Add files using upload-large-folder tool

ckpts/universal/global_step20/zero/26.input_layernorm.weight/exp_avg.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0e0f728bb45c7312584c2c755fc7bebf72d2561d2c437657b21c6bb5b7884913
+size 9372

ckpts/universal/global_step20/zero/26.input_layernorm.weight/exp_avg_sq.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a128296ed5c71e3a8f3f6c0a69df2c4f087906c3c039e972fe8fcd3e568a7169
+size 9387

ckpts/universal/global_step20/zero/26.input_layernorm.weight/fp32.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a69e8ffd317dfc5464ae65ba7534973db1814bc23c6024bde68a545703d06210
+size 9293

ckpts/universal/global_step20/zero/3.mlp.dense_h_to_4h_swiglu.weight/fp32.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c66cf3e2c1940a5984771c4856d9d20598609758ad8010ccd3a765f37d2ef32c
+size 33555533

ckpts/universal/global_step20/zero/4.mlp.dense_4h_to_h.weight/exp_avg.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0a9ff7aacb96dffcf4d5c5635252f1608e1bd78c50486819bf4467971131e8b3
+size 33555612

lm-evaluation-harness/wandb/run-20240514_114312-um1uwghq/files/config.yaml

@@ -0,0 +1,32 @@
+wandb_version: 1
+
+_wandb:
+  desc: null
+  value:
+    python_version: 3.10.12
+    cli_version: 0.17.0
+    framework: huggingface
+    huggingface_version: 4.40.2
+    is_jupyter_run: false
+    is_kaggle_kernel: false
+    start_time: 1715686992
+    t:
+      1:
+      - 1
+      - 5
+      - 11
+      - 49
+      - 51
+      - 53
+      - 55
+      - 71
+      - 98
+      - 100
+      3:
+      - 23
+      4: 3.10.12
+      5: 0.17.0
+      6: 4.40.2
+      8:
+      - 5
+      13: linux-x86_64

lm-evaluation-harness/wandb/run-20240514_114312-um1uwghq/logs/debug-internal.log

@@ -0,0 +1,19 @@
+2024-05-14 11:43:12,780 INFO    StreamThr :80497 [internal.py:wandb_internal():85] W&B internal server running at pid: 80497, started at: 2024-05-14 11:43:12.780007
+2024-05-14 11:43:12,782 DEBUG   HandlerThread:80497 [handler.py:handle_request():158] handle_request: status
+2024-05-14 11:43:12,784 INFO    WriterThread:80497 [datastore.py:open_for_write():87] open: /data/cronscript/lm-evaluation-harness/wandb/run-20240514_114312-um1uwghq/run-um1uwghq.wandb
+2024-05-14 11:43:12,784 DEBUG   SenderThread:80497 [sender.py:send():378] send: header
+2024-05-14 11:43:12,799 DEBUG   SenderThread:80497 [sender.py:send():378] send: run
+2024-05-14 11:43:13,077 INFO    SenderThread:80497 [dir_watcher.py:__init__():211] watching files in: /data/cronscript/lm-evaluation-harness/wandb/run-20240514_114312-um1uwghq/files
+2024-05-14 11:43:13,077 INFO    SenderThread:80497 [sender.py:_start_run_threads():1123] run started: um1uwghq with start time 1715686992.779485
+2024-05-14 11:43:13,784 WARNING StreamThr :80497 [internal.py:is_dead():413] Internal process exiting, parent pid 79480 disappeared
+2024-05-14 11:43:13,784 ERROR   StreamThr :80497 [internal.py:wandb_internal():151] Internal process shutdown.
+2024-05-14 11:43:13,799 INFO    HandlerThread:80497 [handler.py:finish():882] shutting down handler
+2024-05-14 11:43:13,799 INFO    WriterThread:80497 [datastore.py:close():296] close: /data/cronscript/lm-evaluation-harness/wandb/run-20240514_114312-um1uwghq/run-um1uwghq.wandb
+2024-05-14 11:43:14,077 INFO    SenderThread:80497 [sender.py:finish():1545] shutting down sender
+2024-05-14 11:43:14,077 INFO    SenderThread:80497 [dir_watcher.py:finish():358] shutting down directory watcher
+2024-05-14 11:43:15,077 INFO    SenderThread:80497 [dir_watcher.py:finish():388] scan: /data/cronscript/lm-evaluation-harness/wandb/run-20240514_114312-um1uwghq/files
+2024-05-14 11:43:15,078 INFO    SenderThread:80497 [dir_watcher.py:finish():402] scan save: /data/cronscript/lm-evaluation-harness/wandb/run-20240514_114312-um1uwghq/files/config.yaml config.yaml
+2024-05-14 11:43:15,078 INFO    SenderThread:80497 [file_pusher.py:finish():169] shutting down file pusher
+2024-05-14 11:43:15,078 INFO    SenderThread:80497 [file_pusher.py:join():175] waiting for file pusher
+2024-05-14 11:43:15,521 INFO    wandb-upload_0:80497 [upload_job.py:push():130] Uploaded file /data/cronscript/lm-evaluation-harness/wandb/run-20240514_114312-um1uwghq/files/config.yaml
+2024-05-14 11:43:15,784 INFO    MainThread:80497 [internal.py:handle_exit():75] Internal process exited

lm-evaluation-harness/wandb/run-20240514_114312-um1uwghq/logs/debug.log

@@ -0,0 +1,20 @@
+2024-05-14 11:43:12,776 INFO    MainThread:79480 [wandb_setup.py:_flush():76] Current SDK version is 0.17.0
+2024-05-14 11:43:12,776 INFO    MainThread:79480 [wandb_setup.py:_flush():76] Configure stats pid to 79480
+2024-05-14 11:43:12,776 INFO    MainThread:79480 [wandb_setup.py:_flush():76] Loading settings from /root/.config/wandb/settings
+2024-05-14 11:43:12,776 INFO    MainThread:79480 [wandb_setup.py:_flush():76] Loading settings from /data/cronscript/lm-evaluation-harness/wandb/settings
+2024-05-14 11:43:12,776 INFO    MainThread:79480 [wandb_setup.py:_flush():76] Loading settings from environment variables: {}
+2024-05-14 11:43:12,776 INFO    MainThread:79480 [wandb_setup.py:_flush():76] Applying setup settings: {'_disable_service': False}
+2024-05-14 11:43:12,776 WARNING MainThread:79480 [wandb_setup.py:_flush():76] Could not find program at -m lm_eval.__main__
+2024-05-14 11:43:12,776 INFO    MainThread:79480 [wandb_setup.py:_flush():76] Inferring run settings from compute environment: {'program_relpath': None, 'program': '-m lm_eval.__main__'}
+2024-05-14 11:43:12,776 INFO    MainThread:79480 [wandb_setup.py:_flush():76] Applying login settings: {}
+2024-05-14 11:43:12,776 INFO    MainThread:79480 [wandb_init.py:_log_setup():520] Logging user logs to /data/cronscript/lm-evaluation-harness/wandb/run-20240514_114312-um1uwghq/logs/debug.log
+2024-05-14 11:43:12,777 INFO    MainThread:79480 [wandb_init.py:_log_setup():521] Logging internal logs to /data/cronscript/lm-evaluation-harness/wandb/run-20240514_114312-um1uwghq/logs/debug-internal.log
+2024-05-14 11:43:12,777 INFO    MainThread:79480 [wandb_init.py:init():560] calling init triggers
+2024-05-14 11:43:12,777 INFO    MainThread:79480 [wandb_init.py:init():567] wandb.init called with sweep_config: {}
+config: {}
+2024-05-14 11:43:12,777 INFO    MainThread:79480 [wandb_init.py:init():610] starting backend
+2024-05-14 11:43:12,777 INFO    MainThread:79480 [wandb_init.py:init():614] setting up manager
+2024-05-14 11:43:12,778 INFO    MainThread:79480 [backend.py:_multiprocessing_setup():105] multiprocessing start_methods=fork,spawn,forkserver, using: spawn
+2024-05-14 11:43:12,779 INFO    MainThread:79480 [wandb_init.py:init():622] backend started and connected
+2024-05-14 11:43:12,782 INFO    MainThread:79480 [wandb_init.py:init():711] updated telemetry
+2024-05-14 11:43:12,798 INFO    MainThread:79480 [wandb_init.py:init():744] communicating run to backend with 90.0 second timeout

lm-evaluation-harness/wandb/run-20240514_163703-t88cgth5/files/config.yaml

@@ -0,0 +1,43 @@
+wandb_version: 1
+
+_wandb:
+  desc: null
+  value:
+    python_version: 3.10.12
+    cli_version: 0.17.0
+    framework: huggingface
+    huggingface_version: 4.40.2
+    is_jupyter_run: false
+    is_kaggle_kernel: false
+    start_time: 1715704623
+    t:
+      1:
+      - 1
+      - 5
+      - 11
+      - 49
+      - 51
+      - 53
+      - 55
+      - 71
+      - 98
+      - 100
+      2:
+      - 1
+      - 5
+      - 11
+      - 49
+      - 51
+      - 53
+      - 55
+      - 71
+      - 98
+      - 100
+      3:
+      - 23
+      4: 3.10.12
+      5: 0.17.0
+      6: 4.40.2
+      8:
+      - 5
+      13: linux-x86_64

lm-evaluation-harness/wandb/run-20240514_163703-t88cgth5/files/output.log

@@ -0,0 +1,33 @@
+
+2024-05-14:16:37:04,204 INFO     [__main__.py:251] Verbosity set to INFO
+2024-05-14:16:37:08,795 INFO     [__main__.py:335] Selected Tasks: ['indiccopa-hi']
+2024-05-14:16:37:08,797 INFO     [evaluator.py:131] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234
+2024-05-14:16:37:08,797 INFO     [evaluator.py:177] Initializing hf model, with arguments: {'pretrained': '/data/cronscript/ckpts//hf_ckpt//global_step20'}
+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 369, in cached_file
+    raise EnvironmentError(
+OSError: /data/cronscript/ckpts//hf_ckpt//global_step20 does not appear to have a file named config.json. Checkout 'https://huggingface.co//data/cronscript/ckpts//hf_ckpt//global_step20/tree/main' for available files.

lm-evaluation-harness/wandb/run-20240514_163703-t88cgth5/files/requirements.txt

@@ -0,0 +1,163 @@
+DataProperty==1.0.1
+GitPython==3.1.43
+Jinja2==3.1.3
+Markdown==3.6
+MarkupSafe==2.1.5
+Pillow-SIMD==7.0.0.post3
+PyYAML==6.0
+Werkzeug==3.0.2
+absl-py==2.1.0
+accelerate==0.30.1
+aiohttp==3.9.4
+aiosignal==1.3.1
+antlr4-python3-runtime==4.9.3
+anyio==4.3.0
+async-timeout==4.0.3
+attrs==23.2.0
+av==9.2.0
+cachetools==5.3.3
+certifi==2024.2.2
+cffi==1.15.1
+cfgv==3.4.0
+chardet==5.2.0
+charset-normalizer==3.3.2
+click==8.1.7
+cmake==3.29.2
+colorama==0.4.6
+datasets==2.19.1
+deepspeed==0.12.4+hpu.synapse.v1.15.1
+dill==0.3.8
+distlib==0.3.8
+distro==1.9.0
+docker-pycreds==0.4.0
+einops==0.8.0
+evaluate==0.4.2
+exceptiongroup==1.2.0
+expecttest==0.2.1
+filelock==3.13.4
+frozenlist==1.4.1
+fsspec==2024.3.1
+gitdb==4.0.11
+google-auth-oauthlib==0.4.6
+google-auth==2.29.0
+grpcio==1.62.1
+h11==0.14.0
+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
+httpcore==1.0.5
+httpx==0.27.0
+huggingface-hub==0.23.0
+identify==2.5.35
+idna==3.7
+importlib_resources==6.4.0
+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==0.3.0
+lm_eval==0.4.2
+lm_eval==0.4.2
+lm_eval==0.4.2
+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
+omegaconf==2.3.0
+openai==1.29.0
+packaging==24.0
+pandas==2.0.1
+pathspec==0.12.1
+pathvalidate==3.2.0
+peft==0.10.0
+perfetto==0.7.0
+pip==22.0.2
+pip==23.3.1
+platformdirs==4.2.0
+pluggy==1.4.0
+portalocker==2.8.2
+pre-commit==3.3.3
+protobuf==3.20.3
+psutil==5.9.8
+py-cpuinfo==9.0.0
+pyarrow-hotfix==0.6
+pyarrow==16.0.0
+pyasn1==0.6.0
+pyasn1_modules==0.4.0
+pybind11==2.10.4
+pycountry==23.12.11
+pycparser==2.22
+pydantic==1.10.13
+pynvml==8.0.4
+pytablewriter==1.2.0
+pytest==8.1.1
+python-dateutil==2.9.0.post0
+pytorch-lightning==2.2.2
+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==1.5.0
+safetensors==0.4.3
+scikit-learn==1.4.2
+scipy==1.13.0
+sentencepiece==0.2.0
+sentry-sdk==2.1.1
+setproctitle==1.3.3
+setuptools==59.6.0
+setuptools==69.5.1
+six==1.16.0
+smmap==5.0.1
+sniffio==1.3.1
+sqlitedict==2.1.0
+symengine==0.11.0
+sympy==1.12
+tabledata==1.3.3
+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==1.3.2
+torchtext==0.17.0+400da5c
+torchvision==0.17.0+b2383d4
+tqdm-multiprocess==0.0.11
+tqdm==4.66.2
+transformers==4.40.2
+typepy==1.3.2
+typing_extensions==4.11.0
+tzdata==2024.1
+urllib3==1.26.18
+virtualenv==20.25.1
+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

lm-evaluation-harness/wandb/run-20240514_163703-t88cgth5/files/wandb-metadata.json

@@ -0,0 +1,810 @@
+{
+    "os": "Linux-5.15.0-92-generic-x86_64-with-glibc2.35",
+    "python": "3.10.12",
+    "heartbeatAt": "2024-05-14T16:37:04.072219",
+    "startedAt": "2024-05-14T16:37:03.637970",
+    "docker": null,
+    "cuda": null,
+    "args": [
+        "--model",
+        "hf",
+        "--model_args",
+        "pretrained=/data/cronscript/ckpts//hf_ckpt//global_step20",
+        "--tasks",
+        "indiccopa-hi",
+        "--batch_size",
+        "auto",
+        "--wandb_args",
+        "project=bharatgpt,group=trial_expt"
+    ],
+    "state": "running",
+    "program": "-m lm_eval.__main__",
+    "codePathLocal": null,
+    "git": {
+        "remote": "https://github.com/EleutherAI/lm-evaluation-harness",
+        "commit": null
+    },
+    "email": null,
+    "root": "/data/cronscript/lm-evaluation-harness",
+    "host": "vizzhy-150-3",
+    "username": "root",
+    "executable": "/usr/bin/python3",
+    "cpu_count": 76,
+    "cpu_count_logical": 152,
+    "cpu_freq": {
+        "current": 3389.1128618421053,
+        "min": 800.0,
+        "max": 3400.0
+    },
+    "cpu_freq_per_core": [
+        {
+            "current": 3300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3300.003,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3396.073,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3396.311,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3396.716,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        }
+    ],
+    "disk": {
+        "/": {
+            "total": 866.4415092468262,
+            "used": 863.4235572814941
+        }
+    },
+    "memory": {
+        "total": 1007.5000267028809
+    }
+}

lm-evaluation-harness/wandb/run-20240514_163703-t88cgth5/files/wandb-summary.json

