@
+{
+    "os": "Linux-5.15.0-92-generic-x86_64-with-glibc2.35",
+    "python": "3.10.12",
+    "heartbeatAt": "2024-05-30T12:58:56.559065",
+    "startedAt": "2024-05-30T12:58:56.017734",
+    "docker": null,
+    "cuda": null,
+    "args": [
+        "--model",
+        "hf",
+        "--model_args",
+        "pretrained=/mnt/weka/peacock/experiments/llama/eval/checkpoint-english/llamav2-3b/hf/global_step50000,tokenizer=/mnt/weka/peacock/tokenization/trained-tokenizer/enhiben_50k_hf/ConvertedTokenizer",
+        "--tasks",
+        "winogrande,sst2,mrpc,arc_easy,copa,piqa,boolq",
+        "--batch_size",
+        "auto",
+        "--wandb_args",
+        "project=english-eval,group=exp2,name=global_step50000"
+    ],
+    "state": "running",
+    "program": "-m lm_eval.__main__",
+    "codePathLocal": null,
+    "git": {
+        "remote": "https://github.com/EleutherAI/lm-evaluation-harness",
+        "commit": null
+    },
+    "email": null,
+    "root": "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness",
+    "host": "peacock-evaluation-debug-25-worker-0",
+    "username": "root",
+    "executable": "/usr/bin/python3",
+    "cpu_count": 80,
+    "cpu_count_logical": 160,
+    "cpu_freq": {
+        "current": 2327.12058125,
+        "min": 800.0,
+        "max": 3400.0
+    },
+    "cpu_freq_per_core": [
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Drop the period\n    connector = {\n        "cause": "because",\n        "effect": "therefore",\n    }[doc["question"]]\n    return doc["premise"].strip()[:-1] + f" {connector}"\n', 'doc_to_target': 'def doc_to_target(doc):\n    correct_choice = doc["choice1"] if doc["label"] == 0 else doc["choice2"]\n    # Connect the sentences\n    return " " + convert_choice(correct_choice)\n', 'doc_to_choice': 'def doc_to_choice(doc):\n    return [" " + convert_choice(doc["choice1"]), " " + convert_choice(doc["choice2"])]\n', 'description': '', 'target_delimiter': ' ', 'fewshot_delimiter': '\n\n', 'num_fewshot': 0, 'metric_list': [{'metric': 'acc'}], 'output_type': 'multiple_choice', 'repeats': 1, 'should_decontaminate': False, 'metadata': {'version': 1.0}}, 'mrpc': {'task': 'mrpc', 'group': 'glue', 'dataset_path': 'glue', 'dataset_name': 'mrpc', 'training_split': 'train', 'validation_split': 'validation', 'doc_to_text': 'Sentence 1: {{sentence1}}\nSentence 2: {{sentence2}}\nQuestion: Do both sentences mean the same thing?\nAnswer:', 'doc_to_target': 'label', 'doc_to_choice': ['no', 'yes'], 'description': '', 'target_delimiter': ' ', 'fewshot_delimiter': '\n\n', 'num_fewshot': 0, 'metric_list': [{'metric': 'acc'}, {'metric': 'f1'}], 'output_type': 'multiple_choice', 'repeats': 1, 'should_decontaminate': False, 'metadata': {'version': 1.0}}, 'piqa': {'task': 'piqa', 'dataset_path': 'piqa', 'training_split': 'train', 'validation_split': 'validation', 'doc_to_text': 'Question: {{goal}}\nAnswer:', 'doc_to_target': 'label', 'doc_to_choice': '{{[sol1, sol2]}}', 'description': '', 'target_delimiter': ' ', 'fewshot_delimiter': '\n\n', 'num_fewshot': 0, 'metric_list': [{'metric': 'acc', 'aggregation': 'mean', 'higher_is_better': True}, {'metric': 'acc_norm', 'aggregation': 'mean', 'higher_is_better': True}], 'output_type': 'multiple_choice', 'repeats': 1, 'should_decontaminate': True, 'doc_to_decontamination_query': 'goal', 'metadata': {'version': 1.0}}, 'sst2': {'task': 'sst2', 'group': 'glue', 'dataset_path': 'glue', 'dataset_name': 'sst2', 'training_split': 'train', 'validation_split': 'validation', 'doc_to_text': '{{sentence}}\nQuestion: Is this sentence positive or negative?