@@ -0,0 +1 @@
+{"_wandb": {"runtime": 5}}

lm-evaluation-harness/wandb/run-20240514_163703-t88cgth5/logs/debug-internal.log

@@ -0,0 +1,182 @@
+2024-05-14 16:37:03,649 INFO    StreamThr :127609 [internal.py:wandb_internal():85] W&B internal server running at pid: 127609, started at: 2024-05-14 16:37:03.648986
+2024-05-14 16:37:03,652 DEBUG   HandlerThread:127609 [handler.py:handle_request():158] handle_request: status
+2024-05-14 16:37:03,653 INFO    WriterThread:127609 [datastore.py:open_for_write():87] open: /data/cronscript/lm-evaluation-harness/wandb/run-20240514_163703-t88cgth5/run-t88cgth5.wandb
+2024-05-14 16:37:03,653 DEBUG   SenderThread:127609 [sender.py:send():378] send: header
+2024-05-14 16:37:03,662 DEBUG   SenderThread:127609 [sender.py:send():378] send: run
+2024-05-14 16:37:03,915 INFO    SenderThread:127609 [dir_watcher.py:__init__():211] watching files in: /data/cronscript/lm-evaluation-harness/wandb/run-20240514_163703-t88cgth5/files
+2024-05-14 16:37:03,915 INFO    SenderThread:127609 [sender.py:_start_run_threads():1123] run started: t88cgth5 with start time 1715704623.648688
+2024-05-14 16:37:03,922 DEBUG   HandlerThread:127609 [handler.py:handle_request():158] handle_request: check_version
+2024-05-14 16:37:03,922 DEBUG   SenderThread:127609 [sender.py:send_request():405] send_request: check_version
+2024-05-14 16:37:04,005 DEBUG   HandlerThread:127609 [handler.py:handle_request():158] handle_request: run_start
+2024-05-14 16:37:04,007 DEBUG   HandlerThread:127609 [system_info.py:__init__():26] System info init
+2024-05-14 16:37:04,007 DEBUG   HandlerThread:127609 [system_info.py:__init__():41] System info init done
+2024-05-14 16:37:04,007 INFO    HandlerThread:127609 [system_monitor.py:start():194] Starting system monitor
+2024-05-14 16:37:04,007 INFO    SystemMonitor:127609 [system_monitor.py:_start():158] Starting system asset monitoring threads
+2024-05-14 16:37:04,007 INFO    HandlerThread:127609 [system_monitor.py:probe():214] Collecting system info
+2024-05-14 16:37:04,008 INFO    SystemMonitor:127609 [interfaces.py:start():188] Started cpu monitoring
+2024-05-14 16:37:04,008 INFO    SystemMonitor:127609 [interfaces.py:start():188] Started disk monitoring
+2024-05-14 16:37:04,009 INFO    SystemMonitor:127609 [interfaces.py:start():188] Started memory monitoring
+2024-05-14 16:37:04,009 INFO    SystemMonitor:127609 [interfaces.py:start():188] Started network monitoring
+2024-05-14 16:37:04,072 DEBUG   HandlerThread:127609 [system_info.py:probe():150] Probing system
+2024-05-14 16:37:04,080 DEBUG   HandlerThread:127609 [system_info.py:_probe_git():135] Probing git
+2024-05-14 16:37:04,100 ERROR   HandlerThread:127609 [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 '/data/cronscript/lm-evaluation-harness'
+To add an exception for this directory, call:
+
+	git config --global --add safe.directory /data/cronscript/lm-evaluation-harness'
+2024-05-14 16:37:04,100 DEBUG   HandlerThread:127609 [system_info.py:_probe_git():143] Probing git done
+2024-05-14 16:37:04,100 DEBUG   HandlerThread:127609 [system_info.py:probe():198] Probing system done
+2024-05-14 16:37:04,100 DEBUG   HandlerThread:127609 [system_monitor.py:probe():223] {'os': 'Linux-5.15.0-92-generic-x86_64-with-glibc2.35', 'python': '3.10.12', 'heartbeatAt': '2024-05-14T16:37:04.072219', 'startedAt': '2024-05-14T16:37:03.637970', 'docker': None, 'cuda': None, 'args': ('--model', 'hf', '--model_args', 'pretrained=/data/cronscript/ckpts//hf_ckpt//global_step20', '--tasks', 'indiccopa-hi', '--batch_size', 'auto', '--wandb_args', 'project=bharatgpt,group=trial_expt'), 'state': 'running', 'program': '-m lm_eval.__main__', 'codePathLocal': None, 'git': {'remote': 'https://github.com/EleutherAI/lm-evaluation-harness', 'commit': None}, 'email': None, 'root': '/data/cronscript/lm-evaluation-harness', 'host': 'vizzhy-150-3', 'username': 'root', 'executable': '/usr/bin/python3', 'cpu_count': 76, 'cpu_count_logical': 152, 'cpu_freq': {'current': 3389.1128618421053, 'min': 800.0, 'max': 3400.0}, 'cpu_freq_per_core': [{'current': 3300.0, 'min': 800.0, 'max': 3400.0}, {'current': 3300.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3300.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3300.003, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3300.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3396.073, 'min': 800.0, 'max': 3400.0}, {'current': 3396.311, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3396.716, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3300.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3300.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}], 'disk': {'/': {'total': 866.4415092468262, 'used': 863.4235572814941}}, 'memory': {'total': 1007.5000267028809}}
+2024-05-14 16:37:04,101 INFO    HandlerThread:127609 [system_monitor.py:probe():224] Finished collecting system info
+2024-05-14 16:37:04,101 INFO    HandlerThread:127609 [system_monitor.py:probe():227] Publishing system info
+2024-05-14 16:37:04,102 INFO    HandlerThread:127609 [system_monitor.py:probe():229] Finished publishing system info
+2024-05-14 16:37:04,106 DEBUG   SenderThread:127609 [sender.py:send():378] send: files
+2024-05-14 16:37:04,106 INFO    SenderThread:127609 [sender.py:_save_file():1389] saving file wandb-metadata.json with policy now
+2024-05-14 16:37:04,201 DEBUG   HandlerThread:127609 [handler.py:handle_request():158] handle_request: python_packages
+2024-05-14 16:37:04,201 DEBUG   SenderThread:127609 [sender.py:send_request():405] send_request: python_packages
+2024-05-14 16:37:04,201 DEBUG   HandlerThread:127609 [handler.py:handle_request():158] handle_request: stop_status
+2024-05-14 16:37:04,203 DEBUG   SenderThread:127609 [sender.py:send_request():405] send_request: stop_status
+2024-05-14 16:37:04,367 DEBUG   SenderThread:127609 [sender.py:send():378] send: telemetry
+2024-05-14 16:37:04,612 INFO    wandb-upload_0:127609 [upload_job.py:push():130] Uploaded file /tmp/tmps8ro5vrwwandb/s2uqwxfy-wandb-metadata.json
+2024-05-14 16:37:04,916 INFO    Thread-12 :127609 [dir_watcher.py:_on_file_created():271] file/dir created: /data/cronscript/lm-evaluation-harness/wandb/run-20240514_163703-t88cgth5/files/output.log
+2024-05-14 16:37:04,916 INFO    Thread-12 :127609 [dir_watcher.py:_on_file_created():271] file/dir created: /data/cronscript/lm-evaluation-harness/wandb/run-20240514_163703-t88cgth5/files/requirements.txt
+2024-05-14 16:37:04,916 INFO    Thread-12 :127609 [dir_watcher.py:_on_file_created():271] file/dir created: /data/cronscript/lm-evaluation-harness/wandb/run-20240514_163703-t88cgth5/files/wandb-metadata.json
+2024-05-14 16:37:06,916 INFO    Thread-12 :127609 [dir_watcher.py:_on_file_modified():288] file/dir modified: /data/cronscript/lm-evaluation-harness/wandb/run-20240514_163703-t88cgth5/files/output.log
+2024-05-14 16:37:08,797 DEBUG   HandlerThread:127609 [handler.py:handle_request():158] handle_request: status_report
+2024-05-14 16:37:09,895 DEBUG   SenderThread:127609 [sender.py:send():378] send: exit
+2024-05-14 16:37:09,895 INFO    SenderThread:127609 [sender.py:send_exit():585] handling exit code: 1
+2024-05-14 16:37:09,895 INFO    SenderThread:127609 [sender.py:send_exit():587] handling runtime: 5
+2024-05-14 16:37:09,896 INFO    SenderThread:127609 [sender.py:_save_file():1389] saving file wandb-summary.json with policy end
+2024-05-14 16:37:09,896 INFO    SenderThread:127609 [sender.py:send_exit():593] send defer
+2024-05-14 16:37:09,896 DEBUG   HandlerThread:127609 [handler.py:handle_request():158] handle_request: defer
+2024-05-14 16:37:09,897 INFO    HandlerThread:127609 [handler.py:handle_request_defer():184] handle defer: 0
+2024-05-14 16:37:09,897 DEBUG   SenderThread:127609 [sender.py:send_request():405] send_request: defer
+2024-05-14 16:37:09,897 INFO    SenderThread:127609 [sender.py:send_request_defer():609] handle sender defer: 0
+2024-05-14 16:37:09,897 INFO    SenderThread:127609 [sender.py:transition_state():613] send defer: 1
+2024-05-14 16:37:09,897 DEBUG   HandlerThread:127609 [handler.py:handle_request():158] handle_request: defer
+2024-05-14 16:37:09,897 INFO    HandlerThread:127609 [handler.py:handle_request_defer():184] handle defer: 1
+2024-05-14 16:37:09,897 DEBUG   SenderThread:127609 [sender.py:send_request():405] send_request: defer
+2024-05-14 16:37:09,897 INFO    SenderThread:127609 [sender.py:send_request_defer():609] handle sender defer: 1
+2024-05-14 16:37:09,897 INFO    SenderThread:127609 [sender.py:transition_state():613] send defer: 2
+2024-05-14 16:37:09,897 DEBUG   HandlerThread:127609 [handler.py:handle_request():158] handle_request: defer
+2024-05-14 16:37:09,897 INFO    HandlerThread:127609 [handler.py:handle_request_defer():184] handle defer: 2
+2024-05-14 16:37:09,897 INFO    HandlerThread:127609 [system_monitor.py:finish():203] Stopping system monitor
+2024-05-14 16:37:09,897 DEBUG   SystemMonitor:127609 [system_monitor.py:_start():172] Starting system metrics aggregation loop
+2024-05-14 16:37:09,898 INFO    HandlerThread:127609 [interfaces.py:finish():200] Joined cpu monitor
+2024-05-14 16:37:09,898 DEBUG   SystemMonitor:127609 [system_monitor.py:_start():179] Finished system metrics aggregation loop
+2024-05-14 16:37:09,899 INFO    HandlerThread:127609 [interfaces.py:finish():200] Joined disk monitor
+2024-05-14 16:37:09,899 DEBUG   SystemMonitor:127609 [system_monitor.py:_start():183] Publishing last batch of metrics
+2024-05-14 16:37:09,899 INFO    HandlerThread:127609 [interfaces.py:finish():200] Joined memory monitor
+2024-05-14 16:37:09,900 INFO    HandlerThread:127609 [interfaces.py:finish():200] Joined network monitor
+2024-05-14 16:37:09,900 DEBUG   SenderThread:127609 [sender.py:send_request():405] send_request: defer
+2024-05-14 16:37:09,900 INFO    SenderThread:127609 [sender.py:send_request_defer():609] handle sender defer: 2
+2024-05-14 16:37:09,900 INFO    SenderThread:127609 [sender.py:transition_state():613] send defer: 3
+2024-05-14 16:37:09,901 DEBUG   SenderThread:127609 [sender.py:send():378] send: stats
+2024-05-14 16:37:09,901 DEBUG   HandlerThread:127609 [handler.py:handle_request():158] handle_request: defer
+2024-05-14 16:37:09,901 INFO    HandlerThread:127609 [handler.py:handle_request_defer():184] handle defer: 3
+2024-05-14 16:37:09,901 DEBUG   SenderThread:127609 [sender.py:send_request():405] send_request: defer
+2024-05-14 16:37:09,901 INFO    SenderThread:127609 [sender.py:send_request_defer():609] handle sender defer: 3
+2024-05-14 16:37:09,901 INFO    SenderThread:127609 [sender.py:transition_state():613] send defer: 4
+2024-05-14 16:37:09,901 DEBUG   HandlerThread:127609 [handler.py:handle_request():158] handle_request: defer
+2024-05-14 16:37:09,901 INFO    HandlerThread:127609 [handler.py:handle_request_defer():184] handle defer: 4
+2024-05-14 16:37:09,902 DEBUG   SenderThread:127609 [sender.py:send_request():405] send_request: defer
+2024-05-14 16:37:09,902 INFO    SenderThread:127609 [sender.py:send_request_defer():609] handle sender defer: 4
+2024-05-14 16:37:09,902 INFO    SenderThread:127609 [sender.py:transition_state():613] send defer: 5
+2024-05-14 16:37:09,902 DEBUG   HandlerThread:127609 [handler.py:handle_request():158] handle_request: defer
+2024-05-14 16:37:09,902 INFO    HandlerThread:127609 [handler.py:handle_request_defer():184] handle defer: 5
+2024-05-14 16:37:09,902 DEBUG   SenderThread:127609 [sender.py:send():378] send: summary
+2024-05-14 16:37:09,902 INFO    SenderThread:127609 [sender.py:_save_file():1389] saving file wandb-summary.json with policy end
+2024-05-14 16:37:09,903 DEBUG   SenderThread:127609 [sender.py:send_request():405] send_request: defer
+2024-05-14 16:37:09,903 INFO    SenderThread:127609 [sender.py:send_request_defer():609] handle sender defer: 5
+2024-05-14 16:37:09,903 INFO    SenderThread:127609 [sender.py:transition_state():613] send defer: 6
+2024-05-14 16:37:09,903 DEBUG   HandlerThread:127609 [handler.py:handle_request():158] handle_request: defer
+2024-05-14 16:37:09,903 INFO    HandlerThread:127609 [handler.py:handle_request_defer():184] handle defer: 6
+2024-05-14 16:37:09,903 DEBUG   SenderThread:127609 [sender.py:send_request():405] send_request: defer
+2024-05-14 16:37:09,903 INFO    SenderThread:127609 [sender.py:send_request_defer():609] handle sender defer: 6
+2024-05-14 16:37:09,905 DEBUG   HandlerThread:127609 [handler.py:handle_request():158] handle_request: status_report
+2024-05-14 16:37:09,918 INFO    Thread-12 :127609 [dir_watcher.py:_on_file_created():271] file/dir created: /data/cronscript/lm-evaluation-harness/wandb/run-20240514_163703-t88cgth5/files/wandb-summary.json
+2024-05-14 16:37:09,977 INFO    SenderThread:127609 [sender.py:transition_state():613] send defer: 7
+2024-05-14 16:37:09,978 DEBUG   HandlerThread:127609 [handler.py:handle_request():158] handle_request: defer
+2024-05-14 16:37:09,978 INFO    HandlerThread:127609 [handler.py:handle_request_defer():184] handle defer: 7
+2024-05-14 16:37:09,978 DEBUG   SenderThread:127609 [sender.py:send_request():405] send_request: defer
+2024-05-14 16:37:09,978 INFO    SenderThread:127609 [sender.py:send_request_defer():609] handle sender defer: 7
+2024-05-14 16:37:10,895 DEBUG   HandlerThread:127609 [handler.py:handle_request():158] handle_request: poll_exit
+2024-05-14 16:37:10,919 INFO    Thread-12 :127609 [dir_watcher.py:_on_file_modified():288] file/dir modified: /data/cronscript/lm-evaluation-harness/wandb/run-20240514_163703-t88cgth5/files/output.log
+2024-05-14 16:37:10,919 INFO    Thread-12 :127609 [dir_watcher.py:_on_file_modified():288] file/dir modified: /data/cronscript/lm-evaluation-harness/wandb/run-20240514_163703-t88cgth5/files/config.yaml
+2024-05-14 16:37:12,381 INFO    SenderThread:127609 [sender.py:transition_state():613] send defer: 8
+2024-05-14 16:37:12,381 DEBUG   SenderThread:127609 [sender.py:send_request():405] send_request: poll_exit
+2024-05-14 16:37:12,381 DEBUG   HandlerThread:127609 [handler.py:handle_request():158] handle_request: defer
+2024-05-14 16:37:12,381 INFO    HandlerThread:127609 [handler.py:handle_request_defer():184] handle defer: 8
+2024-05-14 16:37:12,382 DEBUG   SenderThread:127609 [sender.py:send_request():405] send_request: defer
+2024-05-14 16:37:12,382 INFO    SenderThread:127609 [sender.py:send_request_defer():609] handle sender defer: 8
+2024-05-14 16:37:12,382 INFO    SenderThread:127609 [job_builder.py:build():432] Attempting to build job artifact
+2024-05-14 16:37:12,382 INFO    SenderThread:127609 [job_builder.py:_get_source_type():576] no source found
+2024-05-14 16:37:12,382 INFO    SenderThread:127609 [sender.py:transition_state():613] send defer: 9
+2024-05-14 16:37:12,382 DEBUG   HandlerThread:127609 [handler.py:handle_request():158] handle_request: defer
+2024-05-14 16:37:12,382 INFO    HandlerThread:127609 [handler.py:handle_request_defer():184] handle defer: 9
+2024-05-14 16:37:12,382 DEBUG   SenderThread:127609 [sender.py:send_request():405] send_request: defer
+2024-05-14 16:37:12,383 INFO    SenderThread:127609 [sender.py:send_request_defer():609] handle sender defer: 9
+2024-05-14 16:37:12,383 INFO    SenderThread:127609 [dir_watcher.py:finish():358] shutting down directory watcher
+2024-05-14 16:37:12,895 DEBUG   HandlerThread:127609 [handler.py:handle_request():158] handle_request: poll_exit
+2024-05-14 16:37:12,920 INFO    SenderThread:127609 [dir_watcher.py:_on_file_modified():288] file/dir modified: /data/cronscript/lm-evaluation-harness/wandb/run-20240514_163703-t88cgth5/files/output.log
+2024-05-14 16:37:12,920 INFO    SenderThread:127609 [dir_watcher.py:finish():388] scan: /data/cronscript/lm-evaluation-harness/wandb/run-20240514_163703-t88cgth5/files
+2024-05-14 16:37:12,921 INFO    SenderThread:127609 [dir_watcher.py:finish():402] scan save: /data/cronscript/lm-evaluation-harness/wandb/run-20240514_163703-t88cgth5/files/config.yaml config.yaml
+2024-05-14 16:37:12,921 INFO    SenderThread:127609 [dir_watcher.py:finish():402] scan save: /data/cronscript/lm-evaluation-harness/wandb/run-20240514_163703-t88cgth5/files/output.log output.log
+2024-05-14 16:37:12,921 INFO    SenderThread:127609 [dir_watcher.py:finish():402] scan save: /data/cronscript/lm-evaluation-harness/wandb/run-20240514_163703-t88cgth5/files/wandb-summary.json wandb-summary.json
+2024-05-14 16:37:12,921 INFO    SenderThread:127609 [dir_watcher.py:finish():402] scan save: /data/cronscript/lm-evaluation-harness/wandb/run-20240514_163703-t88cgth5/files/requirements.txt requirements.txt
+2024-05-14 16:37:12,921 INFO    SenderThread:127609 [dir_watcher.py:finish():402] scan save: /data/cronscript/lm-evaluation-harness/wandb/run-20240514_163703-t88cgth5/files/wandb-metadata.json wandb-metadata.json
+2024-05-14 16:37:12,924 INFO    SenderThread:127609 [sender.py:transition_state():613] send defer: 10
+2024-05-14 16:37:12,924 DEBUG   SenderThread:127609 [sender.py:send_request():405] send_request: poll_exit
+2024-05-14 16:37:12,926 DEBUG   HandlerThread:127609 [handler.py:handle_request():158] handle_request: defer
+2024-05-14 16:37:12,926 INFO    HandlerThread:127609 [handler.py:handle_request_defer():184] handle defer: 10
+2024-05-14 16:37:12,927 DEBUG   SenderThread:127609 [sender.py:send_request():405] send_request: defer
+2024-05-14 16:37:12,927 INFO    SenderThread:127609 [sender.py:send_request_defer():609] handle sender defer: 10
+2024-05-14 16:37:12,927 INFO    SenderThread:127609 [file_pusher.py:finish():169] shutting down file pusher
+2024-05-14 16:37:13,182 INFO    wandb-upload_0:127609 [upload_job.py:push():130] Uploaded file /data/cronscript/lm-evaluation-harness/wandb/run-20240514_163703-t88cgth5/files/output.log
+2024-05-14 16:37:13,315 INFO    wandb-upload_1:127609 [upload_job.py:push():130] Uploaded file /data/cronscript/lm-evaluation-harness/wandb/run-20240514_163703-t88cgth5/files/config.yaml
+2024-05-14 16:37:13,388 INFO    wandb-upload_2:127609 [upload_job.py:push():130] Uploaded file /data/cronscript/lm-evaluation-harness/wandb/run-20240514_163703-t88cgth5/files/wandb-summary.json
+2024-05-14 16:37:13,408 INFO    wandb-upload_3:127609 [upload_job.py:push():130] Uploaded file /data/cronscript/lm-evaluation-harness/wandb/run-20240514_163703-t88cgth5/files/requirements.txt
+2024-05-14 16:37:13,608 INFO    Thread-11 (_thread_body):127609 [sender.py:transition_state():613] send defer: 11
+2024-05-14 16:37:13,609 DEBUG   HandlerThread:127609 [handler.py:handle_request():158] handle_request: defer
+2024-05-14 16:37:13,609 INFO    HandlerThread:127609 [handler.py:handle_request_defer():184] handle defer: 11
+2024-05-14 16:37:13,609 DEBUG   SenderThread:127609 [sender.py:send_request():405] send_request: defer
+2024-05-14 16:37:13,609 INFO    SenderThread:127609 [sender.py:send_request_defer():609] handle sender defer: 11
+2024-05-14 16:37:13,609 INFO    SenderThread:127609 [file_pusher.py:join():175] waiting for file pusher
+2024-05-14 16:37:13,609 INFO    SenderThread:127609 [sender.py:transition_state():613] send defer: 12
+2024-05-14 16:37:13,609 DEBUG   HandlerThread:127609 [handler.py:handle_request():158] handle_request: defer
+2024-05-14 16:37:13,609 INFO    HandlerThread:127609 [handler.py:handle_request_defer():184] handle defer: 12
+2024-05-14 16:37:13,610 DEBUG   SenderThread:127609 [sender.py:send_request():405] send_request: defer
+2024-05-14 16:37:13,610 INFO    SenderThread:127609 [sender.py:send_request_defer():609] handle sender defer: 12
+2024-05-14 16:37:13,610 INFO    SenderThread:127609 [file_stream.py:finish():601] file stream finish called
+2024-05-14 16:37:13,802 INFO    SenderThread:127609 [file_stream.py:finish():605] file stream finish is done
+2024-05-14 16:37:13,803 INFO    SenderThread:127609 [sender.py:transition_state():613] send defer: 13
+2024-05-14 16:37:13,803 DEBUG   HandlerThread:127609 [handler.py:handle_request():158] handle_request: defer
+2024-05-14 16:37:13,803 INFO    HandlerThread:127609 [handler.py:handle_request_defer():184] handle defer: 13
+2024-05-14 16:37:13,803 DEBUG   SenderThread:127609 [sender.py:send_request():405] send_request: defer
+2024-05-14 16:37:13,803 INFO    SenderThread:127609 [sender.py:send_request_defer():609] handle sender defer: 13
+2024-05-14 16:37:13,803 INFO    SenderThread:127609 [sender.py:transition_state():613] send defer: 14
+2024-05-14 16:37:13,803 DEBUG   HandlerThread:127609 [handler.py:handle_request():158] handle_request: defer
+2024-05-14 16:37:13,804 INFO    HandlerThread:127609 [handler.py:handle_request_defer():184] handle defer: 14
+2024-05-14 16:37:13,804 DEBUG   SenderThread:127609 [sender.py:send():378] send: final
+2024-05-14 16:37:13,804 DEBUG   SenderThread:127609 [sender.py:send():378] send: footer
+2024-05-14 16:37:13,804 DEBUG   SenderThread:127609 [sender.py:send_request():405] send_request: defer
+2024-05-14 16:37:13,804 INFO    SenderThread:127609 [sender.py:send_request_defer():609] handle sender defer: 14
+2024-05-14 16:37:13,805 DEBUG   HandlerThread:127609 [handler.py:handle_request():158] handle_request: poll_exit
+2024-05-14 16:37:13,805 DEBUG   SenderThread:127609 [sender.py:send_request():405] send_request: poll_exit
+2024-05-14 16:37:13,805 DEBUG   HandlerThread:127609 [handler.py:handle_request():158] handle_request: poll_exit
+2024-05-14 16:37:13,805 DEBUG   HandlerThread:127609 [handler.py:handle_request():158] handle_request: server_info
+2024-05-14 16:37:13,805 DEBUG   SenderThread:127609 [sender.py:send_request():405] send_request: poll_exit
+2024-05-14 16:37:13,806 DEBUG   SenderThread:127609 [sender.py:send_request():405] send_request: server_info
+2024-05-14 16:37:13,807 DEBUG   HandlerThread:127609 [handler.py:handle_request():158] handle_request: get_summary
+2024-05-14 16:37:13,807 DEBUG   HandlerThread:127609 [handler.py:handle_request():158] handle_request: sampled_history
+2024-05-14 16:37:13,807 DEBUG   HandlerThread:127609 [handler.py:handle_request():158] handle_request: internal_messages
+2024-05-14 16:37:13,868 INFO    MainThread:127609 [wandb_run.py:_footer_history_summary_info():3994] rendering history
+2024-05-14 16:37:13,868 INFO    MainThread:127609 [wandb_run.py:_footer_history_summary_info():4026] rendering summary
+2024-05-14 16:37:13,868 INFO    MainThread:127609 [wandb_run.py:_footer_sync_info():3953] logging synced files
+2024-05-14 16:37:13,869 DEBUG   HandlerThread:127609 [handler.py:handle_request():158] handle_request: shutdown
+2024-05-14 16:37:13,869 INFO    HandlerThread:127609 [handler.py:finish():882] shutting down handler
+2024-05-14 16:37:14,806 INFO    WriterThread:127609 [datastore.py:close():296] close: /data/cronscript/lm-evaluation-harness/wandb/run-20240514_163703-t88cgth5/run-t88cgth5.wandb
+2024-05-14 16:37:14,868 INFO    SenderThread:127609 [sender.py:finish():1545] shutting down sender
+2024-05-14 16:37:14,868 INFO    SenderThread:127609 [file_pusher.py:finish():169] shutting down file pusher
+2024-05-14 16:37:14,868 INFO    SenderThread:127609 [file_pusher.py:join():175] waiting for file pusher

lm-evaluation-harness/wandb/run-20240514_163703-t88cgth5/logs/debug.log

@@ -0,0 +1,29 @@
+2024-05-14 16:37:03,645 INFO    MainThread:126394 [wandb_setup.py:_flush():76] Current SDK version is 0.17.0
+2024-05-14 16:37:03,645 INFO    MainThread:126394 [wandb_setup.py:_flush():76] Configure stats pid to 126394
+2024-05-14 16:37:03,645 INFO    MainThread:126394 [wandb_setup.py:_flush():76] Loading settings from /root/.config/wandb/settings
+2024-05-14 16:37:03,645 INFO    MainThread:126394 [wandb_setup.py:_flush():76] Loading settings from /data/cronscript/lm-evaluation-harness/wandb/settings
+2024-05-14 16:37:03,645 INFO    MainThread:126394 [wandb_setup.py:_flush():76] Loading settings from environment variables: {}
+2024-05-14 16:37:03,645 INFO    MainThread:126394 [wandb_setup.py:_flush():76] Applying setup settings: {'_disable_service': False}
+2024-05-14 16:37:03,645 WARNING MainThread:126394 [wandb_setup.py:_flush():76] Could not find program at -m lm_eval.__main__
+2024-05-14 16:37:03,645 INFO    MainThread:126394 [wandb_setup.py:_flush():76] Inferring run settings from compute environment: {'program_relpath': None, 'program': '-m lm_eval.__main__'}
+2024-05-14 16:37:03,645 INFO    MainThread:126394 [wandb_setup.py:_flush():76] Applying login settings: {}
+2024-05-14 16:37:03,646 INFO    MainThread:126394 [wandb_init.py:_log_setup():520] Logging user logs to /data/cronscript/lm-evaluation-harness/wandb/run-20240514_163703-t88cgth5/logs/debug.log
+2024-05-14 16:37:03,646 INFO    MainThread:126394 [wandb_init.py:_log_setup():521] Logging internal logs to /data/cronscript/lm-evaluation-harness/wandb/run-20240514_163703-t88cgth5/logs/debug-internal.log
+2024-05-14 16:37:03,646 INFO    MainThread:126394 [wandb_init.py:init():560] calling init triggers
+2024-05-14 16:37:03,646 INFO    MainThread:126394 [wandb_init.py:init():567] wandb.init called with sweep_config: {}
+config: {}
+2024-05-14 16:37:03,646 INFO    MainThread:126394 [wandb_init.py:init():610] starting backend
+2024-05-14 16:37:03,646 INFO    MainThread:126394 [wandb_init.py:init():614] setting up manager
+2024-05-14 16:37:03,647 INFO    MainThread:126394 [backend.py:_multiprocessing_setup():105] multiprocessing start_methods=fork,spawn,forkserver, using: spawn
+2024-05-14 16:37:03,648 INFO    MainThread:126394 [wandb_init.py:init():622] backend started and connected
+2024-05-14 16:37:03,651 INFO    MainThread:126394 [wandb_init.py:init():711] updated telemetry
+2024-05-14 16:37:03,662 INFO    MainThread:126394 [wandb_init.py:init():744] communicating run to backend with 90.0 second timeout
+2024-05-14 16:37:03,921 INFO    MainThread:126394 [wandb_run.py:_on_init():2396] communicating current version
+2024-05-14 16:37:04,001 INFO    MainThread:126394 [wandb_run.py:_on_init():2405] got version response 
+2024-05-14 16:37:04,001 INFO    MainThread:126394 [wandb_init.py:init():795] starting run threads in backend
+2024-05-14 16:37:04,201 INFO    MainThread:126394 [wandb_run.py:_console_start():2374] atexit reg
+2024-05-14 16:37:04,201 INFO    MainThread:126394 [wandb_run.py:_redirect():2229] redirect: wrap_raw
+2024-05-14 16:37:04,201 INFO    MainThread:126394 [wandb_run.py:_redirect():2294] Wrapping output streams.
+2024-05-14 16:37:04,202 INFO    MainThread:126394 [wandb_run.py:_redirect():2319] Redirects installed.
+2024-05-14 16:37:04,203 INFO    MainThread:126394 [wandb_init.py:init():838] run started, returning control to user process
+2024-05-14 16:37:14,869 WARNING MsgRouterThr:126394 [router.py:message_loop():77] message_loop has been closed

lm-evaluation-harness/wandb/run-20240523_051727-xsim9azn/files/config.yaml

@@ -0,0 +1,43 @@
+wandb_version: 1
+
+_wandb:
+  desc: null
+  value:
+    python_version: 3.10.12
+    cli_version: 0.17.0
+    framework: huggingface
+    huggingface_version: 4.41.1
+    is_jupyter_run: false
+    is_kaggle_kernel: false
+    start_time: 1716441447
+    t:
+      1:
+      - 1
+      - 5
+      - 11
+      - 49
+      - 51
+      - 53
+      - 55
+      - 71
+      - 98
+      - 100
+      2:
+      - 1
+      - 5
+      - 11
+      - 49
+      - 51
+      - 53
+      - 55
+      - 71
+      - 98
+      - 100
+      3:
+      - 23
+      4: 3.10.12
+      5: 0.17.0
+      6: 4.41.1
+      8:
+      - 5
+      13: linux-x86_64

lm-evaluation-harness/wandb/run-20240523_051727-xsim9azn/files/output.log

@@ -0,0 +1,34 @@
+
+2024-05-23:05:17:28,264 INFO     [__main__.py:251] Verbosity set to INFO
+2024-05-23:05:17:32,782 INFO     [__main__.py:335] Selected Tasks: ['arc_easy', 'hellaswag', 'mrpc', 'openbookqa', 'sst2', 'winogrande']
+2024-05-23:05:17:32,783 INFO     [evaluator.py:131] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234
+2024-05-23:05:17:32,783 INFO     [evaluator.py:177] Initializing hf model, with arguments: {'pretrained': '/mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step16000'}
+2024-05-23:05:17:34,013 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_step16000 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_step16000/tree/main' for available files.

lm-evaluation-harness/wandb/run-20240523_051727-xsim9azn/files/requirements.txt

@@ -0,0 +1,155 @@
+DataProperty==1.0.1
+GitPython==3.1.43
+Jinja2==3.1.4
+Markdown==3.6
+MarkupSafe==2.1.5
+Pillow-SIMD==7.0.0.post3
+PyYAML==6.0
+Werkzeug==3.0.3
+absl-py==2.1.0
+accelerate==0.30.1
+aiohttp==3.9.5
+aiosignal==1.3.1
+async-timeout==4.0.3
+attrs==23.2.0
+av==9.2.0
+cachetools==5.3.3
+certifi==2024.2.2
+cffi==1.15.1
+cfgv==3.4.0
+chardet==5.2.0
+charset-normalizer==3.3.2
+click==8.1.7
+cmake==3.29.2
+colorama==0.4.6
+datasets==2.19.1
+deepspeed==0.12.4+hpu.synapse.v1.15.1
+dill==0.3.8
+distlib==0.3.8
+docker-pycreds==0.4.0
+einops==0.8.0
+evaluate==0.4.2
+exceptiongroup==1.2.1
+expecttest==0.2.1
+filelock==3.14.0
+frozenlist==1.4.1
+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==1.13.1
+sentencepiece==0.2.0
+sentry-sdk==2.2.1
+setproctitle==1.3.3
+setuptools==59.6.0
+setuptools==69.5.1
+six==1.16.0
+smmap==5.0.1
+sqlitedict==2.1.0
+symengine==0.11.0
+sympy==1.12
+tabledata==1.3.3
+tabulate==0.9.0
+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==4.41.1
+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

lm-evaluation-harness/wandb/run-20240523_051727-xsim9azn/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": "2024-05-23T05:17:28.136110",
+    "startedAt": "2024-05-23T05:17:27.718901",
+    "docker": null,
+    "cuda": null,
+    "args": [
+        "--model",
+        "hf",
+        "--model_args",
+        "pretrained=/mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step16000",
+        "--tasks",
+        "hellaswag,arc_easy,openbookqa,winogrande,sst2,mrpc",
+        "--batch_size",
+        "auto",
+        "--wandb_args",
+        "project=bharatgpt,group=trial_expt_2"
+    ],
+    "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-worker-0",
+    "username": "root",
+    "executable": "/usr/bin/python3",
+    "cpu_count": 80,
+    "cpu_count_logical": 160,
+    "cpu_freq": {
+        "current": 2333.75651875,
+        "min": 800.0,
+        "max": 3400.0
+    },
+    "cpu_freq_per_core": [
+        {
+            "current": 3327.471,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3327.443,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3399.275,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3325.804,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3399.225,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        }
+    ],
+    "disk": {
+        "/": {
+            "total": 877.6341285705566,
+            "used": 212.18668365478516
+        }
+    },
+    "memory": {
+        "total": 1007.43798828125
+    }
+}

lm-evaluation-harness/wandb/run-20240523_051727-xsim9azn/files/wandb-summary.json

@@ -0,0 +1 @@
+{"_wandb": {"runtime": 5}}

lm-evaluation-harness/wandb/run-20240523_051727-xsim9azn/logs/debug-internal.log

@@ -0,0 +1,182 @@
+2024-05-23 05:17:27,734 INFO    StreamThr :9831 [internal.py:wandb_internal():85] W&B internal server running at pid: 9831, started at: 2024-05-23 05:17:27.733399
+2024-05-23 05:17:27,735 DEBUG   HandlerThread:9831 [handler.py:handle_request():158] handle_request: status
+2024-05-23 05:17:27,736 INFO    WriterThread:9831 [datastore.py:open_for_write():87] open: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_051727-xsim9azn/run-xsim9azn.wandb
+2024-05-23 05:17:27,737 DEBUG   SenderThread:9831 [sender.py:send():378] send: header
+2024-05-23 05:17:27,747 DEBUG   SenderThread:9831 [sender.py:send():378] send: run
+2024-05-23 05:17:27,968 INFO    SenderThread:9831 [dir_watcher.py:__init__():211] watching files in: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_051727-xsim9azn/files
+2024-05-23 05:17:27,968 INFO    SenderThread:9831 [sender.py:_start_run_threads():1123] run started: xsim9azn with start time 1716441447.73305
+2024-05-23 05:17:27,975 DEBUG   HandlerThread:9831 [handler.py:handle_request():158] handle_request: check_version
+2024-05-23 05:17:27,975 DEBUG   SenderThread:9831 [sender.py:send_request():405] send_request: check_version
+2024-05-23 05:17:28,060 DEBUG   HandlerThread:9831 [handler.py:handle_request():158] handle_request: run_start
+2024-05-23 05:17:28,062 DEBUG   HandlerThread:9831 [system_info.py:__init__():26] System info init
+2024-05-23 05:17:28,062 DEBUG   HandlerThread:9831 [system_info.py:__init__():41] System info init done
+2024-05-23 05:17:28,062 INFO    HandlerThread:9831 [system_monitor.py:start():194] Starting system monitor
+2024-05-23 05:17:28,062 INFO    SystemMonitor:9831 [system_monitor.py:_start():158] Starting system asset monitoring threads
+2024-05-23 05:17:28,062 INFO    HandlerThread:9831 [system_monitor.py:probe():214] Collecting system info
+2024-05-23 05:17:28,063 INFO    SystemMonitor:9831 [interfaces.py:start():188] Started cpu monitoring
+2024-05-23 05:17:28,063 INFO    SystemMonitor:9831 [interfaces.py:start():188] Started disk monitoring
+2024-05-23 05:17:28,064 INFO    SystemMonitor:9831 [interfaces.py:start():188] Started memory monitoring
+2024-05-23 05:17:28,064 INFO    SystemMonitor:9831 [interfaces.py:start():188] Started network monitoring
+2024-05-23 05:17:28,136 DEBUG   HandlerThread:9831 [system_info.py:probe():150] Probing system
+2024-05-23 05:17:28,144 DEBUG   HandlerThread:9831 [system_info.py:_probe_git():135] Probing git
+2024-05-23 05:17:28,163 ERROR   HandlerThread:9831 [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'
+2024-05-23 05:17:28,163 DEBUG   HandlerThread:9831 [system_info.py:_probe_git():143] Probing git done
+2024-05-23 05:17:28,163 DEBUG   HandlerThread:9831 [system_info.py:probe():198] Probing system done
+2024-05-23 05:17:28,163 DEBUG   HandlerThread:9831 [system_monitor.py:probe():223] {'os': 'Linux-5.15.0-92-generic-x86_64-with-glibc2.35', 'python': '3.10.12', 'heartbeatAt': '2024-05-23T05:17:28.136110', 'startedAt': '2024-05-23T05:17:27.718901', 'docker': None, 'cuda': None, 'args': ('--model', 'hf', '--model_args', 'pretrained=/mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step16000', '--tasks', 'hellaswag,arc_easy,openbookqa,winogrande,sst2,mrpc', '--batch_size', 'auto', '--wandb_args', 'project=bharatgpt,group=trial_expt_2'), 'state': 'running', 'program': '-m lm_eval.__main__', 'codePathLocal': None, 'git': {'remote': 'https://github.com/EleutherAI/lm-evaluation-harness', 'commit': None}, 'email': None, 'root': '/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness', 'host': 'peacock-evaluation-debug-worker-0', 'username': 'root', 'executable': '/usr/bin/python3', 'cpu_count': 80, 'cpu_count_logical': 160, 'cpu_freq': {'current': 2333.75651875, 'min': 800.0, 'max': 3400.0}, 'cpu_freq_per_core': [{'current': 3327.471, 'min': 800.0, 'max': 3400.0}, {'current': 3327.443, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 3399.275, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 3325.804, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 3399.225, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}], 'disk': {'/': {'total': 877.6341285705566, 'used': 212.18668365478516}}, 'memory': {'total': 1007.43798828125}}
+2024-05-23 05:17:28,163 INFO    HandlerThread:9831 [system_monitor.py:probe():224] Finished collecting system info
+2024-05-23 05:17:28,163 INFO    HandlerThread:9831 [system_monitor.py:probe():227] Publishing system info
+2024-05-23 05:17:28,165 INFO    HandlerThread:9831 [system_monitor.py:probe():229] Finished publishing system info
+2024-05-23 05:17:28,169 DEBUG   SenderThread:9831 [sender.py:send():378] send: files
+2024-05-23 05:17:28,169 INFO    SenderThread:9831 [sender.py:_save_file():1389] saving file wandb-metadata.json with policy now
+2024-05-23 05:17:28,260 DEBUG   HandlerThread:9831 [handler.py:handle_request():158] handle_request: python_packages
+2024-05-23 05:17:28,261 DEBUG   SenderThread:9831 [sender.py:send_request():405] send_request: python_packages
+2024-05-23 05:17:28,261 DEBUG   HandlerThread:9831 [handler.py:handle_request():158] handle_request: stop_status
+2024-05-23 05:17:28,262 DEBUG   SenderThread:9831 [sender.py:send_request():405] send_request: stop_status
+2024-05-23 05:17:28,376 DEBUG   SenderThread:9831 [sender.py:send():378] send: telemetry
+2024-05-23 05:17:28,755 INFO    wandb-upload_0:9831 [upload_job.py:push():130] Uploaded file /tmp/tmp_3wqr1puwandb/c3u76i0j-wandb-metadata.json
+2024-05-23 05:17:28,970 INFO    Thread-12 :9831 [dir_watcher.py:_on_file_created():271] file/dir created: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_051727-xsim9azn/files/requirements.txt
+2024-05-23 05:17:28,970 INFO    Thread-12 :9831 [dir_watcher.py:_on_file_created():271] file/dir created: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_051727-xsim9azn/files/output.log
+2024-05-23 05:17:28,970 INFO    Thread-12 :9831 [dir_watcher.py:_on_file_created():271] file/dir created: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_051727-xsim9azn/files/wandb-metadata.json
+2024-05-23 05:17:30,971 INFO    Thread-12 :9831 [dir_watcher.py:_on_file_modified():288] file/dir modified: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_051727-xsim9azn/files/output.log
+2024-05-23 05:17:32,783 DEBUG   HandlerThread:9831 [handler.py:handle_request():158] handle_request: status_report
+2024-05-23 05:17:34,019 DEBUG   SenderThread:9831 [sender.py:send():378] send: exit
+2024-05-23 05:17:34,019 INFO    SenderThread:9831 [sender.py:send_exit():585] handling exit code: 1
+2024-05-23 05:17:34,019 INFO    SenderThread:9831 [sender.py:send_exit():587] handling runtime: 5
+2024-05-23 05:17:34,021 INFO    SenderThread:9831 [sender.py:_save_file():1389] saving file wandb-summary.json with policy end
+2024-05-23 05:17:34,021 INFO    SenderThread:9831 [sender.py:send_exit():593] send defer
+2024-05-23 05:17:34,021 DEBUG   HandlerThread:9831 [handler.py:handle_request():158] handle_request: defer
+2024-05-23 05:17:34,021 INFO    HandlerThread:9831 [handler.py:handle_request_defer():184] handle defer: 0
+2024-05-23 05:17:34,021 DEBUG   SenderThread:9831 [sender.py:send_request():405] send_request: defer
+2024-05-23 05:17:34,021 INFO    SenderThread:9831 [sender.py:send_request_defer():609] handle sender defer: 0
+2024-05-23 05:17:34,021 INFO    SenderThread:9831 [sender.py:transition_state():613] send defer: 1
+2024-05-23 05:17:34,021 DEBUG   HandlerThread:9831 [handler.py:handle_request():158] handle_request: defer
+2024-05-23 05:17:34,021 INFO    HandlerThread:9831 [handler.py:handle_request_defer():184] handle defer: 1
+2024-05-23 05:17:34,021 DEBUG   SenderThread:9831 [sender.py:send_request():405] send_request: defer
+2024-05-23 05:17:34,021 INFO    SenderThread:9831 [sender.py:send_request_defer():609] handle sender defer: 1
+2024-05-23 05:17:34,021 INFO    SenderThread:9831 [sender.py:transition_state():613] send defer: 2
+2024-05-23 05:17:34,022 DEBUG   HandlerThread:9831 [handler.py:handle_request():158] handle_request: defer
+2024-05-23 05:17:34,022 INFO    HandlerThread:9831 [handler.py:handle_request_defer():184] handle defer: 2
+2024-05-23 05:17:34,022 INFO    HandlerThread:9831 [system_monitor.py:finish():203] Stopping system monitor
+2024-05-23 05:17:34,022 DEBUG   SystemMonitor:9831 [system_monitor.py:_start():172] Starting system metrics aggregation loop
+2024-05-23 05:17:34,022 INFO    HandlerThread:9831 [interfaces.py:finish():200] Joined cpu monitor
+2024-05-23 05:17:34,022 DEBUG   SystemMonitor:9831 [system_monitor.py:_start():179] Finished system metrics aggregation loop
+2024-05-23 05:17:34,022 INFO    HandlerThread:9831 [interfaces.py:finish():200] Joined disk monitor
+2024-05-23 05:17:34,022 DEBUG   SystemMonitor:9831 [system_monitor.py:_start():183] Publishing last batch of metrics
+2024-05-23 05:17:34,022 INFO    HandlerThread:9831 [interfaces.py:finish():200] Joined memory monitor
+2024-05-23 05:17:34,024 INFO    HandlerThread:9831 [interfaces.py:finish():200] Joined network monitor
+2024-05-23 05:17:34,024 DEBUG   SenderThread:9831 [sender.py:send_request():405] send_request: defer
+2024-05-23 05:17:34,024 INFO    SenderThread:9831 [sender.py:send_request_defer():609] handle sender defer: 2
+2024-05-23 05:17:34,024 INFO    SenderThread:9831 [sender.py:transition_state():613] send defer: 3
+2024-05-23 05:17:34,024 DEBUG   HandlerThread:9831 [handler.py:handle_request():158] handle_request: defer
+2024-05-23 05:17:34,024 INFO    HandlerThread:9831 [handler.py:handle_request_defer():184] handle defer: 3
+2024-05-23 05:17:34,025 DEBUG   SenderThread:9831 [sender.py:send():378] send: stats
+2024-05-23 05:17:34,025 DEBUG   SenderThread:9831 [sender.py:send_request():405] send_request: defer
+2024-05-23 05:17:34,025 INFO    SenderThread:9831 [sender.py:send_request_defer():609] handle sender defer: 3
+2024-05-23 05:17:34,025 INFO    SenderThread:9831 [sender.py:transition_state():613] send defer: 4
+2024-05-23 05:17:34,025 DEBUG   HandlerThread:9831 [handler.py:handle_request():158] handle_request: defer
+2024-05-23 05:17:34,025 INFO    HandlerThread:9831 [handler.py:handle_request_defer():184] handle defer: 4
+2024-05-23 05:17:34,025 DEBUG   SenderThread:9831 [sender.py:send_request():405] send_request: defer
+2024-05-23 05:17:34,025 INFO    SenderThread:9831 [sender.py:send_request_defer():609] handle sender defer: 4
+2024-05-23 05:17:34,026 INFO    SenderThread:9831 [sender.py:transition_state():613] send defer: 5
+2024-05-23 05:17:34,026 DEBUG   HandlerThread:9831 [handler.py:handle_request():158] handle_request: defer
+2024-05-23 05:17:34,026 INFO    HandlerThread:9831 [handler.py:handle_request_defer():184] handle defer: 5
+2024-05-23 05:17:34,026 DEBUG   SenderThread:9831 [sender.py:send():378] send: summary
+2024-05-23 05:17:34,026 INFO    SenderThread:9831 [sender.py:_save_file():1389] saving file wandb-summary.json with policy end
+2024-05-23 05:17:34,027 DEBUG   SenderThread:9831 [sender.py:send_request():405] send_request: defer
+2024-05-23 05:17:34,027 INFO    SenderThread:9831 [sender.py:send_request_defer():609] handle sender defer: 5
+2024-05-23 05:17:34,027 INFO    SenderThread:9831 [sender.py:transition_state():613] send defer: 6
+2024-05-23 05:17:34,027 DEBUG   HandlerThread:9831 [handler.py:handle_request():158] handle_request: defer
+2024-05-23 05:17:34,027 INFO    HandlerThread:9831 [handler.py:handle_request_defer():184] handle defer: 6
+2024-05-23 05:17:34,027 DEBUG   SenderThread:9831 [sender.py:send_request():405] send_request: defer
+2024-05-23 05:17:34,027 INFO    SenderThread:9831 [sender.py:send_request_defer():609] handle sender defer: 6
+2024-05-23 05:17:34,029 DEBUG   HandlerThread:9831 [handler.py:handle_request():158] handle_request: status_report
+2024-05-23 05:17:34,096 INFO    SenderThread:9831 [sender.py:transition_state():613] send defer: 7
+2024-05-23 05:17:34,096 DEBUG   HandlerThread:9831 [handler.py:handle_request():158] handle_request: defer
+2024-05-23 05:17:34,096 INFO    HandlerThread:9831 [handler.py:handle_request_defer():184] handle defer: 7
+2024-05-23 05:17:34,097 DEBUG   SenderThread:9831 [sender.py:send_request():405] send_request: defer
+2024-05-23 05:17:34,097 INFO    SenderThread:9831 [sender.py:send_request_defer():609] handle sender defer: 7
+2024-05-23 05:17:34,975 INFO    Thread-12 :9831 [dir_watcher.py:_on_file_modified():288] file/dir modified: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_051727-xsim9azn/files/output.log
+2024-05-23 05:17:34,975 INFO    Thread-12 :9831 [dir_watcher.py:_on_file_modified():288] file/dir modified: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_051727-xsim9azn/files/config.yaml
+2024-05-23 05:17:34,975 INFO    Thread-12 :9831 [dir_watcher.py:_on_file_created():271] file/dir created: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_051727-xsim9azn/files/wandb-summary.json
+2024-05-23 05:17:35,019 DEBUG   HandlerThread:9831 [handler.py:handle_request():158] handle_request: poll_exit
+2024-05-23 05:17:36,397 INFO    SenderThread:9831 [sender.py:transition_state():613] send defer: 8
+2024-05-23 05:17:36,397 DEBUG   SenderThread:9831 [sender.py:send_request():405] send_request: poll_exit
+2024-05-23 05:17:36,397 DEBUG   HandlerThread:9831 [handler.py:handle_request():158] handle_request: defer
+2024-05-23 05:17:36,397 INFO    HandlerThread:9831 [handler.py:handle_request_defer():184] handle defer: 8
+2024-05-23 05:17:36,398 DEBUG   SenderThread:9831 [sender.py:send_request():405] send_request: defer
+2024-05-23 05:17:36,398 INFO    SenderThread:9831 [sender.py:send_request_defer():609] handle sender defer: 8
+2024-05-23 05:17:36,398 INFO    SenderThread:9831 [job_builder.py:build():432] Attempting to build job artifact
+2024-05-23 05:17:36,398 INFO    SenderThread:9831 [job_builder.py:_get_source_type():576] no source found
+2024-05-23 05:17:36,398 INFO    SenderThread:9831 [sender.py:transition_state():613] send defer: 9
+2024-05-23 05:17:36,398 DEBUG   HandlerThread:9831 [handler.py:handle_request():158] handle_request: defer
+2024-05-23 05:17:36,398 INFO    HandlerThread:9831 [handler.py:handle_request_defer():184] handle defer: 9
+2024-05-23 05:17:36,399 DEBUG   SenderThread:9831 [sender.py:send_request():405] send_request: defer
+2024-05-23 05:17:36,399 INFO    SenderThread:9831 [sender.py:send_request_defer():609] handle sender defer: 9
+2024-05-23 05:17:36,399 INFO    SenderThread:9831 [dir_watcher.py:finish():358] shutting down directory watcher
+2024-05-23 05:17:36,977 INFO    SenderThread:9831 [dir_watcher.py:_on_file_modified():288] file/dir modified: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_051727-xsim9azn/files/output.log
+2024-05-23 05:17:36,977 INFO    SenderThread:9831 [dir_watcher.py:finish():388] scan: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_051727-xsim9azn/files
+2024-05-23 05:17:36,977 INFO    SenderThread:9831 [dir_watcher.py:finish():402] scan save: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_051727-xsim9azn/files/wandb-summary.json wandb-summary.json
+2024-05-23 05:17:36,978 INFO    SenderThread:9831 [dir_watcher.py:finish():402] scan save: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_051727-xsim9azn/files/requirements.txt requirements.txt
+2024-05-23 05:17:36,978 INFO    SenderThread:9831 [dir_watcher.py:finish():402] scan save: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_051727-xsim9azn/files/wandb-metadata.json wandb-metadata.json
+2024-05-23 05:17:36,978 INFO    SenderThread:9831 [dir_watcher.py:finish():402] scan save: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_051727-xsim9azn/files/config.yaml config.yaml
+2024-05-23 05:17:36,978 INFO    SenderThread:9831 [dir_watcher.py:finish():402] scan save: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_051727-xsim9azn/files/output.log output.log
+2024-05-23 05:17:36,978 INFO    SenderThread:9831 [sender.py:transition_state():613] send defer: 10
+2024-05-23 05:17:36,978 DEBUG   HandlerThread:9831 [handler.py:handle_request():158] handle_request: defer
+2024-05-23 05:17:36,981 INFO    HandlerThread:9831 [handler.py:handle_request_defer():184] handle defer: 10
+2024-05-23 05:17:36,981 DEBUG   SenderThread:9831 [sender.py:send_request():405] send_request: defer
+2024-05-23 05:17:36,981 INFO    SenderThread:9831 [sender.py:send_request_defer():609] handle sender defer: 10
+2024-05-23 05:17:36,982 INFO    SenderThread:9831 [file_pusher.py:finish():169] shutting down file pusher
+2024-05-23 05:17:37,019 DEBUG   HandlerThread:9831 [handler.py:handle_request():158] handle_request: poll_exit
+2024-05-23 05:17:37,020 DEBUG   SenderThread:9831 [sender.py:send_request():405] send_request: poll_exit
+2024-05-23 05:17:37,239 INFO    wandb-upload_1:9831 [upload_job.py:push():130] Uploaded file /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_051727-xsim9azn/files/requirements.txt
+2024-05-23 05:17:37,382 INFO    wandb-upload_0:9831 [upload_job.py:push():130] Uploaded file /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_051727-xsim9azn/files/wandb-summary.json
+2024-05-23 05:17:37,455 INFO    wandb-upload_2:9831 [upload_job.py:push():130] Uploaded file /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_051727-xsim9azn/files/config.yaml
+2024-05-23 05:17:37,512 INFO    wandb-upload_3:9831 [upload_job.py:push():130] Uploaded file /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_051727-xsim9azn/files/output.log
+2024-05-23 05:17:37,712 INFO    Thread-11 (_thread_body):9831 [sender.py:transition_state():613] send defer: 11
+2024-05-23 05:17:37,713 DEBUG   HandlerThread:9831 [handler.py:handle_request():158] handle_request: defer
+2024-05-23 05:17:37,713 INFO    HandlerThread:9831 [handler.py:handle_request_defer():184] handle defer: 11
+2024-05-23 05:17:37,713 DEBUG   SenderThread:9831 [sender.py:send_request():405] send_request: defer
+2024-05-23 05:17:37,713 INFO    SenderThread:9831 [sender.py:send_request_defer():609] handle sender defer: 11
+2024-05-23 05:17:37,713 INFO    SenderThread:9831 [file_pusher.py:join():175] waiting for file pusher
+2024-05-23 05:17:37,713 INFO    SenderThread:9831 [sender.py:transition_state():613] send defer: 12
+2024-05-23 05:17:37,714 DEBUG   HandlerThread:9831 [handler.py:handle_request():158] handle_request: defer
+2024-05-23 05:17:37,714 INFO    HandlerThread:9831 [handler.py:handle_request_defer():184] handle defer: 12
+2024-05-23 05:17:37,714 DEBUG   SenderThread:9831 [sender.py:send_request():405] send_request: defer
+2024-05-23 05:17:37,714 INFO    SenderThread:9831 [sender.py:send_request_defer():609] handle sender defer: 12
+2024-05-23 05:17:37,714 INFO    SenderThread:9831 [file_stream.py:finish():601] file stream finish called
+2024-05-23 05:17:37,926 INFO    SenderThread:9831 [file_stream.py:finish():605] file stream finish is done
+2024-05-23 05:17:37,926 INFO    SenderThread:9831 [sender.py:transition_state():613] send defer: 13
+2024-05-23 05:17:37,926 DEBUG   HandlerThread:9831 [handler.py:handle_request():158] handle_request: defer
+2024-05-23 05:17:37,926 INFO    HandlerThread:9831 [handler.py:handle_request_defer():184] handle defer: 13
+2024-05-23 05:17:37,927 DEBUG   SenderThread:9831 [sender.py:send_request():405] send_request: defer
+2024-05-23 05:17:37,927 INFO    SenderThread:9831 [sender.py:send_request_defer():609] handle sender defer: 13
+2024-05-23 05:17:37,927 INFO    SenderThread:9831 [sender.py:transition_state():613] send defer: 14
+2024-05-23 05:17:37,927 DEBUG   HandlerThread:9831 [handler.py:handle_request():158] handle_request: defer
+2024-05-23 05:17:37,927 INFO    HandlerThread:9831 [handler.py:handle_request_defer():184] handle defer: 14
+2024-05-23 05:17:37,927 DEBUG   SenderThread:9831 [sender.py:send():378] send: final
+2024-05-23 05:17:37,927 DEBUG   SenderThread:9831 [sender.py:send():378] send: footer
+2024-05-23 05:17:37,927 DEBUG   SenderThread:9831 [sender.py:send_request():405] send_request: defer
+2024-05-23 05:17:37,927 INFO    SenderThread:9831 [sender.py:send_request_defer():609] handle sender defer: 14
+2024-05-23 05:17:37,928 DEBUG   HandlerThread:9831 [handler.py:handle_request():158] handle_request: poll_exit
+2024-05-23 05:17:37,928 DEBUG   SenderThread:9831 [sender.py:send_request():405] send_request: poll_exit
+2024-05-23 05:17:37,928 DEBUG   HandlerThread:9831 [handler.py:handle_request():158] handle_request: poll_exit
+2024-05-23 05:17:37,928 DEBUG   HandlerThread:9831 [handler.py:handle_request():158] handle_request: server_info
+2024-05-23 05:17:37,929 DEBUG   SenderThread:9831 [sender.py:send_request():405] send_request: poll_exit
+2024-05-23 05:17:37,929 DEBUG   HandlerThread:9831 [handler.py:handle_request():158] handle_request: get_summary
+2024-05-23 05:17:37,929 DEBUG   SenderThread:9831 [sender.py:send_request():405] send_request: server_info
+2024-05-23 05:17:37,929 DEBUG   HandlerThread:9831 [handler.py:handle_request():158] handle_request: sampled_history
+2024-05-23 05:17:37,930 DEBUG   HandlerThread:9831 [handler.py:handle_request():158] handle_request: internal_messages
+2024-05-23 05:17:37,983 INFO    MainThread:9831 [wandb_run.py:_footer_history_summary_info():3994] rendering history
+2024-05-23 05:17:37,983 INFO    MainThread:9831 [wandb_run.py:_footer_history_summary_info():4026] rendering summary
+2024-05-23 05:17:37,983 INFO    MainThread:9831 [wandb_run.py:_footer_sync_info():3953] logging synced files
+2024-05-23 05:17:37,983 DEBUG   HandlerThread:9831 [handler.py:handle_request():158] handle_request: shutdown
+2024-05-23 05:17:37,983 INFO    HandlerThread:9831 [handler.py:finish():882] shutting down handler
+2024-05-23 05:17:38,929 INFO    WriterThread:9831 [datastore.py:close():296] close: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_051727-xsim9azn/run-xsim9azn.wandb
+2024-05-23 05:17:38,983 INFO    SenderThread:9831 [sender.py:finish():1545] shutting down sender
+2024-05-23 05:17:38,983 INFO    SenderThread:9831 [file_pusher.py:finish():169] shutting down file pusher
+2024-05-23 05:17:38,983 INFO    SenderThread:9831 [file_pusher.py:join():175] waiting for file pusher