\nAnswer:', 'doc_to_target': 'label', 'doc_to_choice': ['negative', 'positive'], 'description': '', 'target_delimiter': ' ', 'fewshot_delimiter': '\n\n', 'num_fewshot': 0, 'metric_list': [{'metric': 'acc'}], 'output_type': 'multiple_choice', 'repeats': 1, 'should_decontaminate': False, 'metadata': {'version': 1.0}}, 'winogrande': {'task': 'winogrande', 'dataset_path': 'winogrande', 'dataset_name': 'winogrande_xl', 'training_split': 'train', 'validation_split': 'validation', 'doc_to_text': 'def doc_to_text(doc):\n    answer_to_num = {"1": 0, "2": 1}\n    return answer_to_num[doc["answer"]]\n', 'doc_to_target': 'def doc_to_target(doc):\n    idx = doc["sentence"].index("_") + 1\n    return doc["sentence"][idx:].strip()\n', 'doc_to_choice': 'def doc_to_choice(doc):\n    idx = doc["sentence"].index("_")\n    options = [doc["option1"], doc["option2"]]\n    return [doc["sentence"][:idx] + opt for opt in options]\n', 'description': '', 'target_delimiter': ' ', 'fewshot_delimiter': '\n\n', 'num_fewshot': 0, 'metric_list': [{'metric': 'acc', 'aggregation': 'mean', 'higher_is_better': True}], 'output_type': 'multiple_choice', 'repeats': 1, 'should_decontaminate': True, 'doc_to_decontamination_query': 'sentence', 'metadata': {'version': 1.0}}}, 'cli_configs': {'model': 'hf', 'model_args': 'pretrained=/mnt/weka/peacock/experiments/llama/eval/checkpoint-english/llamav2-3b/hf/global_step50000,tokenizer=/mnt/weka/peacock/tokenization/trained-tokenizer/enhiben_50k_hf/ConvertedTokenizer', 'batch_size': 'auto', 'batch_sizes': [64], 'device': None, 'use_cache': None, 'limit': None, 'bootstrap_iters': 100000, 'gen_kwargs': None}}
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venv/lib/python3.10/site-packages/nvidia/cudnn/lib/libcudnn_ops_train.so.8

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c89d9c871d0f1d1b3d1037234a66ebb09f990e3d5324c5025d45835d7d82d462
+size 70922856

venv/lib/python3.10/site-packages/transformers/models/bart/__init__.py

@@ -0,0 +1,148 @@
+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import (
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    is_flax_available,
+    is_tf_available,
+    is_tokenizers_available,
+    is_torch_available,
+)
+
+
+_import_structure = {
+    "configuration_bart": ["BART_PRETRAINED_CONFIG_ARCHIVE_MAP", "BartConfig", "BartOnnxConfig"],
+    "tokenization_bart": ["BartTokenizer"],
+}
+
+try:
+    if not is_tokenizers_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["tokenization_bart_fast"] = ["BartTokenizerFast"]
+
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_bart"] = [
+        "BART_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "BartForCausalLM",
+        "BartForConditionalGeneration",
+        "BartForQuestionAnswering",
+        "BartForSequenceClassification",
+        "BartModel",
+        "BartPreTrainedModel",
+        "BartPretrainedModel",
+        "PretrainedBartModel",
+    ]
+
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_tf_bart"] = [
+        "TFBartForConditionalGeneration",
+        "TFBartForSequenceClassification",
+        "TFBartModel",
+        "TFBartPretrainedModel",
+    ]
+
+try:
+    if not is_flax_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_flax_bart"] = [
+        "FlaxBartDecoderPreTrainedModel",
+        "FlaxBartForCausalLM",
+        "FlaxBartForConditionalGeneration",
+        "FlaxBartForQuestionAnswering",
+        "FlaxBartForSequenceClassification",
+        "FlaxBartModel",
+        "FlaxBartPreTrainedModel",
+    ]
+
+if TYPE_CHECKING:
+    from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
+    from .tokenization_bart import BartTokenizer
+
+    try:
+        if not is_tokenizers_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .tokenization_bart_fast import BartTokenizerFast
+
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_bart import (
+            BART_PRETRAINED_MODEL_ARCHIVE_LIST,
+            BartForCausalLM,
+            BartForConditionalGeneration,
+            BartForQuestionAnswering,
+            BartForSequenceClassification,
+            BartModel,
+            BartPreTrainedModel,
+            BartPretrainedModel,
+            PretrainedBartModel,
+        )
+
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_tf_bart import (
+            TFBartForConditionalGeneration,
+            TFBartForSequenceClassification,
+            TFBartModel,
+            TFBartPretrainedModel,
+        )
+
+    try:
+        if not is_flax_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_flax_bart import (
+            FlaxBartDecoderPreTrainedModel,
+            FlaxBartForCausalLM,
+            FlaxBartForConditionalGeneration,
+            FlaxBartForQuestionAnswering,
+            FlaxBartForSequenceClassification,
+            FlaxBartModel,
+            FlaxBartPreTrainedModel,
+        )
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

venv/lib/python3.10/site-packages/transformers/models/bart/configuration_bart.py

@@ -0,0 +1,401 @@
+# coding=utf-8
+# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" BART model configuration"""
+import warnings
+from collections import OrderedDict
+from typing import Any, Mapping, Optional
+
+from ... import PreTrainedTokenizer
+from ...configuration_utils import PretrainedConfig
+from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeq2SeqConfigWithPast
+from ...onnx.utils import compute_effective_axis_dimension
+from ...utils import TensorType, is_torch_available, logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class BartConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`BartModel`]. It is used to instantiate a BART
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the BART
+    [facebook/bart-large](https://huggingface.co/facebook/bart-large) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 50265):
+            Vocabulary size of the BART model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`BartModel`] or [`TFBartModel`].
+        d_model (`int`, *optional*, defaults to 1024):
+            Dimensionality of the layers and the pooler layer.
+        encoder_layers (`int`, *optional*, defaults to 12):
+            Number of encoder layers.
+        decoder_layers (`int`, *optional*, defaults to 12):
+            Number of decoder layers.
+        encoder_attention_heads (`int`, *optional*, defaults to 16):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        decoder_attention_heads (`int`, *optional*, defaults to 16):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        decoder_ffn_dim (`int`, *optional*, defaults to 4096):
+            Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
+        encoder_ffn_dim (`int`, *optional*, defaults to 4096):
+            Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
+        activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"silu"` and `"gelu_new"` are supported.
+        dropout (`float`, *optional*, defaults to 0.1):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        activation_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for activations inside the fully connected layer.
+        classifier_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for classifier.
+        max_position_embeddings (`int`, *optional*, defaults to 1024):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 2048).
+        init_std (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        encoder_layerdrop (`float`, *optional*, defaults to 0.0):
+            The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
+            for more details.
+        decoder_layerdrop (`float`, *optional*, defaults to 0.0):
+            The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
+            for more details.
+        scale_embedding (`bool`, *optional*, defaults to `False`):
+            Scale embeddings by diving by sqrt(d_model).
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models).
+        num_labels (`int`, *optional*, defaults to 3):
+            The number of labels to use in [`BartForSequenceClassification`].