lm-evaluation-harness/wandb/run-20240523_051727-xsim9azn/logs/debug.log

@@ -0,0 +1,29 @@
+2024-05-23 05:17:27,730 INFO    MainThread:9676 [wandb_setup.py:_flush():76] Current SDK version is 0.17.0
+2024-05-23 05:17:27,730 INFO    MainThread:9676 [wandb_setup.py:_flush():76] Configure stats pid to 9676
+2024-05-23 05:17:27,730 INFO    MainThread:9676 [wandb_setup.py:_flush():76] Loading settings from /root/.config/wandb/settings
+2024-05-23 05:17:27,730 INFO    MainThread:9676 [wandb_setup.py:_flush():76] Loading settings from /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/settings
+2024-05-23 05:17:27,730 INFO    MainThread:9676 [wandb_setup.py:_flush():76] Loading settings from environment variables: {}
+2024-05-23 05:17:27,730 INFO    MainThread:9676 [wandb_setup.py:_flush():76] Applying setup settings: {'_disable_service': False}
+2024-05-23 05:17:27,730 WARNING MainThread:9676 [wandb_setup.py:_flush():76] Could not find program at -m lm_eval.__main__
+2024-05-23 05:17:27,730 INFO    MainThread:9676 [wandb_setup.py:_flush():76] Inferring run settings from compute environment: {'program_relpath': None, 'program': '-m lm_eval.__main__'}
+2024-05-23 05:17:27,730 INFO    MainThread:9676 [wandb_setup.py:_flush():76] Applying login settings: {}
+2024-05-23 05:17:27,730 INFO    MainThread:9676 [wandb_init.py:_log_setup():520] Logging user logs to /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_051727-xsim9azn/logs/debug.log
+2024-05-23 05:17:27,730 INFO    MainThread:9676 [wandb_init.py:_log_setup():521] Logging internal logs to /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_051727-xsim9azn/logs/debug-internal.log
+2024-05-23 05:17:27,730 INFO    MainThread:9676 [wandb_init.py:init():560] calling init triggers
+2024-05-23 05:17:27,730 INFO    MainThread:9676 [wandb_init.py:init():567] wandb.init called with sweep_config: {}
+config: {}
+2024-05-23 05:17:27,730 INFO    MainThread:9676 [wandb_init.py:init():610] starting backend
+2024-05-23 05:17:27,730 INFO    MainThread:9676 [wandb_init.py:init():614] setting up manager
+2024-05-23 05:17:27,732 INFO    MainThread:9676 [backend.py:_multiprocessing_setup():105] multiprocessing start_methods=fork,spawn,forkserver, using: spawn
+2024-05-23 05:17:27,732 INFO    MainThread:9676 [wandb_init.py:init():622] backend started and connected
+2024-05-23 05:17:27,735 INFO    MainThread:9676 [wandb_init.py:init():711] updated telemetry
+2024-05-23 05:17:27,746 INFO    MainThread:9676 [wandb_init.py:init():744] communicating run to backend with 90.0 second timeout
+2024-05-23 05:17:27,974 INFO    MainThread:9676 [wandb_run.py:_on_init():2396] communicating current version
+2024-05-23 05:17:28,056 INFO    MainThread:9676 [wandb_run.py:_on_init():2405] got version response 
+2024-05-23 05:17:28,056 INFO    MainThread:9676 [wandb_init.py:init():795] starting run threads in backend
+2024-05-23 05:17:28,261 INFO    MainThread:9676 [wandb_run.py:_console_start():2374] atexit reg
+2024-05-23 05:17:28,261 INFO    MainThread:9676 [wandb_run.py:_redirect():2229] redirect: wrap_raw
+2024-05-23 05:17:28,261 INFO    MainThread:9676 [wandb_run.py:_redirect():2294] Wrapping output streams.
+2024-05-23 05:17:28,261 INFO    MainThread:9676 [wandb_run.py:_redirect():2319] Redirects installed.
+2024-05-23 05:17:28,262 INFO    MainThread:9676 [wandb_init.py:init():838] run started, returning control to user process
+2024-05-23 05:17:38,985 WARNING MsgRouterThr:9676 [router.py:message_loop():77] message_loop has been closed

lm-evaluation-harness/wandb/run-20240523_131005-q495m75j/files/config.yaml

@@ -0,0 +1,43 @@
+wandb_version: 1
+
+_wandb:
+  desc: null
+  value:
+    python_version: 3.10.12
+    cli_version: 0.17.0
+    framework: huggingface
+    huggingface_version: 4.41.1
+    is_jupyter_run: false
+    is_kaggle_kernel: false
+    start_time: 1716469805
+    t:
+      1:
+      - 1
+      - 5
+      - 11
+      - 49
+      - 51
+      - 53
+      - 55
+      - 71
+      - 98
+      - 100
+      2:
+      - 1
+      - 5
+      - 11
+      - 49
+      - 51
+      - 53
+      - 55
+      - 71
+      - 98
+      - 100
+      3:
+      - 23
+      4: 3.10.12
+      5: 0.17.0
+      6: 4.41.1
+      8:
+      - 5
+      13: linux-x86_64

lm-evaluation-harness/wandb/run-20240523_131005-q495m75j/files/output.log

@@ -0,0 +1,34 @@
+
+2024-05-23:13:10:06,122 INFO     [__main__.py:251] Verbosity set to INFO
+2024-05-23:13:10:14,511 INFO     [__main__.py:335] Selected Tasks: ['arc_easy', 'hellaswag', 'mrpc', 'openbookqa', 'sst2', 'winogrande']
+2024-05-23:13:10:14,513 INFO     [evaluator.py:131] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234
+2024-05-23:13:10:14,513 INFO     [evaluator.py:177] Initializing hf model, with arguments: {'pretrained': '/mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step28000'}
+2024-05-23:13:10:16,939 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_step28000 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_step28000/tree/main' for available files.

lm-evaluation-harness/wandb/run-20240523_131005-q495m75j/files/requirements.txt

@@ -0,0 +1,155 @@
+DataProperty==1.0.1
+GitPython==3.1.43
+Jinja2==3.1.4
+Markdown==3.6
+MarkupSafe==2.1.5
+Pillow-SIMD==7.0.0.post3
+PyYAML==6.0
+Werkzeug==3.0.3
+absl-py==2.1.0
+accelerate==0.30.1
+aiohttp==3.9.5
+aiosignal==1.3.1
+async-timeout==4.0.3
+attrs==23.2.0
+av==9.2.0
+cachetools==5.3.3
+certifi==2024.2.2
+cffi==1.15.1
+cfgv==3.4.0
+chardet==5.2.0
+charset-normalizer==3.3.2
+click==8.1.7
+cmake==3.29.2
+colorama==0.4.6
+datasets==2.19.1
+deepspeed==0.12.4+hpu.synapse.v1.15.1
+dill==0.3.8
+distlib==0.3.8
+docker-pycreds==0.4.0
+einops==0.8.0
+evaluate==0.4.2
+exceptiongroup==1.2.1
+expecttest==0.2.1
+filelock==3.14.0
+frozenlist==1.4.1
+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==1.13.1
+sentencepiece==0.2.0
+sentry-sdk==2.3.0
+setproctitle==1.3.3
+setuptools==59.6.0
+setuptools==69.5.1
+six==1.16.0
+smmap==5.0.1
+sqlitedict==2.1.0
+symengine==0.11.0
+sympy==1.12
+tabledata==1.3.3
+tabulate==0.9.0
+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==4.41.1
+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

lm-evaluation-harness/wandb/run-20240523_131005-q495m75j/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": "2024-05-23T13:10:05.921078",
+    "startedAt": "2024-05-23T13:10:05.381984",
+    "docker": null,
+    "cuda": null,
+    "args": [
+        "--model",
+        "hf",
+        "--model_args",
+        "pretrained=/mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step28000",
+        "--tasks",
+        "hellaswag,arc_easy,openbookqa,winogrande,sst2,mrpc",
+        "--batch_size",
+        "auto",
+        "--wandb_args",
+        "project=bharatgpt,group=trial_expt_2"
+    ],
+    "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-worker-0",
+    "username": "root",
+    "executable": "/usr/bin/python3",
+    "cpu_count": 80,
+    "cpu_count_logical": 160,
+    "cpu_freq": {
+        "current": 2327.50000625,
+        "min": 800.0,
+        "max": 3400.0
+    },
+    "cpu_freq_per_core": [
+        {
+            "current": 3399.997,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 3400.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        },
+        {
+            "current": 2300.0,
+            "min": 800.0,
+            "max": 3400.0
+        }
+    ],
+    "disk": {
+        "/": {
+            "total": 877.6341285705566,
+            "used": 211.62947463989258
+        }
+    },
+    "memory": {
+        "total": 1007.4379539489746
+    }
+}

lm-evaluation-harness/wandb/run-20240523_131005-q495m75j/files/wandb-summary.json