+        forced_eos_token_id (`int`, *optional*, defaults to 2):
+            The id of the token to force as the last generated token when `max_length` is reached. Usually set to
+            `eos_token_id`.
+
+    Example:
+
+    ```python
+    >>> from transformers import BartConfig, BartModel
+
+    >>> # Initializing a BART facebook/bart-large style configuration
+    >>> configuration = BartConfig()
+
+    >>> # Initializing a model (with random weights) from the facebook/bart-large style configuration
+    >>> model = BartModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "bart"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
+
+    def __init__(
+        self,
+        vocab_size=50265,
+        max_position_embeddings=1024,
+        encoder_layers=12,
+        encoder_ffn_dim=4096,
+        encoder_attention_heads=16,
+        decoder_layers=12,
+        decoder_ffn_dim=4096,
+        decoder_attention_heads=16,
+        encoder_layerdrop=0.0,
+        decoder_layerdrop=0.0,
+        activation_function="gelu",
+        d_model=1024,
+        dropout=0.1,
+        attention_dropout=0.0,
+        activation_dropout=0.0,
+        init_std=0.02,
+        classifier_dropout=0.0,
+        scale_embedding=False,
+        use_cache=True,
+        num_labels=3,
+        pad_token_id=1,
+        bos_token_id=0,
+        eos_token_id=2,
+        is_encoder_decoder=True,
+        decoder_start_token_id=2,
+        forced_eos_token_id=2,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.d_model = d_model
+        self.encoder_ffn_dim = encoder_ffn_dim
+        self.encoder_layers = encoder_layers
+        self.encoder_attention_heads = encoder_attention_heads
+        self.decoder_ffn_dim = decoder_ffn_dim
+        self.decoder_layers = decoder_layers
+        self.decoder_attention_heads = decoder_attention_heads
+        self.dropout = dropout
+        self.attention_dropout = attention_dropout
+        self.activation_dropout = activation_dropout
+        self.activation_function = activation_function
+        self.init_std = init_std
+        self.encoder_layerdrop = encoder_layerdrop
+        self.decoder_layerdrop = decoder_layerdrop
+        self.classifier_dropout = classifier_dropout
+        self.use_cache = use_cache
+        self.num_hidden_layers = encoder_layers
+        self.scale_embedding = scale_embedding  # scale factor will be sqrt(d_model) if True
+
+        super().__init__(
+            num_labels=num_labels,
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            is_encoder_decoder=is_encoder_decoder,
+            decoder_start_token_id=decoder_start_token_id,
+            forced_eos_token_id=forced_eos_token_id,
+            **kwargs,
+        )
+
+        # ensure backward compatibility for BART CNN models
+        if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated", False):
+            self.forced_bos_token_id = self.bos_token_id
+            warnings.warn(
+                f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
+                "The config can simply be saved and uploaded again to be fixed."
+            )
+
+
+class BartOnnxConfig(OnnxSeq2SeqConfigWithPast):
+    @property
+    def inputs(self) -> Mapping[str, Mapping[int, str]]:
+        if self.task in ["default", "seq2seq-lm"]:
+            common_inputs = OrderedDict(
+                [
+                    ("input_ids", {0: "batch", 1: "encoder_sequence"}),
+                    ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
+                ]
+            )
+
+            if self.use_past:
+                common_inputs["decoder_input_ids"] = {0: "batch"}
+                common_inputs["decoder_attention_mask"] = {0: "batch", 1: "past_decoder_sequence + sequence"}
+            else:
+                common_inputs["decoder_input_ids"] = {0: "batch", 1: "decoder_sequence"}
+                common_inputs["decoder_attention_mask"] = {0: "batch", 1: "decoder_sequence"}
+
+            if self.use_past:
+                self.fill_with_past_key_values_(common_inputs, direction="inputs")
+        elif self.task == "causal-lm":
+            # TODO: figure this case out.