@@ -0,0 +1 @@
+{"_wandb": {"runtime": 11}}

lm-evaluation-harness/wandb/run-20240523_131005-q495m75j/logs/debug-internal.log

@@ -0,0 +1,183 @@
+2024-05-23 13:10:05,402 INFO    StreamThr :3633 [internal.py:wandb_internal():85] W&B internal server running at pid: 3633, started at: 2024-05-23 13:10:05.400548
+2024-05-23 13:10:05,407 DEBUG   HandlerThread:3633 [handler.py:handle_request():158] handle_request: status
+2024-05-23 13:10:05,409 INFO    WriterThread:3633 [datastore.py:open_for_write():87] open: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_131005-q495m75j/run-q495m75j.wandb
+2024-05-23 13:10:05,410 DEBUG   SenderThread:3633 [sender.py:send():378] send: header
+2024-05-23 13:10:05,413 DEBUG   SenderThread:3633 [sender.py:send():378] send: run
+2024-05-23 13:10:05,710 INFO    SenderThread:3633 [dir_watcher.py:__init__():211] watching files in: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_131005-q495m75j/files
+2024-05-23 13:10:05,711 INFO    SenderThread:3633 [sender.py:_start_run_threads():1123] run started: q495m75j with start time 1716469805.401001
+2024-05-23 13:10:05,719 DEBUG   HandlerThread:3633 [handler.py:handle_request():158] handle_request: check_version
+2024-05-23 13:10:05,719 DEBUG   SenderThread:3633 [sender.py:send_request():405] send_request: check_version
+2024-05-23 13:10:05,846 DEBUG   HandlerThread:3633 [handler.py:handle_request():158] handle_request: run_start
+2024-05-23 13:10:05,848 DEBUG   HandlerThread:3633 [system_info.py:__init__():26] System info init
+2024-05-23 13:10:05,848 DEBUG   HandlerThread:3633 [system_info.py:__init__():41] System info init done
+2024-05-23 13:10:05,848 INFO    HandlerThread:3633 [system_monitor.py:start():194] Starting system monitor
+2024-05-23 13:10:05,848 INFO    SystemMonitor:3633 [system_monitor.py:_start():158] Starting system asset monitoring threads
+2024-05-23 13:10:05,848 INFO    HandlerThread:3633 [system_monitor.py:probe():214] Collecting system info
+2024-05-23 13:10:05,855 INFO    SystemMonitor:3633 [interfaces.py:start():188] Started cpu monitoring
+2024-05-23 13:10:05,855 INFO    SystemMonitor:3633 [interfaces.py:start():188] Started disk monitoring
+2024-05-23 13:10:05,856 INFO    SystemMonitor:3633 [interfaces.py:start():188] Started memory monitoring
+2024-05-23 13:10:05,856 INFO    SystemMonitor:3633 [interfaces.py:start():188] Started network monitoring
+2024-05-23 13:10:05,921 DEBUG   HandlerThread:3633 [system_info.py:probe():150] Probing system
+2024-05-23 13:10:05,924 DEBUG   HandlerThread:3633 [system_info.py:_probe_git():135] Probing git
+2024-05-23 13:10:05,933 ERROR   HandlerThread:3633 [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'
+2024-05-23 13:10:05,933 DEBUG   HandlerThread:3633 [system_info.py:_probe_git():143] Probing git done
+2024-05-23 13:10:05,933 DEBUG   HandlerThread:3633 [system_info.py:probe():198] Probing system done
+2024-05-23 13:10:05,933 DEBUG   HandlerThread:3633 [system_monitor.py:probe():223] {'os': 'Linux-5.15.0-92-generic-x86_64-with-glibc2.35', 'python': '3.10.12', 'heartbeatAt': '2024-05-23T13:10:05.921078', 'startedAt': '2024-05-23T13:10:05.381984', 'docker': None, 'cuda': None, 'args': ('--model', 'hf', '--model_args', 'pretrained=/mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step28000', '--tasks', 'hellaswag,arc_easy,openbookqa,winogrande,sst2,mrpc', '--batch_size', 'auto', '--wandb_args', 'project=bharatgpt,group=trial_expt_2'), 'state': 'running', 'program': '-m lm_eval.__main__', 'codePathLocal': None, 'git': {'remote': 'https://github.com/EleutherAI/lm-evaluation-harness', 'commit': None}, 'email': None, 'root': '/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness', 'host': 'peacock-evaluation-worker-0', 'username': 'root', 'executable': '/usr/bin/python3', 'cpu_count': 80, 'cpu_count_logical': 160, 'cpu_freq': {'current': 2327.50000625, 'min': 800.0, 'max': 3400.0}, 'cpu_freq_per_core': [{'current': 3399.997, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 3400.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}, {'current': 2300.0, 'min': 800.0, 'max': 3400.0}], 'disk': {'/': {'total': 877.6341285705566, 'used': 211.62947463989258}}, 'memory': {'total': 1007.4379539489746}}
+2024-05-23 13:10:05,934 INFO    HandlerThread:3633 [system_monitor.py:probe():224] Finished collecting system info
+2024-05-23 13:10:05,934 INFO    HandlerThread:3633 [system_monitor.py:probe():227] Publishing system info
+2024-05-23 13:10:05,937 INFO    HandlerThread:3633 [system_monitor.py:probe():229] Finished publishing system info
+2024-05-23 13:10:05,942 DEBUG   SenderThread:3633 [sender.py:send():378] send: files
+2024-05-23 13:10:05,942 INFO    SenderThread:3633 [sender.py:_save_file():1389] saving file wandb-metadata.json with policy now
+2024-05-23 13:10:06,114 DEBUG   HandlerThread:3633 [handler.py:handle_request():158] handle_request: python_packages
+2024-05-23 13:10:06,115 DEBUG   SenderThread:3633 [sender.py:send_request():405] send_request: python_packages
+2024-05-23 13:10:06,115 DEBUG   HandlerThread:3633 [handler.py:handle_request():158] handle_request: stop_status
+2024-05-23 13:10:06,118 DEBUG   SenderThread:3633 [sender.py:send_request():405] send_request: stop_status
+2024-05-23 13:10:06,274 DEBUG   SenderThread:3633 [sender.py:send():378] send: telemetry
+2024-05-23 13:10:06,544 INFO    wandb-upload_0:3633 [upload_job.py:push():130] Uploaded file /tmp/tmpsaxvtbfqwandb/swf409i5-wandb-metadata.json
+2024-05-23 13:10:06,713 INFO    Thread-12 :3633 [dir_watcher.py:_on_file_created():271] file/dir created: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_131005-q495m75j/files/output.log
+2024-05-23 13:10:06,714 INFO    Thread-12 :3633 [dir_watcher.py:_on_file_created():271] file/dir created: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_131005-q495m75j/files/wandb-metadata.json
+2024-05-23 13:10:06,714 INFO    Thread-12 :3633 [dir_watcher.py:_on_file_created():271] file/dir created: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_131005-q495m75j/files/requirements.txt
+2024-05-23 13:10:08,713 INFO    Thread-12 :3633 [dir_watcher.py:_on_file_modified():288] file/dir modified: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_131005-q495m75j/files/output.log
+2024-05-23 13:10:11,277 DEBUG   HandlerThread:3633 [handler.py:handle_request():158] handle_request: status_report
+2024-05-23 13:10:16,514 DEBUG   HandlerThread:3633 [handler.py:handle_request():158] handle_request: status_report
+2024-05-23 13:10:16,720 INFO    Thread-12 :3633 [dir_watcher.py:_on_file_modified():288] file/dir modified: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_131005-q495m75j/files/output.log
+2024-05-23 13:10:16,957 DEBUG   SenderThread:3633 [sender.py:send():378] send: exit
+2024-05-23 13:10:16,957 INFO    SenderThread:3633 [sender.py:send_exit():585] handling exit code: 1
+2024-05-23 13:10:16,957 INFO    SenderThread:3633 [sender.py:send_exit():587] handling runtime: 11
+2024-05-23 13:10:16,963 INFO    SenderThread:3633 [sender.py:_save_file():1389] saving file wandb-summary.json with policy end
+2024-05-23 13:10:16,963 INFO    SenderThread:3633 [sender.py:send_exit():593] send defer
+2024-05-23 13:10:16,963 DEBUG   HandlerThread:3633 [handler.py:handle_request():158] handle_request: defer
+2024-05-23 13:10:16,963 INFO    HandlerThread:3633 [handler.py:handle_request_defer():184] handle defer: 0
+2024-05-23 13:10:16,963 DEBUG   SenderThread:3633 [sender.py:send_request():405] send_request: defer
+2024-05-23 13:10:16,963 INFO    SenderThread:3633 [sender.py:send_request_defer():609] handle sender defer: 0
+2024-05-23 13:10:16,963 INFO    SenderThread:3633 [sender.py:transition_state():613] send defer: 1
+2024-05-23 13:10:16,963 DEBUG   HandlerThread:3633 [handler.py:handle_request():158] handle_request: defer
+2024-05-23 13:10:16,963 INFO    HandlerThread:3633 [handler.py:handle_request_defer():184] handle defer: 1
+2024-05-23 13:10:16,964 DEBUG   SenderThread:3633 [sender.py:send_request():405] send_request: defer
+2024-05-23 13:10:16,964 INFO    SenderThread:3633 [sender.py:send_request_defer():609] handle sender defer: 1
+2024-05-23 13:10:16,964 INFO    SenderThread:3633 [sender.py:transition_state():613] send defer: 2
+2024-05-23 13:10:16,964 DEBUG   HandlerThread:3633 [handler.py:handle_request():158] handle_request: defer
+2024-05-23 13:10:16,964 INFO    HandlerThread:3633 [handler.py:handle_request_defer():184] handle defer: 2
+2024-05-23 13:10:16,964 INFO    HandlerThread:3633 [system_monitor.py:finish():203] Stopping system monitor
+2024-05-23 13:10:16,964 INFO    HandlerThread:3633 [interfaces.py:finish():200] Joined cpu monitor
+2024-05-23 13:10:16,965 INFO    HandlerThread:3633 [interfaces.py:finish():200] Joined disk monitor
+2024-05-23 13:10:16,965 INFO    HandlerThread:3633 [interfaces.py:finish():200] Joined memory monitor
+2024-05-23 13:10:16,965 INFO    HandlerThread:3633 [interfaces.py:finish():200] Joined network monitor
+2024-05-23 13:10:16,964 DEBUG   SystemMonitor:3633 [system_monitor.py:_start():172] Starting system metrics aggregation loop
+2024-05-23 13:10:16,965 DEBUG   SystemMonitor:3633 [system_monitor.py:_start():179] Finished system metrics aggregation loop
+2024-05-23 13:10:16,965 DEBUG   SystemMonitor:3633 [system_monitor.py:_start():183] Publishing last batch of metrics
+2024-05-23 13:10:16,968 DEBUG   SenderThread:3633 [sender.py:send_request():405] send_request: defer
+2024-05-23 13:10:16,968 INFO    SenderThread:3633 [sender.py:send_request_defer():609] handle sender defer: 2
+2024-05-23 13:10:16,968 INFO    SenderThread:3633 [sender.py:transition_state():613] send defer: 3
+2024-05-23 13:10:16,968 DEBUG   SenderThread:3633 [sender.py:send():378] send: stats
+2024-05-23 13:10:16,969 DEBUG   HandlerThread:3633 [handler.py:handle_request():158] handle_request: defer
+2024-05-23 13:10:16,969 INFO    HandlerThread:3633 [handler.py:handle_request_defer():184] handle defer: 3
+2024-05-23 13:10:16,969 DEBUG   SenderThread:3633 [sender.py:send_request():405] send_request: defer
+2024-05-23 13:10:16,969 INFO    SenderThread:3633 [sender.py:send_request_defer():609] handle sender defer: 3
+2024-05-23 13:10:16,969 INFO    SenderThread:3633 [sender.py:transition_state():613] send defer: 4
+2024-05-23 13:10:16,969 DEBUG   HandlerThread:3633 [handler.py:handle_request():158] handle_request: defer
+2024-05-23 13:10:16,969 INFO    HandlerThread:3633 [handler.py:handle_request_defer():184] handle defer: 4
+2024-05-23 13:10:16,970 DEBUG   SenderThread:3633 [sender.py:send_request():405] send_request: defer
+2024-05-23 13:10:16,970 INFO    SenderThread:3633 [sender.py:send_request_defer():609] handle sender defer: 4
+2024-05-23 13:10:16,970 INFO    SenderThread:3633 [sender.py:transition_state():613] send defer: 5
+2024-05-23 13:10:16,970 DEBUG   HandlerThread:3633 [handler.py:handle_request():158] handle_request: defer
+2024-05-23 13:10:16,970 INFO    HandlerThread:3633 [handler.py:handle_request_defer():184] handle defer: 5
+2024-05-23 13:10:16,970 DEBUG   SenderThread:3633 [sender.py:send():378] send: summary
+2024-05-23 13:10:16,971 INFO    SenderThread:3633 [sender.py:_save_file():1389] saving file wandb-summary.json with policy end
+2024-05-23 13:10:16,971 DEBUG   SenderThread:3633 [sender.py:send_request():405] send_request: defer
+2024-05-23 13:10:16,971 INFO    SenderThread:3633 [sender.py:send_request_defer():609] handle sender defer: 5
+2024-05-23 13:10:16,971 INFO    SenderThread:3633 [sender.py:transition_state():613] send defer: 6
+2024-05-23 13:10:16,971 DEBUG   HandlerThread:3633 [handler.py:handle_request():158] handle_request: defer
+2024-05-23 13:10:16,971 INFO    HandlerThread:3633 [handler.py:handle_request_defer():184] handle defer: 6
+2024-05-23 13:10:16,971 DEBUG   SenderThread:3633 [sender.py:send_request():405] send_request: defer
+2024-05-23 13:10:16,971 INFO    SenderThread:3633 [sender.py:send_request_defer():609] handle sender defer: 6
+2024-05-23 13:10:16,976 DEBUG   HandlerThread:3633 [handler.py:handle_request():158] handle_request: status_report
+2024-05-23 13:10:17,061 INFO    SenderThread:3633 [sender.py:transition_state():613] send defer: 7
+2024-05-23 13:10:17,061 DEBUG   HandlerThread:3633 [handler.py:handle_request():158] handle_request: defer
+2024-05-23 13:10:17,061 INFO    HandlerThread:3633 [handler.py:handle_request_defer():184] handle defer: 7
+2024-05-23 13:10:17,061 DEBUG   SenderThread:3633 [sender.py:send_request():405] send_request: defer
+2024-05-23 13:10:17,061 INFO    SenderThread:3633 [sender.py:send_request_defer():609] handle sender defer: 7
+2024-05-23 13:10:17,722 INFO    Thread-12 :3633 [dir_watcher.py:_on_file_modified():288] file/dir modified: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_131005-q495m75j/files/config.yaml
+2024-05-23 13:10:17,722 INFO    Thread-12 :3633 [dir_watcher.py:_on_file_created():271] file/dir created: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_131005-q495m75j/files/wandb-summary.json
+2024-05-23 13:10:17,957 DEBUG   HandlerThread:3633 [handler.py:handle_request():158] handle_request: poll_exit
+2024-05-23 13:10:18,299 INFO    SenderThread:3633 [sender.py:transition_state():613] send defer: 8
+2024-05-23 13:10:18,300 DEBUG   SenderThread:3633 [sender.py:send_request():405] send_request: poll_exit
+2024-05-23 13:10:18,300 DEBUG   HandlerThread:3633 [handler.py:handle_request():158] handle_request: defer
+2024-05-23 13:10:18,300 INFO    HandlerThread:3633 [handler.py:handle_request_defer():184] handle defer: 8
+2024-05-23 13:10:18,300 DEBUG   SenderThread:3633 [sender.py:send_request():405] send_request: defer
+2024-05-23 13:10:18,300 INFO    SenderThread:3633 [sender.py:send_request_defer():609] handle sender defer: 8
+2024-05-23 13:10:18,300 INFO    SenderThread:3633 [job_builder.py:build():432] Attempting to build job artifact
+2024-05-23 13:10:18,301 INFO    SenderThread:3633 [job_builder.py:_get_source_type():576] no source found
+2024-05-23 13:10:18,301 INFO    SenderThread:3633 [sender.py:transition_state():613] send defer: 9
+2024-05-23 13:10:18,301 DEBUG   HandlerThread:3633 [handler.py:handle_request():158] handle_request: defer
+2024-05-23 13:10:18,301 INFO    HandlerThread:3633 [handler.py:handle_request_defer():184] handle defer: 9
+2024-05-23 13:10:18,301 DEBUG   SenderThread:3633 [sender.py:send_request():405] send_request: defer
+2024-05-23 13:10:18,301 INFO    SenderThread:3633 [sender.py:send_request_defer():609] handle sender defer: 9
+2024-05-23 13:10:18,301 INFO    SenderThread:3633 [dir_watcher.py:finish():358] shutting down directory watcher
+2024-05-23 13:10:18,723 INFO    SenderThread:3633 [dir_watcher.py:_on_file_modified():288] file/dir modified: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_131005-q495m75j/files/output.log
+2024-05-23 13:10:18,724 INFO    SenderThread:3633 [dir_watcher.py:finish():388] scan: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_131005-q495m75j/files
+2024-05-23 13:10:18,724 INFO    SenderThread:3633 [dir_watcher.py:finish():402] scan save: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_131005-q495m75j/files/output.log output.log
+2024-05-23 13:10:18,724 INFO    SenderThread:3633 [dir_watcher.py:finish():402] scan save: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_131005-q495m75j/files/wandb-summary.json wandb-summary.json
+2024-05-23 13:10:18,726 INFO    SenderThread:3633 [dir_watcher.py:finish():402] scan save: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_131005-q495m75j/files/wandb-metadata.json wandb-metadata.json
+2024-05-23 13:10:18,728 INFO    SenderThread:3633 [dir_watcher.py:finish():402] scan save: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_131005-q495m75j/files/requirements.txt requirements.txt
+2024-05-23 13:10:18,728 INFO    SenderThread:3633 [dir_watcher.py:finish():402] scan save: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_131005-q495m75j/files/config.yaml config.yaml
+2024-05-23 13:10:18,729 INFO    SenderThread:3633 [sender.py:transition_state():613] send defer: 10
+2024-05-23 13:10:18,730 DEBUG   HandlerThread:3633 [handler.py:handle_request():158] handle_request: defer
+2024-05-23 13:10:18,730 INFO    HandlerThread:3633 [handler.py:handle_request_defer():184] handle defer: 10
+2024-05-23 13:10:18,731 DEBUG   SenderThread:3633 [sender.py:send_request():405] send_request: defer
+2024-05-23 13:10:18,731 INFO    SenderThread:3633 [sender.py:send_request_defer():609] handle sender defer: 10
+2024-05-23 13:10:18,731 INFO    SenderThread:3633 [file_pusher.py:finish():169] shutting down file pusher
+2024-05-23 13:10:18,957 DEBUG   HandlerThread:3633 [handler.py:handle_request():158] handle_request: poll_exit
+2024-05-23 13:10:18,957 DEBUG   SenderThread:3633 [sender.py:send_request():405] send_request: poll_exit
+2024-05-23 13:10:18,974 INFO    wandb-upload_0:3633 [upload_job.py:push():130] Uploaded file /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_131005-q495m75j/files/output.log
+2024-05-23 13:10:19,385 INFO    wandb-upload_3:3633 [upload_job.py:push():130] Uploaded file /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_131005-q495m75j/files/config.yaml
+2024-05-23 13:10:19,393 INFO    wandb-upload_2:3633 [upload_job.py:push():130] Uploaded file /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_131005-q495m75j/files/requirements.txt
+2024-05-23 13:10:19,411 INFO    wandb-upload_1:3633 [upload_job.py:push():130] Uploaded file /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_131005-q495m75j/files/wandb-summary.json
+2024-05-23 13:10:19,611 INFO    Thread-11 (_thread_body):3633 [sender.py:transition_state():613] send defer: 11
+2024-05-23 13:10:19,611 DEBUG   HandlerThread:3633 [handler.py:handle_request():158] handle_request: defer
+2024-05-23 13:10:19,611 INFO    HandlerThread:3633 [handler.py:handle_request_defer():184] handle defer: 11
+2024-05-23 13:10:19,611 DEBUG   SenderThread:3633 [sender.py:send_request():405] send_request: defer
+2024-05-23 13:10:19,611 INFO    SenderThread:3633 [sender.py:send_request_defer():609] handle sender defer: 11
+2024-05-23 13:10:19,611 INFO    SenderThread:3633 [file_pusher.py:join():175] waiting for file pusher
+2024-05-23 13:10:19,611 INFO    SenderThread:3633 [sender.py:transition_state():613] send defer: 12
+2024-05-23 13:10:19,612 DEBUG   HandlerThread:3633 [handler.py:handle_request():158] handle_request: defer
+2024-05-23 13:10:19,612 INFO    HandlerThread:3633 [handler.py:handle_request_defer():184] handle defer: 12
+2024-05-23 13:10:19,612 DEBUG   SenderThread:3633 [sender.py:send_request():405] send_request: defer
+2024-05-23 13:10:19,612 INFO    SenderThread:3633 [sender.py:send_request_defer():609] handle sender defer: 12
+2024-05-23 13:10:19,612 INFO    SenderThread:3633 [file_stream.py:finish():601] file stream finish called
+2024-05-23 13:10:19,688 INFO    SenderThread:3633 [file_stream.py:finish():605] file stream finish is done
+2024-05-23 13:10:19,688 INFO    SenderThread:3633 [sender.py:transition_state():613] send defer: 13
+2024-05-23 13:10:19,688 DEBUG   HandlerThread:3633 [handler.py:handle_request():158] handle_request: defer
+2024-05-23 13:10:19,688 INFO    HandlerThread:3633 [handler.py:handle_request_defer():184] handle defer: 13
+2024-05-23 13:10:19,689 DEBUG   SenderThread:3633 [sender.py:send_request():405] send_request: defer
+2024-05-23 13:10:19,689 INFO    SenderThread:3633 [sender.py:send_request_defer():609] handle sender defer: 13
+2024-05-23 13:10:19,689 INFO    SenderThread:3633 [sender.py:transition_state():613] send defer: 14
+2024-05-23 13:10:19,689 DEBUG   HandlerThread:3633 [handler.py:handle_request():158] handle_request: defer
+2024-05-23 13:10:19,689 INFO    HandlerThread:3633 [handler.py:handle_request_defer():184] handle defer: 14
+2024-05-23 13:10:19,689 DEBUG   SenderThread:3633 [sender.py:send():378] send: final
+2024-05-23 13:10:19,689 DEBUG   SenderThread:3633 [sender.py:send():378] send: footer
+2024-05-23 13:10:19,689 DEBUG   SenderThread:3633 [sender.py:send_request():405] send_request: defer
+2024-05-23 13:10:19,689 INFO    SenderThread:3633 [sender.py:send_request_defer():609] handle sender defer: 14
+2024-05-23 13:10:19,689 DEBUG   HandlerThread:3633 [handler.py:handle_request():158] handle_request: poll_exit
+2024-05-23 13:10:19,690 DEBUG   SenderThread:3633 [sender.py:send_request():405] send_request: poll_exit
+2024-05-23 13:10:19,690 DEBUG   HandlerThread:3633 [handler.py:handle_request():158] handle_request: poll_exit
+2024-05-23 13:10:19,690 DEBUG   HandlerThread:3633 [handler.py:handle_request():158] handle_request: server_info
+2024-05-23 13:10:19,690 DEBUG   HandlerThread:3633 [handler.py:handle_request():158] handle_request: get_summary
+2024-05-23 13:10:19,690 DEBUG   HandlerThread:3633 [handler.py:handle_request():158] handle_request: sampled_history
+2024-05-23 13:10:19,690 DEBUG   HandlerThread:3633 [handler.py:handle_request():158] handle_request: internal_messages
+2024-05-23 13:10:19,691 DEBUG   SenderThread:3633 [sender.py:send_request():405] send_request: poll_exit
+2024-05-23 13:10:19,691 DEBUG   SenderThread:3633 [sender.py:send_request():405] send_request: server_info
+2024-05-23 13:10:19,752 INFO    MainThread:3633 [wandb_run.py:_footer_history_summary_info():3994] rendering history
+2024-05-23 13:10:19,752 INFO    MainThread:3633 [wandb_run.py:_footer_history_summary_info():4026] rendering summary
+2024-05-23 13:10:19,752 INFO    MainThread:3633 [wandb_run.py:_footer_sync_info():3953] logging synced files
+2024-05-23 13:10:19,753 DEBUG   HandlerThread:3633 [handler.py:handle_request():158] handle_request: shutdown
+2024-05-23 13:10:19,753 INFO    HandlerThread:3633 [handler.py:finish():882] shutting down handler
+2024-05-23 13:10:20,691 INFO    WriterThread:3633 [datastore.py:close():296] close: /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_131005-q495m75j/run-q495m75j.wandb
+2024-05-23 13:10:20,752 INFO    SenderThread:3633 [sender.py:finish():1545] shutting down sender
+2024-05-23 13:10:20,752 INFO    SenderThread:3633 [file_pusher.py:finish():169] shutting down file pusher
+2024-05-23 13:10:20,752 INFO    SenderThread:3633 [file_pusher.py:join():175] waiting for file pusher

lm-evaluation-harness/wandb/run-20240523_131005-q495m75j/logs/debug.log

@@ -0,0 +1,29 @@
+2024-05-23 13:10:05,395 INFO    MainThread:3478 [wandb_setup.py:_flush():76] Current SDK version is 0.17.0
+2024-05-23 13:10:05,396 INFO    MainThread:3478 [wandb_setup.py:_flush():76] Configure stats pid to 3478
+2024-05-23 13:10:05,396 INFO    MainThread:3478 [wandb_setup.py:_flush():76] Loading settings from /root/.config/wandb/settings
+2024-05-23 13:10:05,396 INFO    MainThread:3478 [wandb_setup.py:_flush():76] Loading settings from /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/settings
+2024-05-23 13:10:05,396 INFO    MainThread:3478 [wandb_setup.py:_flush():76] Loading settings from environment variables: {}
+2024-05-23 13:10:05,396 INFO    MainThread:3478 [wandb_setup.py:_flush():76] Applying setup settings: {'_disable_service': False}
+2024-05-23 13:10:05,396 WARNING MainThread:3478 [wandb_setup.py:_flush():76] Could not find program at -m lm_eval.__main__
+2024-05-23 13:10:05,396 INFO    MainThread:3478 [wandb_setup.py:_flush():76] Inferring run settings from compute environment: {'program_relpath': None, 'program': '-m lm_eval.__main__'}
+2024-05-23 13:10:05,396 INFO    MainThread:3478 [wandb_setup.py:_flush():76] Applying login settings: {}
+2024-05-23 13:10:05,396 INFO    MainThread:3478 [wandb_init.py:_log_setup():520] Logging user logs to /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_131005-q495m75j/logs/debug.log
+2024-05-23 13:10:05,396 INFO    MainThread:3478 [wandb_init.py:_log_setup():521] Logging internal logs to /mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/wandb/run-20240523_131005-q495m75j/logs/debug-internal.log
+2024-05-23 13:10:05,396 INFO    MainThread:3478 [wandb_init.py:init():560] calling init triggers
+2024-05-23 13:10:05,396 INFO    MainThread:3478 [wandb_init.py:init():567] wandb.init called with sweep_config: {}
+config: {}
+2024-05-23 13:10:05,396 INFO    MainThread:3478 [wandb_init.py:init():610] starting backend
+2024-05-23 13:10:05,396 INFO    MainThread:3478 [wandb_init.py:init():614] setting up manager
+2024-05-23 13:10:05,399 INFO    MainThread:3478 [backend.py:_multiprocessing_setup():105] multiprocessing start_methods=fork,spawn,forkserver, using: spawn
+2024-05-23 13:10:05,400 INFO    MainThread:3478 [wandb_init.py:init():622] backend started and connected
+2024-05-23 13:10:05,404 INFO    MainThread:3478 [wandb_init.py:init():711] updated telemetry
+2024-05-23 13:10:05,412 INFO    MainThread:3478 [wandb_init.py:init():744] communicating run to backend with 90.0 second timeout
+2024-05-23 13:10:05,719 INFO    MainThread:3478 [wandb_run.py:_on_init():2396] communicating current version
+2024-05-23 13:10:05,840 INFO    MainThread:3478 [wandb_run.py:_on_init():2405] got version response 
+2024-05-23 13:10:05,840 INFO    MainThread:3478 [wandb_init.py:init():795] starting run threads in backend
+2024-05-23 13:10:06,116 INFO    MainThread:3478 [wandb_run.py:_console_start():2374] atexit reg
+2024-05-23 13:10:06,116 INFO    MainThread:3478 [wandb_run.py:_redirect():2229] redirect: wrap_raw
+2024-05-23 13:10:06,116 INFO    MainThread:3478 [wandb_run.py:_redirect():2294] Wrapping output streams.
+2024-05-23 13:10:06,116 INFO    MainThread:3478 [wandb_run.py:_redirect():2319] Redirects installed.
+2024-05-23 13:10:06,119 INFO    MainThread:3478 [wandb_init.py:init():838] run started, returning control to user process
+2024-05-23 13:10:20,754 WARNING MsgRouterThr:3478 [router.py:message_loop():77] message_loop has been closed

lm-evaluation-harness/wandb/run-20240530_070447-fi4sos5j/files/requirements.txt

@@ -0,0 +1,154 @@
+DataProperty==1.0.1
+GitPython==3.1.43
+Jinja2==3.1.4
+Markdown==3.6
+MarkupSafe==2.1.5
+Pillow-SIMD==7.0.0.post3
+PyYAML==6.0
+Werkzeug==3.0.3
+absl-py==2.1.0
+accelerate==0.30.1
+aiohttp==3.9.5
+aiosignal==1.3.1
+async-timeout==4.0.3
+attrs==23.2.0
+av==9.2.0
+cachetools==5.3.3
+certifi==2024.2.2
+cffi==1.15.1
+cfgv==3.4.0
+chardet==5.2.0
+charset-normalizer==3.3.2
+click==8.1.7
+cmake==3.29.2
+colorama==0.4.6
+datasets==2.19.1
+deepspeed==0.12.4+hpu.synapse.v1.15.1
+dill==0.3.8
+distlib==0.3.8
+docker-pycreds==0.4.0
+einops==0.8.0
+evaluate==0.4.2
+exceptiongroup==1.2.1
+expecttest==0.2.1
+filelock==3.14.0
+frozenlist==1.4.1
+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==0.23.2
+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
+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==1.13.1
+sentencepiece==0.2.0
+sentry-sdk==2.3.1
+setproctitle==1.3.3
+setuptools==59.6.0
+setuptools==69.5.1
+six==1.16.0
+smmap==5.0.1
+sqlitedict==2.1.0
+symengine==0.11.0
+sympy==1.12
+tabledata==1.3.3
+tabulate==0.9.0
+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==0.15.2
+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==4.36.2
+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

lm-evaluation-harness/wandb/run-20240530_070447-fi4sos5j/files/wandb-summary.json