+            common_inputs = OrderedDict(
+                [
+                    ("input_ids", {0: "batch", 1: "encoder_sequence"}),
+                    ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
+                ]
+            )
+            if self.use_past:
+                num_encoder_layers, _ = self.num_layers
+                for i in range(num_encoder_layers):
+                    common_inputs[f"past_key_values.{i}.key"] = {0: "batch", 2: "past_sequence + sequence"}
+                    common_inputs[f"past_key_values.{i}.value"] = {0: "batch", 2: "past_sequence + sequence"}
+        else:
+            common_inputs = OrderedDict(
+                [
+                    ("input_ids", {0: "batch", 1: "encoder_sequence"}),
+                    ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
+                    ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
+                    ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
+                ]
+            )
+
+        return common_inputs
+
+    @property
+    def outputs(self) -> Mapping[str, Mapping[int, str]]:
+        if self.task in ["default", "seq2seq-lm"]:
+            common_outputs = super().outputs
+        else:
+            common_outputs = super(OnnxConfigWithPast, self).outputs
+            if self.use_past:
+                num_encoder_layers, _ = self.num_layers
+                for i in range(num_encoder_layers):
+                    common_outputs[f"present.{i}.key"] = {0: "batch", 2: "past_sequence + sequence"}
+                    common_outputs[f"present.{i}.value"] = {0: "batch", 2: "past_sequence + sequence"}
+        return common_outputs
+
+    def _generate_dummy_inputs_for_default_and_seq2seq_lm(
+        self,
+        tokenizer: PreTrainedTokenizer,
+        batch_size: int = -1,
+        seq_length: int = -1,
+        is_pair: bool = False,
+        framework: Optional[TensorType] = None,
+    ) -> Mapping[str, Any]:
+        encoder_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
+            tokenizer, batch_size, seq_length, is_pair, framework
+        )
+
+        # Generate decoder inputs
+        decoder_seq_length = seq_length if not self.use_past else 1
+        decoder_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
+            tokenizer, batch_size, decoder_seq_length, is_pair, framework
+        )
+        decoder_inputs = {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
+        common_inputs = dict(**encoder_inputs, **decoder_inputs)
+
+        if self.use_past:
+            if not is_torch_available():
+                raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
+            else:
+                import torch
+            batch, encoder_seq_length = common_inputs["input_ids"].shape
+            decoder_seq_length = common_inputs["decoder_input_ids"].shape[1]
+            num_encoder_attention_heads, num_decoder_attention_heads = self.num_attention_heads
+            encoder_shape = (
+                batch,
+                num_encoder_attention_heads,
+                encoder_seq_length,
+                self._config.hidden_size // num_encoder_attention_heads,
+            )
+            decoder_past_length = decoder_seq_length + 3
+            decoder_shape = (
+                batch,
+                num_decoder_attention_heads,
+                decoder_past_length,
+                self._config.hidden_size // num_decoder_attention_heads,
+            )
+
+            common_inputs["decoder_attention_mask"] = torch.cat(
+                [common_inputs["decoder_attention_mask"], torch.ones(batch, decoder_past_length)], dim=1
+            )
+
+            common_inputs["past_key_values"] = []
+            # If the number of encoder and decoder layers are present in the model configuration, both are considered
+            num_encoder_layers, num_decoder_layers = self.num_layers
+            min_num_layers = min(num_encoder_layers, num_decoder_layers)
+            max_num_layers = max(num_encoder_layers, num_decoder_layers) - min_num_layers
+            remaining_side_name = "encoder" if num_encoder_layers > num_decoder_layers else "decoder"
+
+            for _ in range(min_num_layers):
+                common_inputs["past_key_values"].append(
+                    (
+                        torch.zeros(decoder_shape),
+                        torch.zeros(decoder_shape),
+                        torch.zeros(encoder_shape),
+                        torch.zeros(encoder_shape),
+                    )
+                )
+            # TODO: test this.
+            shape = encoder_shape if remaining_side_name == "encoder" else decoder_shape
+            for _ in range(min_num_layers, max_num_layers):
+                common_inputs["past_key_values"].append((torch.zeros(shape), torch.zeros(shape)))
+        return common_inputs
+
+    def _generate_dummy_inputs_for_causal_lm(
+        self,
+        tokenizer: PreTrainedTokenizer,
+        batch_size: int = -1,
+        seq_length: int = -1,
+        is_pair: bool = False,
+        framework: Optional[TensorType] = None,
+    ) -> Mapping[str, Any]:
+        common_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
+            tokenizer, batch_size, seq_length, is_pair, framework
+        )
+
+        if self.use_past:
+            if not is_torch_available():
+                raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
+            else:
+                import torch
+            batch, seqlen = common_inputs["input_ids"].shape
+            # Not using the same length for past_key_values
+            past_key_values_length = seqlen + 2
+            num_encoder_layers, _ = self.num_layers
+            num_encoder_attention_heads, _ = self.num_attention_heads
+            past_shape = (
+                batch,
+                num_encoder_attention_heads,
+                past_key_values_length,
+                self._config.hidden_size // num_encoder_attention_heads,
+            )
+
+            mask_dtype = common_inputs["attention_mask"].dtype
+            common_inputs["attention_mask"] = torch.cat(
+                [common_inputs["attention_mask"], torch.ones(batch, past_key_values_length, dtype=mask_dtype)], dim=1
+            )
+            common_inputs["past_key_values"] = [
+                (torch.zeros(past_shape), torch.zeros(past_shape)) for _ in range(num_encoder_layers)
+            ]
+        return common_inputs
+
+    def _generate_dummy_inputs_for_sequence_classification_and_question_answering(
+        self,
+        tokenizer: PreTrainedTokenizer,
+        batch_size: int = -1,
+        seq_length: int = -1,
+        is_pair: bool = False,
+        framework: Optional[TensorType] = None,
+    ) -> Mapping[str, Any]:
+        # Copied from OnnxConfig.generate_dummy_inputs
+        # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
+        # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
+        batch_size = compute_effective_axis_dimension(
+            batch_size, fixed_dimension=OnnxConfig.default_fixed_batch, num_token_to_add=0
+        )
+
+        # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
+        token_to_add = tokenizer.num_special_tokens_to_add(is_pair)
+        seq_length = compute_effective_axis_dimension(
+            seq_length, fixed_dimension=OnnxConfig.default_fixed_sequence, num_token_to_add=token_to_add
+        )
+
+        # Generate dummy inputs according to compute batch and sequence
+        dummy_input = [" ".join([tokenizer.unk_token]) * seq_length] * batch_size
+        common_inputs = dict(tokenizer(dummy_input, return_tensors=framework))
+        return common_inputs
+
+    def generate_dummy_inputs(
+        self,
+        tokenizer: PreTrainedTokenizer,
+        batch_size: int = -1,
+        seq_length: int = -1,
+        is_pair: bool = False,
+        framework: Optional[TensorType] = None,
+    ) -> Mapping[str, Any]:
+        if self.task in ["default", "seq2seq-lm"]:
+            common_inputs = self._generate_dummy_inputs_for_default_and_seq2seq_lm(
+                tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
+            )
+
+        elif self.task == "causal-lm":
+            common_inputs = self._generate_dummy_inputs_for_causal_lm(
+                tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
+            )
+        else:
+            common_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
+                tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
+            )
+
+        return common_inputs
+
+    def _flatten_past_key_values_(self, flattened_output, name, idx, t):
+        if self.task in ["default", "seq2seq-lm"]:
+            flattened_output = super()._flatten_past_key_values_(flattened_output, name, idx, t)
+        else:
+            flattened_output = super(OnnxSeq2SeqConfigWithPast, self)._flatten_past_key_values_(
+                flattened_output, name, idx, t
+            )