@@ -0,0 +1 @@
+{"_wandb": {"runtime": 39}}

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

@@ -0,0 +1,29 @@
+# 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 _LazyModule
+
+
+_import_structure = {"tokenization_bertweet": ["BertweetTokenizer"]}
+
+
+if TYPE_CHECKING:
+    from .tokenization_bertweet import BertweetTokenizer
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

venv/lib/python3.10/site-packages/transformers/models/bertweet/tokenization_bertweet.py

@@ -0,0 +1,767 @@
+# coding=utf-8
+# Copyright (c) 2020, VinAI Research and the HuggingFace Inc. team.
+# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team.
+#
+# 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.
+""" Tokenization classes for BERTweet"""
+
+
+import html
+import os
+import re
+from shutil import copyfile
+from typing import List, Optional, Tuple
+
+import regex
+
+from ...tokenization_utils import PreTrainedTokenizer
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {
+    "vocab_file": "vocab.txt",
+    "merges_file": "bpe.codes",
+}
+
+
+def get_pairs(word):
+    """
+    Return set of symbol pairs in a word.
+
+    Word is represented as tuple of symbols (symbols being variable-length strings).
+    """
+    pairs = set()
+    prev_char = word[0]
+    for char in word[1:]:
+        pairs.add((prev_char, char))
+        prev_char = char
+
+    pairs = set(pairs)
+    return pairs
+
+
+class BertweetTokenizer(PreTrainedTokenizer):
+    """
+    Constructs a BERTweet tokenizer, using Byte-Pair-Encoding.
+
+    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
+    this superclass for more information regarding those methods.
+
+    Args:
+        vocab_file (`str`):
+            Path to the vocabulary file.
+        merges_file (`str`):
+            Path to the merges file.
+        normalization (`bool`, *optional*, defaults to `False`):
+            Whether or not to apply a normalization preprocess.
+        bos_token (`str`, *optional*, defaults to `"<s>"`):
+            The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
+
+            <Tip>
+
+            When building a sequence using special tokens, this is not the token that is used for the beginning of
+            sequence. The token used is the `cls_token`.
+
+            </Tip>
+
+        eos_token (`str`, *optional*, defaults to `"</s>"`):
+            The end of sequence token.
+
+            <Tip>
+
+            When building a sequence using special tokens, this is not the token that is used for the end of sequence.
+            The token used is the `sep_token`.
+
+            </Tip>
+
+        sep_token (`str`, *optional*, defaults to `"</s>"`):
+            The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+            sequence classification or for a text and a question for question answering. It is also used as the last
+            token of a sequence built with special tokens.
+        cls_token (`str`, *optional*, defaults to `"<s>"`):
+            The classifier token which is used when doing sequence classification (classification of the whole sequence
+            instead of per-token classification). It is the first token of the sequence when built with special tokens.
+        unk_token (`str`, *optional*, defaults to `"<unk>"`):
+            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+            token instead.
+        pad_token (`str`, *optional*, defaults to `"<pad>"`):
+            The token used for padding, for example when batching sequences of different lengths.
+        mask_token (`str`, *optional*, defaults to `"<mask>"`):
+            The token used for masking values. This is the token used when training this model with masked language
+            modeling. This is the token which the model will try to predict.
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+
+    def __init__(
+        self,
+        vocab_file,
+        merges_file,
+        normalization=False,
+        bos_token="<s>",
+        eos_token="</s>",
+        sep_token="</s>",
+        cls_token="<s>",
+        unk_token="<unk>",
+        pad_token="<pad>",
+        mask_token="<mask>",
+        **kwargs,
+    ):
+        try:
+            from emoji import demojize
+
+            self.demojizer = demojize
+        except ImportError:
+            logger.warning(
+                "emoji is not installed, thus not converting emoticons or emojis into text. Install emoji: pip3"
+                " install emoji==0.6.0"
+            )
+            self.demojizer = None
+
+        self.vocab_file = vocab_file
+        self.merges_file = merges_file
+
+        self.encoder = {}
+        self.encoder[str(bos_token)] = 0
+        self.encoder[str(pad_token)] = 1
+        self.encoder[str(eos_token)] = 2
+        self.encoder[str(unk_token)] = 3
+
+        self.add_from_file(vocab_file)
+
+        self.decoder = {v: k for k, v in self.encoder.items()}
+
+        with open(merges_file, encoding="utf-8") as merges_handle:
+            merges = merges_handle.read().split("\n")[:-1]
+        merges = [tuple(merge.split()[:-1]) for merge in merges]
+        self.bpe_ranks = dict(zip(merges, range(len(merges))))
+        self.cache = {}
+
+        self.normalization = normalization
+        self.tweetPreprocessor = TweetTokenizer()
+        self.special_puncts = {"’": "'", "…": "..."}
+
+        super().__init__(
+            normalization=normalization,
+            bos_token=bos_token,
+            eos_token=eos_token,
+            sep_token=sep_token,
+            cls_token=cls_token,
+            unk_token=unk_token,
+            pad_token=pad_token,
+            mask_token=mask_token,
+            **kwargs,
+        )
+
+    def build_inputs_with_special_tokens(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+        adding special tokens. A BERTweet sequence has the following format:
+
+        - single sequence: `<s> X </s>`
+        - pair of sequences: `<s> A </s></s> B </s>`
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs to which the special tokens will be added.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+        """
+
+        if token_ids_1 is None:
+            return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
+        cls = [self.cls_token_id]
+        sep = [self.sep_token_id]
+        return cls + token_ids_0 + sep + sep + token_ids_1 + sep
+
+    def get_special_tokens_mask(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
+    ) -> List[int]:
+        """
+        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer `prepare_for_model` method.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+
+        Returns:
+            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+            )
+
+        if token_ids_1 is None:
+            return [1] + ([0] * len(token_ids_0)) + [1]
+        return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
+
+    def create_token_type_ids_from_sequences(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Create a mask from the two sequences passed to be used in a sequence-pair classification task. BERTweet does
+        not make use of token type ids, therefore a list of zeros is returned.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `List[int]`: List of zeros.
+        """
+
+        sep = [self.sep_token_id]
+        cls = [self.cls_token_id]
+
+        if token_ids_1 is None:
+            return len(cls + token_ids_0 + sep) * [0]
+        return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
+
+    @property
+    def vocab_size(self):
+        return len(self.encoder)
+
+    def get_vocab(self):
+        return dict(self.encoder, **self.added_tokens_encoder)
+
+    def bpe(self, token):
+        if token in self.cache:
+            return self.cache[token]
+        word = tuple(token)
+        word = tuple(list(word[:-1]) + [word[-1] + "</w>"])
+        pairs = get_pairs(word)
+
+        if not pairs:
+            return token
+
+        while True:
+            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
+            if bigram not in self.bpe_ranks:
+                break
+            first, second = bigram
+            new_word = []
+            i = 0
+            while i < len(word):
+                try:
+                    j = word.index(first, i)
+                except ValueError:
+                    new_word.extend(word[i:])
+                    break
+                else:
+                    new_word.extend(word[i:j])
+                    i = j
+
+                if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
+                    new_word.append(first + second)
+                    i += 2
+                else:
+                    new_word.append(word[i])
+                    i += 1
+            new_word = tuple(new_word)
+            word = new_word
+            if len(word) == 1:
+                break
+            else:
+                pairs = get_pairs(word)
+        word = "@@ ".join(word)
+        word = word[:-4]
+        self.cache[token] = word
+        return word
+
+    def _tokenize(self, text):
+        """Tokenize a string."""
+        if self.normalization:  # Perform Tweet normalization before performing BPE
+            text = self.normalizeTweet(text)
+
+        split_tokens = []
+        words = re.findall(r"\S+\n?", text)
+        for token in words:
+            split_tokens.extend(list(self.bpe(token).split(" ")))
+        return split_tokens
+
+    def normalizeTweet(self, tweet):
+        """
+        Normalize a raw Tweet
+        """
+        for punct in self.special_puncts:
+            tweet = tweet.replace(punct, self.special_puncts[punct])
+
+        tokens = self.tweetPreprocessor.tokenize(tweet)
+        normTweet = " ".join([self.normalizeToken(token) for token in tokens])
+
+        normTweet = (
+            normTweet.replace("cannot ", "can not ")
+            .replace("n't ", " n't ")
+            .replace("n 't ", " n't ")
+            .replace("ca n't", "can't")
+            .replace("ai n't", "ain't")
+        )
+        normTweet = (
+            normTweet.replace("'m ", " 'm ")
+            .replace("'re ", " 're ")
+            .replace("'s ", " 's ")
+            .replace("'ll ", " 'll ")
+            .replace("'d ", " 'd ")
+            .replace("'ve ", " 've ")
+        )
+        normTweet = (
+            normTweet.replace(" p . m .", "  p.m.")
+            .replace(" p . m ", " p.m ")
+            .replace(" a . m .", " a.m.")
+            .replace(" a . m ", " a.m ")
+        )
+
+        return " ".join(normTweet.split())
+
+    def normalizeToken(self, token):
+        """
+        Normalize tokens in a Tweet
+        """
+        lowercased_token = token.lower()
+        if token.startswith("@"):
+            return "@USER"
+        elif lowercased_token.startswith("http") or lowercased_token.startswith("www"):
+            return "HTTPURL"
+        elif len(token) == 1:
+            if token in self.special_puncts:
+                return self.special_puncts[token]
+            if self.demojizer is not None:
+                return self.demojizer(token)
+            else:
+                return token
+        else:
+            return token
+
+    def _convert_token_to_id(self, token):
+        """Converts a token (str) in an id using the vocab."""
+        return self.encoder.get(token, self.encoder.get(self.unk_token))
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        return self.decoder.get(index, self.unk_token)
+
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (string) in a single string."""
+        out_string = " ".join(tokens).replace("@@ ", "").strip()
+        return out_string
+
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        if not os.path.isdir(save_directory):
+            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+            return
+        out_vocab_file = os.path.join(
+            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+        )
+        out_merge_file = os.path.join(
+            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
+        )
+
+        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
+            copyfile(self.vocab_file, out_vocab_file)
+        elif not os.path.isfile(self.vocab_file):
+            with open(out_vocab_file, "wb") as fi:
+                content_spiece_model = self.sp_model.serialized_model_proto()
+                fi.write(content_spiece_model)
+
+        if os.path.abspath(self.merges_file) != os.path.abspath(out_merge_file):
+            copyfile(self.merges_file, out_merge_file)
+
+        return out_vocab_file, out_merge_file
+
+    # def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
+    #     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
+    #     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
+    #     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
+    #     return ''.join(tokens_generated_so_far)
+
+    def add_from_file(self, f):
+        """
+        Loads a pre-existing dictionary from a text file and adds its symbols to this instance.
+        """
+        if isinstance(f, str):
+            try:
+                with open(f, "r", encoding="utf-8") as fd:
+                    self.add_from_file(fd)
+            except FileNotFoundError as fnfe:
+                raise fnfe
+            except UnicodeError:
+                raise Exception(f"Incorrect encoding detected in {f}, please rebuild the dataset")
+            return
+
+        lines = f.readlines()
+        for lineTmp in lines:
+            line = lineTmp.strip()
+            idx = line.rfind(" ")
+            if idx == -1:
+                raise ValueError("Incorrect dictionary format, expected '<token> <cnt>'")
+            word = line[:idx]
+            self.encoder[word] = len(self.encoder)
+
+
+# Natural Language Toolkit: Twitter Tokenizer
+#
+# Copyright (C) 2001-2020 NLTK Project
+# Author: Christopher Potts <[email protected]>
+#         Ewan Klein <[email protected]> (modifications)
+#         Pierpaolo Pantone <> (modifications)
+# URL: http://nltk.org/
+# For license information, see LICENSE.TXT
+#
+
+
+"""
+Twitter-aware tokenizer, designed to be flexible and easy to adapt to new domains and tasks. The basic logic is this:
+
+1. The tuple regex_strings defines a list of regular expression strings.
+
+2. The regex_strings strings are put, in order, into a compiled regular expression object called word_re.
+
+3. The tokenization is done by word_re.findall(s), where s is the user-supplied string, inside the tokenize() method of
+   the class Tokenizer.
+
+4. When instantiating Tokenizer objects, there is a single option: preserve_case. By default, it is set to True. If it
+   is set to False, then the tokenizer will lowercase everything except for emoticons.
+
+"""
+
+
+######################################################################
+#
+# import regex  # https://github.com/nltk/nltk/issues/2409
+# import html
+#
+######################################################################
+# The following strings are components in the regular expression
+# that is used for tokenizing. It's important that phone_number
+# appears first in the final regex (since it can contain whitespace).
+# It also could matter that tags comes after emoticons, due to the
+# possibility of having text like
+#
+#     <:| and some text >:)
+#
+# Most importantly, the final element should always be last, since it
+# does a last ditch whitespace-based tokenization of whatever is left.
+
+# ToDo: Update with http://en.wikipedia.org/wiki/List_of_emoticons ?
+
+# This particular element is used in a couple ways, so we define it
+# with a name:
+# docstyle-ignore
+EMOTICONS = r"""
+    (?:
+      [<>]?
+      [:;=8]                     # eyes
+      [\-o\*\']?                 # optional nose
+      [\)\]\(\[dDpP/\:\}\{@\|\\] # mouth
+      |
+      [\)\]\(\[dDpP/\:\}\{@\|\\] # mouth
+      [\-o\*\']?                 # optional nose
+      [:;=8]                     # eyes
+      [<>]?
+      |
+      <3                         # heart
+    )"""
+
+# URL pattern due to John Gruber, modified by Tom Winzig. See
+# https://gist.github.com/winzig/8894715
+# docstyle-ignore
+URLS = r"""			# Capture 1: entire matched URL
+  (?:
+  https?:				# URL protocol and colon
+    (?:
+      /{1,3}				# 1-3 slashes
+      |					#   or
+      [a-z0-9%]				# Single letter or digit or '%'
+                                       # (Trying not to match e.g. "URI::Escape")
+    )
+    |					#   or
+                                       # looks like domain name followed by a slash:
+    [a-z0-9.\-]+[.]
+    (?:[a-z]{2,13})
+    /
+  )
+  (?:					# One or more:
+    [^\s()<>{}\[\]]+			# Run of non-space, non-()<>{}[]
+    |					#   or
+    \([^\s()]*?\([^\s()]+\)[^\s()]*?\) # balanced parens, one level deep: (...(...)...)
+    |
+    \([^\s]+?\)				# balanced parens, non-recursive: (...)
+  )+
+  (?:					# End with:
+    \([^\s()]*?\([^\s()]+\)[^\s()]*?\) # balanced parens, one level deep: (...(...)...)
+    |
+    \([^\s]+?\)				# balanced parens, non-recursive: (...)
+    |					#   or
+    [^\s`!()\[\]{};:'".,<>?«»“”‘’]	# not a space or one of these punct chars
+  )
+  |					# OR, the following to match naked domains:
+  (?:
+    (?<!@)			        # not preceded by a @, avoid matching foo@_gmail.com_
+    [a-z0-9]+
+    (?:[.\-][a-z0-9]+)*
+    [.]
+    (?:[a-z]{2,13})
+    \b
+    /?
+    (?!@)			        # not succeeded by a @,
+                            # avoid matching "foo.na" in "[email protected]"
+  )
+"""
+
+# docstyle-ignore
+# The components of the tokenizer:
+REGEXPS = (
+    URLS,
+    # Phone numbers:
+    r"""
+    (?:
+      (?:            # (international)
+        \+?[01]
+        [ *\-.\)]*
+      )?
+      (?:            # (area code)
+        [\(]?
+        \d{3}
+        [ *\-.\)]*
+      )?
+      \d{3}          # exchange
+      [ *\-.\)]*
+      \d{4}          # base
+    )""",
+    # ASCII Emoticons
+    EMOTICONS,
+    # HTML tags:
+    r"""<[^>\s]+>""",
+    # ASCII Arrows
+    r"""[\-]+>|<[\-]+""",
+    # Twitter username:
+    r"""(?:@[\w_]+)""",
+    # Twitter hashtags:
+    r"""(?:\#+[\w_]+[\w\'_\-]*[\w_]+)""",
+    # email addresses
+    r"""[\w.+-]+@[\w-]+\.(?:[\w-]\.?)+[\w-]""",
+    # docstyle-ignore
+    # Remaining word types:
+    r"""
+    (?:[^\W\d_](?:[^\W\d_]|['\-_])+[^\W\d_]) # Words with apostrophes or dashes.
+    |
+    (?:[+\-]?\d+[,/.:-]\d+[+\-]?)  # Numbers, including fractions, decimals.
+    |
+    (?:[\w_]+)                     # Words without apostrophes or dashes.
+    |
+    (?:\.(?:\s*\.){1,})            # Ellipsis dots.
+    |
+    (?:\S)                         # Everything else that isn't whitespace.
+    """,
+)
+
+######################################################################
+# This is the core tokenizing regex:
+
+WORD_RE = regex.compile(r"""(%s)""" % "|".join(REGEXPS), regex.VERBOSE | regex.I | regex.UNICODE)
+
+# WORD_RE performs poorly on these patterns:
+HANG_RE = regex.compile(r"([^a-zA-Z0-9])\1{3,}")
+
+# The emoticon string gets its own regex so that we can preserve case for
+# them as needed:
+EMOTICON_RE = regex.compile(EMOTICONS, regex.VERBOSE | regex.I | regex.UNICODE)
+
+# These are for regularizing HTML entities to Unicode:
+ENT_RE = regex.compile(r"&(#?(x?))([^&;\s]+);")
+
+
+######################################################################
+# Functions for converting html entities
+######################################################################
+
+
+def _str_to_unicode(text, encoding=None, errors="strict"):
+    if encoding is None:
+        encoding = "utf-8"
+    if isinstance(text, bytes):
+        return text.decode(encoding, errors)
+    return text
+
+
+def _replace_html_entities(text, keep=(), remove_illegal=True, encoding="utf-8"):
+    """
+    Remove entities from text by converting them to their corresponding unicode character.
+
+    Args:
+        text:
+            A unicode string or a byte string encoded in the given *encoding* (which defaults to 'utf-8').
+        keep (list):
+            List of entity names which should not be replaced. This supports both numeric entities (`&#nnnn;` and
+            `&#hhhh;`) and named entities (such as `&nbsp;` or `&gt;`).
+        remove_illegal (bool):
+            If `True`, entities that can't be converted are removed. Otherwise, entities that can't be converted are
+            kept "as is".
+
+    Returns: A unicode string with the entities removed.
+
+    See https://github.com/scrapy/w3lib/blob/master/w3lib/html.py
+
+    Examples:
+
+    ```python
+    >>> from nltk.tokenize.casual import _replace_html_entities
+
+    >>> _replace_html_entities(b"Price: &pound;100")
+    'Price: \\xa3100'
+
+    >>> print(_replace_html_entities(b"Price: &pound;100"))
+    Price: £100
+    ```"""
+
+    def _convert_entity(match):
+        entity_body = match.group(3)
+        if match.group(1):
+            try:
+                if match.group(2):
+                    number = int(entity_body, 16)
+                else:
+                    number = int(entity_body, 10)
+                # Numeric character references in the 80-9F range are typically
+                # interpreted by browsers as representing the characters mapped
+                # to bytes 80-9F in the Windows-1252 encoding. For more info
+                # see: https://en.wikipedia.org/wiki/ISO/IEC_8859-1#Similar_character_sets
+                if 0x80 <= number <= 0x9F:
+                    return bytes((number,)).decode("cp1252")
+            except ValueError:
+                number = None
+        else:
+            if entity_body in keep:
+                return match.group(0)
+            else:
+                number = html.entities.name2codepoint.get(entity_body)
+        if number is not None:
+            try:
+                return chr(number)
+            except (ValueError, OverflowError):
+                pass
+
+        return "" if remove_illegal else match.group(0)
+
+    return ENT_RE.sub(_convert_entity, _str_to_unicode(text, encoding))
+
+
+######################################################################
+
+
+class TweetTokenizer:
+    r"""
+    Examples:
+
+    ```python
+    >>> # Tokenizer for tweets.
+    >>> from nltk.tokenize import TweetTokenizer
+
+    >>> tknzr = TweetTokenizer()
+    >>> s0 = "This is a cooool #dummysmiley: :-) :-P <3 and some arrows < > -> <--"
+    >>> tknzr.tokenize(s0)
+    ['This', 'is', 'a', 'cooool', '#dummysmiley', ':', ':-)', ':-P', '<3', 'and', 'some', 'arrows', '<', '>', '->', '<--']
+
+    >>> # Examples using *strip_handles* and *reduce_len parameters*:
+    >>> tknzr = TweetTokenizer(strip_handles=True, reduce_len=True)
+    >>> s1 = "@remy: This is waaaaayyyy too much for you!!!!!!"
+    >>> tknzr.tokenize(s1)
+    [':', 'This', 'is', 'waaayyy', 'too', 'much', 'for', 'you', '!', '!', '!']
+    ```"""
+
+    def __init__(self, preserve_case=True, reduce_len=False, strip_handles=False):
+        self.preserve_case = preserve_case
+        self.reduce_len = reduce_len
+        self.strip_handles = strip_handles
+
+    def tokenize(self, text):
+        """
+        Args:
+            text: str
+
+        Returns: list(str) A tokenized list of strings; concatenating this list returns the original string if
+        `preserve_case=False`
+        """
+        # Fix HTML character entities:
+        text = _replace_html_entities(text)
+        # Remove username handles
+        if self.strip_handles:
+            text = remove_handles(text)
+        # Normalize word lengthening
+        if self.reduce_len:
+            text = reduce_lengthening(text)
+        # Shorten problematic sequences of characters
+        safe_text = HANG_RE.sub(r"\1\1\1", text)
+        # Tokenize:
+        words = WORD_RE.findall(safe_text)
+        # Possibly alter the case, but avoid changing emoticons like :D into :d:
+        if not self.preserve_case:
+            words = [x if EMOTICON_RE.search(x) else x.lower() for x in words]
+        return words
+
+
+######################################################################
+# Normalization Functions
+######################################################################
+
+
+def reduce_lengthening(text):
+    """
+    Replace repeated character sequences of length 3 or greater with sequences of length 3.
+    """
+    pattern = regex.compile(r"(.)\1{2,}")
+    return pattern.sub(r"\1\1\1", text)
+
+
+def remove_handles(text):
+    """
+    Remove Twitter username handles from text.
+    """
+    pattern = regex.compile(
+        r"(?<![A-Za-z0-9_!@#\$%&*])@(([A-Za-z0-9_]){20}(?!@))|(?<![A-Za-z0-9_!@#\$%&*])@(([A-Za-z0-9_]){1,19})(?![A-Za-z0-9_]*@)"
+    )
+    # Substitute handles with ' ' to ensure that text on either side of removed handles are tokenized correctly
+    return pattern.sub(" ", text)
+
+
+######################################################################
+# Tokenization Function
+######################################################################
+
+
+def casual_tokenize(text, preserve_case=True, reduce_len=False, strip_handles=False):
+    """
+    Convenience function for wrapping the tokenizer.
+    """
+    return TweetTokenizer(preserve_case=preserve_case, reduce_len=reduce_len, strip_handles=strip_handles).tokenize(
+        text
+    )
+
+
+###############################################################################

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

@@ -0,0 +1,82 @@
+# Copyright 2023 The HuggingFace and Baidu 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
+
+# rely on isort to merge the imports
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available
+
+
+_import_structure = {
+    "configuration_ernie_m": ["ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieMConfig"],
+}
+
+try:
+    if not is_sentencepiece_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["tokenization_ernie_m"] = ["ErnieMTokenizer"]
+
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_ernie_m"] = [
+        "ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "ErnieMForMultipleChoice",
+        "ErnieMForQuestionAnswering",
+        "ErnieMForSequenceClassification",
+        "ErnieMForTokenClassification",
+        "ErnieMModel",
+        "ErnieMPreTrainedModel",
+        "ErnieMForInformationExtraction",
+    ]
+
+
+if TYPE_CHECKING:
+    from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig
+
+    try:
+        if not is_sentencepiece_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .tokenization_ernie_m import ErnieMTokenizer
+
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_ernie_m import (
+            ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
+            ErnieMForInformationExtraction,
+            ErnieMForMultipleChoice,
+            ErnieMForQuestionAnswering,
+            ErnieMForSequenceClassification,
+            ErnieMForTokenClassification,
+            ErnieMModel,
+            ErnieMPreTrainedModel,
+        )
+
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

venv/lib/python3.10/site-packages/transformers/models/ernie_m/configuration_ernie_m.py

@@ -0,0 +1,112 @@
+# coding=utf-8
+# Copyright 2023 Xuan Ouyang, Shuohuan Wang, Chao Pang, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang 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.
+""" ErnieM model configuration"""
+# Adapted from original paddlenlp repository.(https://github.com/PaddlePaddle/PaddleNLP/blob/develop/paddlenlp/transformers/ernie_m/configuration.py)
+
+from __future__ import annotations
+
+from typing import Dict
+
+from ...configuration_utils import PretrainedConfig
+from ..deprecated._archive_maps import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP  # noqa: F401, E402
+
+
+class ErnieMConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`ErnieMModel`]. It is used to instantiate a
+    Ernie-M 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 `Ernie-M`
+    [susnato/ernie-m-base_pytorch](https://huggingface.co/susnato/ernie-m-base_pytorch) 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 250002):
+            Vocabulary size of `inputs_ids` in [`ErnieMModel`]. Also is the vocab size of token embedding matrix.
+            Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling
+            [`ErnieMModel`].
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the embedding layer, encoder layers and pooler layer.
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (`int`, *optional*, defaults to 3072):
+            Dimensionality of the feed-forward (ff) layer in the encoder. Input tensors to feed-forward layers are
+            firstly projected from hidden_size to intermediate_size, and then projected back to hidden_size. Typically
+            intermediate_size is larger than hidden_size.
+        hidden_act (`str`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function in the feed-forward layer. `"gelu"`, `"relu"` and any other torch
+            supported activation functions are supported.
+        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+            The dropout probability for all fully connected layers in the embeddings and encoder.
+        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
+            The dropout probability used in `MultiHeadAttention` in all encoder layers to drop some attention target.
+        max_position_embeddings (`int`, *optional*, defaults to 514):
+            The maximum value of the dimensionality of position encoding, which dictates the maximum supported length
+            of an input sequence.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the normal initializer for initializing all weight matrices. The index of padding
+            token in the token vocabulary.
+        pad_token_id (`int`, *optional*, defaults to 1):
+            Padding token id.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon used by the layer normalization layers.
+        classifier_dropout (`float`, *optional*):
+            The dropout ratio for the classification head.
+        act_dropout (`float`, *optional*, defaults to 0.0):
+            This dropout probability is used in `ErnieMEncoderLayer` after activation.
+
+    A normal_initializer initializes weight matrices as normal distributions. See
+    `ErnieMPretrainedModel._init_weights()` for how weights are initialized in `ErnieMModel`.
+    """
+
+    model_type = "ernie_m"
+    attribute_map: Dict[str, str] = {"dropout": "classifier_dropout", "num_classes": "num_labels"}
+
+    def __init__(
+        self,
+        vocab_size: int = 250002,
+        hidden_size: int = 768,
+        num_hidden_layers: int = 12,
+        num_attention_heads: int = 12,
+        intermediate_size: int = 3072,
+        hidden_act: str = "gelu",
+        hidden_dropout_prob: float = 0.1,
+        attention_probs_dropout_prob: float = 0.1,
+        max_position_embeddings: int = 514,
+        initializer_range: float = 0.02,
+        pad_token_id: int = 1,
+        layer_norm_eps: float = 1e-05,
+        classifier_dropout=None,
+        act_dropout=0.0,
+        **kwargs,
+    ):
+        super().__init__(pad_token_id=pad_token_id, **kwargs)
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.classifier_dropout = classifier_dropout
+        self.act_dropout = act_dropout

venv/lib/python3.10/site-packages/transformers/models/ernie_m/modeling_ernie_m.py

@@ -0,0 +1,1058 @@
+# coding=utf-8
+# Copyright 2023 Xuan Ouyang, Shuohuan Wang, Chao Pang, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang 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.
+""" PyTorch ErnieM model."""
+
+
+import math
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn, tensor
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from ...activations import ACT2FN
+from ...modeling_outputs import (
+    BaseModelOutputWithPastAndCrossAttentions,
+    BaseModelOutputWithPoolingAndCrossAttentions,
+    MultipleChoiceModelOutput,
+    QuestionAnsweringModelOutput,
+    SequenceClassifierOutput,
+    TokenClassifierOutput,
+)
+from ...modeling_utils import PreTrainedModel
+from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
+from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
+from .configuration_ernie_m import ErnieMConfig
+
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "susnato/ernie-m-base_pytorch"
+_CONFIG_FOR_DOC = "ErnieMConfig"
+_TOKENIZER_FOR_DOC = "ErnieMTokenizer"
+
+
+from ..deprecated._archive_maps import ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST  # noqa: F401, E402
+
+
+# Adapted from paddlenlp.transformers.ernie_m.modeling.ErnieEmbeddings
+class ErnieMEmbeddings(nn.Module):
+    """Construct the embeddings from word and position embeddings."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
+        self.position_embeddings = nn.Embedding(
+            config.max_position_embeddings, config.hidden_size, padding_idx=config.pad_token_id
+        )
+        self.layer_norm = nn.LayerNorm(normalized_shape=config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(p=config.hidden_dropout_prob)
+        self.padding_idx = config.pad_token_id
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.LongTensor] = None,
+        past_key_values_length: int = 0,
+    ) -> torch.Tensor:
+        if inputs_embeds is None:
+            inputs_embeds = self.word_embeddings(input_ids)
+        if position_ids is None:
+            input_shape = inputs_embeds.size()[:-1]
+            ones = torch.ones(input_shape, dtype=torch.int64, device=inputs_embeds.device)
+            seq_length = torch.cumsum(ones, dim=1)
+            position_ids = seq_length - ones
+
+            if past_key_values_length > 0:
+                position_ids = position_ids + past_key_values_length
+        # to mimic paddlenlp implementation
+        position_ids += 2
+        position_embeddings = self.position_embeddings(position_ids)
+        embeddings = inputs_embeds + position_embeddings
+        embeddings = self.layer_norm(embeddings)
+        embeddings = self.dropout(embeddings)
+
+        return embeddings
+
+
+# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->ErnieM,self.value->self.v_proj,self.key->self.k_proj,self.query->self.q_proj
+class ErnieMSelfAttention(nn.Module):
+    def __init__(self, config, position_embedding_type=None):
+        super().__init__()
+        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
+            raise ValueError(
+                f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+                f"heads ({config.num_attention_heads})"
+            )
+
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        self.q_proj = nn.Linear(config.hidden_size, self.all_head_size)
+        self.k_proj = nn.Linear(config.hidden_size, self.all_head_size)
+        self.v_proj = nn.Linear(config.hidden_size, self.all_head_size)
+
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+        self.position_embedding_type = position_embedding_type or getattr(
+            config, "position_embedding_type", "absolute"
+        )
+        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
+            self.max_position_embeddings = config.max_position_embeddings
+            self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
+
+        self.is_decoder = config.is_decoder
+
+    def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
+        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
+        x = x.view(new_x_shape)
+        return x.permute(0, 2, 1, 3)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        output_attentions: Optional[bool] = False,
+    ) -> Tuple[torch.Tensor]:
+        mixed_query_layer = self.q_proj(hidden_states)
+
+        # If this is instantiated as a cross-attention module, the keys
+        # and values come from an encoder; the attention mask needs to be
+        # such that the encoder's padding tokens are not attended to.
+        is_cross_attention = encoder_hidden_states is not None
+
+        if is_cross_attention and past_key_value is not None:
+            # reuse k,v, cross_attentions
+            key_layer = past_key_value[0]
+            value_layer = past_key_value[1]
+            attention_mask = encoder_attention_mask
+        elif is_cross_attention:
+            key_layer = self.transpose_for_scores(self.k_proj(encoder_hidden_states))
+            value_layer = self.transpose_for_scores(self.v_proj(encoder_hidden_states))
+            attention_mask = encoder_attention_mask
+        elif past_key_value is not None:
+            key_layer = self.transpose_for_scores(self.k_proj(hidden_states))
+            value_layer = self.transpose_for_scores(self.v_proj(hidden_states))
+            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
+            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
+        else:
+            key_layer = self.transpose_for_scores(self.k_proj(hidden_states))
+            value_layer = self.transpose_for_scores(self.v_proj(hidden_states))
+
+        query_layer = self.transpose_for_scores(mixed_query_layer)
+
+        use_cache = past_key_value is not None
+        if self.is_decoder:
+            # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
+            # Further calls to cross_attention layer can then reuse all cross-attention
+            # key/value_states (first "if" case)
+            # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
+            # all previous decoder key/value_states. Further calls to uni-directional self-attention
+            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
+            # if encoder bi-directional self-attention `past_key_value` is always `None`
+            past_key_value = (key_layer, value_layer)
+
+        # Take the dot product between "query" and "key" to get the raw attention scores.
+        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+
+        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
+            query_length, key_length = query_layer.shape[2], key_layer.shape[2]
+            if use_cache:
+                position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view(
+                    -1, 1
+                )
+            else:
+                position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
+            position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
+            distance = position_ids_l - position_ids_r
+
+            positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
+            positional_embedding = positional_embedding.to(dtype=query_layer.dtype)  # fp16 compatibility
+
+            if self.position_embedding_type == "relative_key":
+                relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
+                attention_scores = attention_scores + relative_position_scores
+            elif self.position_embedding_type == "relative_key_query":
+                relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
+                relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
+                attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
+
+        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+        if attention_mask is not None:
+            # Apply the attention mask is (precomputed for all layers in ErnieMModel forward() function)
+            attention_scores = attention_scores + attention_mask
+
+        # Normalize the attention scores to probabilities.
+        attention_probs = nn.functional.softmax(attention_scores, dim=-1)
+
+        # This is actually dropping out entire tokens to attend to, which might
+        # seem a bit unusual, but is taken from the original Transformer paper.
+        attention_probs = self.dropout(attention_probs)
+
+        # Mask heads if we want to
+        if head_mask is not None:
+            attention_probs = attention_probs * head_mask
+
+        context_layer = torch.matmul(attention_probs, value_layer)
+
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+        context_layer = context_layer.view(new_context_layer_shape)
+
+        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
+
+        if self.is_decoder:
+            outputs = outputs + (past_key_value,)
+        return outputs
+
+
+class ErnieMAttention(nn.Module):
+    def __init__(self, config, position_embedding_type=None):
+        super().__init__()
+        self.self_attn = ErnieMSelfAttention(config, position_embedding_type=position_embedding_type)
+        self.out_proj = nn.Linear(config.hidden_size, config.hidden_size)
+        self.pruned_heads = set()
+
+    def prune_heads(self, heads):
+        if len(heads) == 0:
+            return
+        heads, index = find_pruneable_heads_and_indices(
+            heads, self.self_attn.num_attention_heads, self.self_attn.attention_head_size, self.pruned_heads
+        )
+
+        # Prune linear layers
+        self.self_attn.q_proj = prune_linear_layer(self.self_attn.q_proj, index)
+        self.self_attn.k_proj = prune_linear_layer(self.self_attn.k_proj, index)
+        self.self_attn.v_proj = prune_linear_layer(self.self_attn.v_proj, index)
+        self.out_proj = prune_linear_layer(self.out_proj, index, dim=1)
+
+        # Update hyper params and store pruned heads
+        self.self_attn.num_attention_heads = self.self_attn.num_attention_heads - len(heads)
+        self.self_attn.all_head_size = self.self_attn.attention_head_size * self.self_attn.num_attention_heads
+        self.pruned_heads = self.pruned_heads.union(heads)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        output_attentions: Optional[bool] = False,
+    ) -> Tuple[torch.Tensor]:
+        self_outputs = self.self_attn(
+            hidden_states,
+            attention_mask,
+            head_mask,
+            encoder_hidden_states,
+            encoder_attention_mask,
+            past_key_value,
+            output_attentions,
+        )
+        attention_output = self.out_proj(self_outputs[0])
+        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
+        return outputs
+
+
+class ErnieMEncoderLayer(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        # to mimic paddlenlp implementation
+        dropout = 0.1 if config.hidden_dropout_prob is None else config.hidden_dropout_prob
+        act_dropout = config.hidden_dropout_prob if config.act_dropout is None else config.act_dropout
+
+        self.self_attn = ErnieMAttention(config)
+        self.linear1 = nn.Linear(config.hidden_size, config.intermediate_size)
+        self.dropout = nn.Dropout(act_dropout)
+        self.linear2 = nn.Linear(config.intermediate_size, config.hidden_size)
+        self.norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout1 = nn.Dropout(dropout)
+        self.dropout2 = nn.Dropout(dropout)
+        if isinstance(config.hidden_act, str):
+            self.activation = ACT2FN[config.hidden_act]
+        else:
+            self.activation = config.hidden_act
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        output_attentions: Optional[bool] = True,
+    ):
+        residual = hidden_states
+        if output_attentions:
+            hidden_states, attention_opt_weights = self.self_attn(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                head_mask=head_mask,
+                past_key_value=past_key_value,
+                output_attentions=output_attentions,
+            )
+
+        else:
+            hidden_states = self.self_attn(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                head_mask=head_mask,
+                past_key_value=past_key_value,
+                output_attentions=output_attentions,
+            )
+        hidden_states = residual + self.dropout1(hidden_states)
+        hidden_states = self.norm1(hidden_states)
+        residual = hidden_states
+
+        hidden_states = self.linear1(hidden_states)
+        hidden_states = self.activation(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.linear2(hidden_states)
+        hidden_states = residual + self.dropout2(hidden_states)
+        hidden_states = self.norm2(hidden_states)
+
+        if output_attentions:
+            return hidden_states, attention_opt_weights
+        else:
+            return hidden_states
+
+
+class ErnieMEncoder(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.layers = nn.ModuleList([ErnieMEncoderLayer(config) for _ in range(config.num_hidden_layers)])
+
+    def forward(
+        self,
+        input_embeds: torch.Tensor,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        output_attentions: Optional[bool] = False,
+        output_hidden_states: Optional[bool] = False,
+        return_dict: Optional[bool] = True,
+    ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
+        hidden_states = () if output_hidden_states else None
+        attentions = () if output_attentions else None
+
+        output = input_embeds
+        if output_hidden_states:
+            hidden_states = hidden_states + (output,)
+        for i, layer in enumerate(self.layers):
+            layer_head_mask = head_mask[i] if head_mask is not None else None
+            past_key_value = past_key_values[i] if past_key_values is not None else None
+
+            output, opt_attn_weights = layer(
+                hidden_states=output,
+                attention_mask=attention_mask,
+                head_mask=layer_head_mask,
+                past_key_value=past_key_value,
+            )
+
+            if output_hidden_states:
+                hidden_states = hidden_states + (output,)
+            if output_attentions:
+                attentions = attentions + (opt_attn_weights,)
+
+        last_hidden_state = output
+        if not return_dict:
+            return tuple(v for v in [last_hidden_state, hidden_states, attentions] if v is not None)
+
+        return BaseModelOutputWithPastAndCrossAttentions(
+            last_hidden_state=last_hidden_state, hidden_states=hidden_states, attentions=attentions
+        )
+
+
+# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->ErnieM
+class ErnieMPooler(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.activation = nn.Tanh()
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        # We "pool" the model by simply taking the hidden state corresponding
+        # to the first token.
+        first_token_tensor = hidden_states[:, 0]
+        pooled_output = self.dense(first_token_tensor)
+        pooled_output = self.activation(pooled_output)
+        return pooled_output
+
+
+class ErnieMPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = ErnieMConfig
+    base_model_prefix = "ernie_m"
+
+    def _init_weights(self, module):
+        """Initialize the weights"""
+        if isinstance(module, nn.Linear):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+
+ERNIE_M_START_DOCSTRING = r"""
+
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
+    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
+    behavior.
+
+    Parameters:
+        config ([`ErnieMConfig`]): Model configuration class with all the parameters of the model.
+            Initializing with a config file does not load the weights associated with the model, only the
+            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+ERNIE_M_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `({0})`):
+            Indices of input sequence tokens in the vocabulary.
+
+            Indices can be obtained using [`ErnieMTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+        position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.max_position_embeddings - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+
+        inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    "The bare ErnieM Model transformer outputting raw hidden-states without any specific head on top.",
+    ERNIE_M_START_DOCSTRING,
+)
+class ErnieMModel(ErnieMPreTrainedModel):
+    def __init__(self, config, add_pooling_layer=True):
+        super(ErnieMModel, self).__init__(config)
+        self.initializer_range = config.initializer_range
+        self.embeddings = ErnieMEmbeddings(config)
+        self.encoder = ErnieMEncoder(config)
+        self.pooler = ErnieMPooler(config) if add_pooling_layer else None
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embeddings.word_embeddings
+
+    def set_input_embeddings(self, value):
+        self.embeddings.word_embeddings = value
+
+    def _prune_heads(self, heads_to_prune):
+        """
+        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+        class PreTrainedModel
+        """
+        for layer, heads in heads_to_prune.items():
+            self.encoder.layers[layer].self_attn.prune_heads(heads)
+
+    @add_start_docstrings_to_model_forward(ERNIE_M_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        processor_class=_TOKENIZER_FOR_DOC,
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=BaseModelOutputWithPastAndCrossAttentions,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[tensor] = None,
+        position_ids: Optional[tensor] = None,
+        attention_mask: Optional[tensor] = None,
+        head_mask: Optional[tensor] = None,
+        inputs_embeds: Optional[tensor] = None,
+        past_key_values: Optional[Tuple[Tuple[tensor]]] = None,
+        use_cache: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.FloatTensor], BaseModelOutputWithPoolingAndCrossAttentions]:
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time.")
+
+        # init the default bool value
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+        past_key_values_length = 0
+        if past_key_values is not None:
+            past_key_values_length = past_key_values[0][0].shape[2]
+
+        # Adapted from paddlenlp.transformers.ernie_m.ErnieMModel
+        if attention_mask is None:
+            attention_mask = (input_ids == self.config.pad_token_id).to(torch.float32)
+            attention_mask *= torch.finfo(attention_mask.dtype).min
+            if past_key_values is not None:
+                batch_size = past_key_values[0][0].shape[0]
+                past_mask = torch.zeros([batch_size, 1, 1, past_key_values_length], dtype=attention_mask.dtype)
+                attention_mask = torch.concat([past_mask, attention_mask], dim=-1)
+        # For 2D attention_mask from tokenizer
+        elif attention_mask.ndim == 2:
+            attention_mask = attention_mask.to(torch.float32)
+            attention_mask = 1.0 - attention_mask
+            attention_mask *= torch.finfo(attention_mask.dtype).min
+
+        extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(1)
+
+        embedding_output = self.embeddings(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            past_key_values_length=past_key_values_length,
+        )
+        encoder_outputs = self.encoder(
+            embedding_output,
+            attention_mask=extended_attention_mask,
+            head_mask=head_mask,
+            past_key_values=past_key_values,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        if not return_dict:
+            sequence_output = encoder_outputs[0]
+            pooler_output = self.pooler(sequence_output) if self.pooler is not None else None
+            return (sequence_output, pooler_output) + encoder_outputs[1:]
+
+        sequence_output = encoder_outputs["last_hidden_state"]
+        pooler_output = self.pooler(sequence_output) if self.pooler is not None else None
+        hidden_states = None if not output_hidden_states else encoder_outputs["hidden_states"]
+        attentions = None if not output_attentions else encoder_outputs["attentions"]
+
+        return BaseModelOutputWithPoolingAndCrossAttentions(
+            last_hidden_state=sequence_output,
+            pooler_output=pooler_output,
+            hidden_states=hidden_states,
+            attentions=attentions,
+        )
+
+
+@add_start_docstrings(
+    """ErnieM Model transformer with a sequence classification/regression head on top (a linear layer on top of
+    the pooled output) e.g. for GLUE tasks.""",
+    ERNIE_M_START_DOCSTRING,
+)
+class ErnieMForSequenceClassification(ErnieMPreTrainedModel):
+    # Copied from transformers.models.bert.modeling_bert.BertForSequenceClassification.__init__ with Bert->ErnieM,bert->ernie_m
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.config = config
+
+        self.ernie_m = ErnieMModel(config)
+        classifier_dropout = (
+            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
+        )
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(ERNIE_M_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        processor_class=_TOKENIZER_FOR_DOC,
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=SequenceClassifierOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        past_key_values: Optional[List[torch.Tensor]] = None,
+        use_cache: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        return_dict: Optional[bool] = True,
+        labels: Optional[torch.Tensor] = None,
+    ) -> Union[Tuple[torch.FloatTensor], SequenceClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.ernie_m(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            past_key_values=past_key_values,
+            output_hidden_states=output_hidden_states,
+            output_attentions=output_attentions,
+            return_dict=return_dict,
+        )
+
+        pooled_output = outputs[1]
+
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+
+        loss = None
+        if labels is not None:
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """ErnieM Model with a multiple choice classification head on top (a linear layer on top of
+    the pooled output and a softmax) e.g. for RocStories/SWAG tasks.""",
+    ERNIE_M_START_DOCSTRING,
+)
+class ErnieMForMultipleChoice(ErnieMPreTrainedModel):
+    # Copied from transformers.models.bert.modeling_bert.BertForMultipleChoice.__init__ with Bert->ErnieM,bert->ernie_m
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.ernie_m = ErnieMModel(config)
+        classifier_dropout = (
+            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
+        )
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.classifier = nn.Linear(config.hidden_size, 1)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(ERNIE_M_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=MultipleChoiceModelOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = True,
+    ) -> Union[Tuple[torch.FloatTensor], MultipleChoiceModelOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
+            num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
+            `input_ids` above)
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
+
+        input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
+        attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
+        position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
+        inputs_embeds = (
+            inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
+            if inputs_embeds is not None
+            else None
+        )
+
+        outputs = self.ernie_m(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        pooled_output = outputs[1]
+
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+        reshaped_logits = logits.view(-1, num_choices)
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(reshaped_logits, labels)
+
+        if not return_dict:
+            output = (reshaped_logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return MultipleChoiceModelOutput(
+            loss=loss,
+            logits=reshaped_logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """ErnieM Model with a token classification head on top (a linear layer on top of
+    the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.""",
+    ERNIE_M_START_DOCSTRING,
+)
+class ErnieMForTokenClassification(ErnieMPreTrainedModel):
+    # Copied from transformers.models.bert.modeling_bert.BertForTokenClassification.__init__ with Bert->ErnieM,bert->ernie_m
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.ernie_m = ErnieMModel(config, add_pooling_layer=False)
+        classifier_dropout = (
+            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
+        )
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(ERNIE_M_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        processor_class=_TOKENIZER_FOR_DOC,
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=TokenClassifierOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        past_key_values: Optional[List[torch.Tensor]] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        return_dict: Optional[bool] = True,
+        labels: Optional[torch.Tensor] = None,
+    ) -> Union[Tuple[torch.FloatTensor], TokenClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.ernie_m(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            past_key_values=past_key_values,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+
+        sequence_output = self.dropout(sequence_output)
+        logits = self.classifier(sequence_output)
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """ErnieM Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
+    layers on top of the hidden-states output to compute `span start logits` and `span end logits`).""",
+    ERNIE_M_START_DOCSTRING,
+)
+class ErnieMForQuestionAnswering(ErnieMPreTrainedModel):
+    # Copied from transformers.models.bert.modeling_bert.BertForQuestionAnswering.__init__ with Bert->ErnieM,bert->ernie_m
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.ernie_m = ErnieMModel(config, add_pooling_layer=False)
+        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(ERNIE_M_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        processor_class=_TOKENIZER_FOR_DOC,
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=QuestionAnsweringModelOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        start_positions: Optional[torch.Tensor] = None,
+        end_positions: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = True,
+    ) -> Union[Tuple[torch.FloatTensor], QuestionAnsweringModelOutput]:
+        r"""
+        start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the start of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+        end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the end of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.ernie_m(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+
+        logits = self.qa_outputs(sequence_output)
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
+        end_logits = end_logits.squeeze(-1).contiguous()
+
+        total_loss = None
+        if start_positions is not None and end_positions is not None:
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions = start_positions.clamp(0, ignored_index)
+            end_positions = end_positions.clamp(0, ignored_index)
+
+            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+            start_loss = loss_fct(start_logits, start_positions)
+            end_loss = loss_fct(end_logits, end_positions)
+            total_loss = (start_loss + end_loss) / 2
+
+        if not return_dict:
+            output = (start_logits, end_logits) + outputs[2:]
+            return ((total_loss,) + output) if total_loss is not None else output
+
+        return QuestionAnsweringModelOutput(
+            loss=total_loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """ErnieMForInformationExtraction is a Ernie-M Model with two linear layer on top of the hidden-states output to
+    compute `start_prob` and `end_prob`, designed for Universal Information Extraction.""",
+    ERNIE_M_START_DOCSTRING,
+)
+# Copied from paddlenlp.transformers.ernie_m.modeling.UIEM
+class ErnieMForInformationExtraction(ErnieMPreTrainedModel):
+    def __init__(self, config):
+        super(ErnieMForInformationExtraction, self).__init__(config)
+        self.ernie_m = ErnieMModel(config)
+        self.linear_start = nn.Linear(config.hidden_size, 1)
+        self.linear_end = nn.Linear(config.hidden_size, 1)
+        self.sigmoid = nn.Sigmoid()
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(ERNIE_M_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        start_positions: Optional[torch.Tensor] = None,
+        end_positions: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = True,
+    ) -> Union[Tuple[torch.FloatTensor], QuestionAnsweringModelOutput]:
+        r"""
+        start_positions (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for position (index) for computing the start_positions loss. Position outside of the sequence are
+            not taken into account for computing the loss.
+        end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) for computing the end_positions loss. Position outside of the sequence are not
+            taken into account for computing the loss.
+        """
+
+        result = self.ernie_m(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        if return_dict:
+            sequence_output = result.last_hidden_state
+        elif not return_dict:
+            sequence_output = result[0]
+
+        start_logits = self.linear_start(sequence_output)
+        start_logits = start_logits.squeeze(-1)
+        end_logits = self.linear_end(sequence_output)
+        end_logits = end_logits.squeeze(-1)
+
+        total_loss = None
+        if start_positions is not None and end_positions is not None:
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions = start_positions.clamp(0, ignored_index)
+            end_positions = end_positions.clamp(0, ignored_index)
+
+            loss_fct = BCEWithLogitsLoss()
+            start_loss = loss_fct(start_logits, start_positions)
+            end_loss = loss_fct(end_logits, end_positions)
+            total_loss = (start_loss + end_loss) / 2
+
+        if not return_dict:
+            return tuple(
+                i
+                for i in [total_loss, start_logits, end_logits, result.hidden_states, result.attentions]
+                if i is not None
+            )
+
+        return QuestionAnsweringModelOutput(
+            loss=total_loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            hidden_states=result.hidden_states,
+            attentions=result.attentions,
+        )

venv/lib/python3.10/site-packages/transformers/models/ernie_m/tokenization_ernie_m.py

@@ -0,0 +1,405 @@
+# coding=utf-8
+# Copyright 2023 Xuan Ouyang, Shuohuan Wang, Chao Pang, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang 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.
+"""Tokenization classes for Ernie-M."""
+
+import io
+import os
+import unicodedata
+from typing import Any, Dict, List, Optional, Tuple
+
+import sentencepiece as spm
+
+from ...tokenization_utils import PreTrainedTokenizer
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+SPIECE_UNDERLINE = "▁"
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "sentencepiece_model_ckpt": "sentencepiece.bpe.model"}
+
+RESOURCE_FILES_NAMES = {
+    "sentencepiece_model_file": "sentencepiece.bpe.model",
+    "vocab_file": "vocab.txt",
+}
+
+
+# Adapted from paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer
+class ErnieMTokenizer(PreTrainedTokenizer):
+    r"""
+    Constructs a Ernie-M tokenizer. It uses the `sentencepiece` tools to cut the words to sub-words.
+
+    Args:
+        sentencepiece_model_file (`str`):
+            The file path of sentencepiece model.
+        vocab_file (`str`, *optional*):
+            The file path of the vocabulary.
+        do_lower_case (`str`, *optional*, defaults to `True`):
+            Whether or not to lowercase the input when tokenizing.
+        unk_token (`str`, *optional*, defaults to `"[UNK]"`):
+            A special token representing the `unknown (out-of-vocabulary)` token. An unknown token is set to be
+            `unk_token` inorder to be converted to an ID.
+        sep_token (`str`, *optional*, defaults to `"[SEP]"`):
+            A special token separating two different sentences in the same input.
+        pad_token (`str`, *optional*, defaults to `"[PAD]"`):
+            A special token used to make arrays of tokens the same size for batching purposes.
+        cls_token (`str`, *optional*, defaults to `"[CLS]"`):
+            A special token used for sequence classification. It is the last token of the sequence when built with
+            special tokens.
+        mask_token (`str`, *optional*, defaults to `"[MASK]"`):
+            A special token representing a masked token. This is the token used in the masked language modeling task
+            which the model tries to predict the original unmasked ones.
+    """
+
+    # Ernie-M model doesn't have token_type embedding.
+    model_input_names: List[str] = ["input_ids"]
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    resource_files_names = RESOURCE_FILES_NAMES
+
+    def __init__(
+        self,
+        sentencepiece_model_ckpt,
+        vocab_file=None,
+        do_lower_case=False,
+        encoding="utf8",
+        unk_token="[UNK]",
+        sep_token="[SEP]",
+        pad_token="[PAD]",
+        cls_token="[CLS]",
+        mask_token="[MASK]",
+        sp_model_kwargs: Optional[Dict[str, Any]] = None,
+        **kwargs,
+    ) -> None:
+        # Mask token behave like a normal word, i.e. include the space before it and
+        # is included in the raw text, there should be a match in a non-normalized sentence.
+
+        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+
+        self.do_lower_case = do_lower_case
+        self.sentencepiece_model_ckpt = sentencepiece_model_ckpt
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.Load(sentencepiece_model_ckpt)
+
+        # to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
+        if vocab_file is not None:
+            self.vocab = self.load_vocab(filepath=vocab_file)
+        else:
+            self.vocab = {self.sp_model.id_to_piece(id): id for id in range(self.sp_model.get_piece_size())}
+        self.reverse_vocab = {v: k for k, v in self.vocab.items()}
+
+        super().__init__(
+            do_lower_case=do_lower_case,
+            unk_token=unk_token,
+            sep_token=sep_token,
+            pad_token=pad_token,
+            cls_token=cls_token,
+            mask_token=mask_token,
+            vocab_file=vocab_file,
+            encoding=encoding,
+            sp_model_kwargs=self.sp_model_kwargs,
+            **kwargs,
+        )
+
+    def get_offset_mapping(self, text):
+        if text is None:
+            return None
+
+        split_tokens = self.tokenize(text)
+        normalized_text, char_mapping = "", []
+
+        for i, ch in enumerate(text):
+            if ch in self.SP_CHAR_MAPPING:
+                ch = self.SP_CHAR_MAPPING.get(ch)
+            else:
+                ch = unicodedata.normalize("NFKC", ch)
+            if self.is_whitespace(ch):
+                continue
+            normalized_text += ch
+            char_mapping.extend([i] * len(ch))
+
+        text, token_mapping, offset = normalized_text, [], 0
+
+        if self.do_lower_case:
+            text = text.lower()
+
+        for token in split_tokens:
+            if token[:1] == "▁":
+                token = token[1:]
+            start = text[offset:].index(token) + offset
+            end = start + len(token)
+
+            token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1))
+            offset = end
+        return token_mapping
+
+    @property
+    def vocab_size(self):
+        return len(self.vocab)
+
+    def get_vocab(self):
+        return dict(self.vocab, **self.added_tokens_encoder)
+
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state["sp_model"] = None
+        return state
+
+    def __setstate__(self, d):
+        self.__dict__ = d
+
+        # for backward compatibility
+        if not hasattr(self, "sp_model_kwargs"):
+            self.sp_model_kwargs = {}
+
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.Load(self.sentencepiece_model_ckpt)
+
+    def clean_text(self, text):
+        """Performs invalid character removal and whitespace cleanup on text."""
+        return "".join((self.SP_CHAR_MAPPING.get(c, c) for c in text))
+
+    def _tokenize(self, text, enable_sampling=False, nbest_size=64, alpha=0.1):
+        """Tokenize a string."""
+
+        if self.sp_model_kwargs.get("enable_sampling") is True:
+            enable_sampling = True
+        if self.sp_model_kwargs.get("alpha") is not None:
+            alpha = self.sp_model_kwargs.get("alpha")
+        if self.sp_model_kwargs.get("nbest_size") is not None:
+            nbest_size = self.sp_model_kwargs.get("nbest_size")
+
+        if not enable_sampling:
+            pieces = self.sp_model.EncodeAsPieces(text)
+        else:
+            pieces = self.sp_model.SampleEncodeAsPieces(text, nbest_size, alpha)
+        new_pieces = []
+        for pi, piece in enumerate(pieces):
+            if piece == SPIECE_UNDERLINE:
+                if not pieces[pi + 1].startswith(SPIECE_UNDERLINE) and pi != 0:
+                    new_pieces.append(SPIECE_UNDERLINE)
+                    continue
+                else:
+                    continue
+            lst_i = 0
+            for i, chunk in enumerate(piece):
+                if chunk == SPIECE_UNDERLINE:
+                    continue
+                if self.is_ch_char(chunk) or self.is_punct(chunk):
+                    if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
+                        new_pieces.append(piece[lst_i:i])
+                    new_pieces.append(chunk)
+                    lst_i = i + 1
+                elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
+                    if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
+                        new_pieces.append(piece[lst_i:i])
+                    lst_i = i
+                elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
+                    if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
+                        new_pieces.append(piece[lst_i:i])
+                    lst_i = i
+            if len(piece) > lst_i:
+                new_pieces.append(piece[lst_i:])
+        return new_pieces
+
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (strings for sub-words) in a single string."""
+        out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip()
+        return out_string
+
+    def convert_ids_to_string(self, ids):
+        """
+        Converts a sequence of tokens (strings for sub-words) in a single string.
+        """
+        tokens = self.convert_ids_to_tokens(ids)
+        out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip()
+        return out_string
+
+    # to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
+    def _convert_token_to_id(self, token):
+        return self.vocab.get(token, self.vocab.get(self.unk_token))
+
+    # to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        return self.reverse_vocab.get(index, self.unk_token)
+
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        r"""
+        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+        adding special tokens. An ErnieM sequence has the following format:
+
+        - single sequence: `[CLS] X [SEP]`
+        - pair of sequences: `[CLS] A [SEP] [SEP] B [SEP]`
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs to which the special tokens will be added.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+        Returns:
+            `List[int]`: List of input_id with the appropriate special tokens.
+        """
+        if token_ids_1 is None:
+            return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
+        _cls = [self.cls_token_id]
+        _sep = [self.sep_token_id]
+        return _cls + token_ids_0 + _sep + _sep + token_ids_1 + _sep
+
+    def build_offset_mapping_with_special_tokens(self, offset_mapping_0, offset_mapping_1=None):
+        r"""
+        Build offset map from a pair of offset map by concatenating and adding offsets of special tokens. An Ernie-M
+        offset_mapping has the following format:
+
+        - single sequence: `(0,0) X (0,0)`
+        - pair of sequences: `(0,0) A (0,0) (0,0) B (0,0)`
+
+        Args:
+            offset_mapping_ids_0 (`List[tuple]`):
+                List of char offsets to which the special tokens will be added.
+            offset_mapping_ids_1 (`List[tuple]`, *optional*):
+                Optional second list of wordpiece offsets for offset mapping pairs.
+        Returns:
+            `List[tuple]`: List of wordpiece offsets with the appropriate offsets of special tokens.
+        """
+        if offset_mapping_1 is None:
+            return [(0, 0)] + offset_mapping_0 + [(0, 0)]
+
+        return [(0, 0)] + offset_mapping_0 + [(0, 0), (0, 0)] + offset_mapping_1 + [(0, 0)]
+
+    def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False):
+        r"""
+        Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer `encode` method.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of ids of the first sequence.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (`str`, *optional*, defaults to `False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+        Returns:
+            `List[int]`:
+                The list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+
+        if already_has_special_tokens:
+            if token_ids_1 is not None:
+                raise ValueError(
+                    "You should not supply a second sequence if the provided sequence of "
+                    "ids is already formatted with special tokens for the model."
+                )
+            return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_0]
+
+        if token_ids_1 is not None:
+            return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
+        return [1] + ([0] * len(token_ids_0)) + [1]
+
+    def create_token_type_ids_from_sequences(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Create the token type IDs corresponding to the sequences passed. [What are token type
+        IDs?](../glossary#token-type-ids) Should be overridden in a subclass if the model has a special way of
+        building: those.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                The first tokenized sequence.
+            token_ids_1 (`List[int]`, *optional*):
+                The second tokenized sequence.
+        Returns:
+            `List[int]`: The token type ids.
+        """
+        # called when `add_special_tokens` is True, so align with `build_inputs_with_special_tokens` method
+        if token_ids_1 is None:
+            # [CLS] X [SEP]
+            return (len(token_ids_0) + 2) * [0]
+
+        # [CLS] A [SEP] [SEP] B [SEP]
+        return [0] * (len(token_ids_0) + 1) + [1] * (len(token_ids_1) + 3)
+
+    def is_ch_char(self, char):
+        """
+        is_ch_char
+        """
+        if "\u4e00" <= char <= "\u9fff":
+            return True
+        return False
+
+    def is_alpha(self, char):
+        """
+        is_alpha
+        """
+        if ("a" <= char <= "z") or ("A" <= char <= "Z"):
+            return True
+        return False
+
+    def is_punct(self, char):
+        """
+        is_punct
+        """
+        if char in ",;:.?!~，；：。？！《》【】":
+            return True
+        return False
+
+    def is_whitespace(self, char):
+        """
+        is whitespace
+        """
+        if char == " " or char == "\t" or char == "\n" or char == "\r":
+            return True
+        if len(char) == 1:
+            cat = unicodedata.category(char)
+            if cat == "Zs":
+                return True
+        return False
+
+    def load_vocab(self, filepath):
+        token_to_idx = {}
+        with io.open(filepath, "r", encoding="utf-8") as f:
+            for index, line in enumerate(f):
+                token = line.rstrip("\n")
+                token_to_idx[token] = int(index)
+
+        return token_to_idx
+
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        index = 0
+        if os.path.isdir(save_directory):
+            vocab_file = os.path.join(
+                save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+            )
+        else:
+            vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory
+        with open(vocab_file, "w", encoding="utf-8") as writer:
+            for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
+                if index != token_index:
+                    logger.warning(
+                        f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
+                        " Please check that the vocabulary is not corrupted!"
+                    )
+                    index = token_index
+                writer.write(token + "\n")
+                index += 1
+
+        tokenizer_model_file = os.path.join(save_directory, "sentencepiece.bpe.model")
+        with open(tokenizer_model_file, "wb") as fi:
+            content_spiece_model = self.sp_model.serialized_model_proto()
+            fi.write(content_spiece_model)
+
+        return (vocab_file,)

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

@@ -0,0 +1,67 @@
+# Copyright 2023 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_torch_available
+
+
+_import_structure = {
+    "configuration_musicgen": [
+        "MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP",
+        "MusicgenConfig",
+        "MusicgenDecoderConfig",
+    ],
+    "processing_musicgen": ["MusicgenProcessor"],
+}
+
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_musicgen"] = [
+        "MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "MusicgenForConditionalGeneration",
+        "MusicgenForCausalLM",
+        "MusicgenModel",
+        "MusicgenPreTrainedModel",
+    ]
+
+if TYPE_CHECKING:
+    from .configuration_musicgen import (
+        MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP,
+        MusicgenConfig,
+        MusicgenDecoderConfig,
+    )
+    from .processing_musicgen import MusicgenProcessor
+
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_musicgen import (
+            MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
+            MusicgenForCausalLM,
+            MusicgenForConditionalGeneration,
+            MusicgenModel,
+            MusicgenPreTrainedModel,
+        )
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)