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gpt2_ablation_data

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Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      JSON parse error: Missing a name for object member. in row 0
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 174, in _generate_tables
                  df = pandas_read_json(f)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
                  return pd.read_json(path_or_buf, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 815, in read_json
                  return json_reader.read()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1025, in read
                  obj = self._get_object_parser(self.data)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1051, in _get_object_parser
                  obj = FrameParser(json, **kwargs).parse()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1187, in parse
                  self._parse()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1402, in _parse
                  self.obj = DataFrame(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/core/frame.py", line 778, in __init__
                  mgr = dict_to_mgr(data, index, columns, dtype=dtype, copy=copy, typ=manager)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/core/internals/construction.py", line 503, in dict_to_mgr
                  return arrays_to_mgr(arrays, columns, index, dtype=dtype, typ=typ, consolidate=copy)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/core/internals/construction.py", line 114, in arrays_to_mgr
                  index = _extract_index(arrays)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/core/internals/construction.py", line 667, in _extract_index
                  raise ValueError("If using all scalar values, you must pass an index")
              ValueError: If using all scalar values, you must pass an index
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 228, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 3422, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2187, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2391, in iter
                  for key, example in iterator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1882, in __iter__
                  for key, pa_table in self._iter_arrow():
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1904, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 499, in _iter_arrow
                  for key, pa_table in iterator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 346, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 177, in _generate_tables
                  raise e
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 151, in _generate_tables
                  pa_table = paj.read_json(
                File "pyarrow/_json.pyx", line 308, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: JSON parse error: Missing a name for object member. in row 0

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GPT-2 Ablation Data

This dataset contains gradient magnitude data from GPT-2 model ablation experiments, specifically focusing on different gradient computation methods and block configurations.

Dataset Structure

The dataset contains two main directories:

1. gpt2_gradient_staircase_jblock_3P_1M/

Contains gradient magnitude data using staircase gradient computation method:

  • Method: Staircase gradient computation
  • Block Configuration: 3P (3 parallel blocks)
  • Dataset Size: 1M tokens
  • Files: Multiple .safetensors files with corresponding metadata JSON files
  • Layers: Data from layers 1, 6, and 11
  • Token Counts: Various token counts (1, 10, 100, 1000, 10000, 100000, 1000000)

2. gpt2_gradient_topk_jblock_3P_1M/

Contains gradient magnitude data using top-k gradient computation method:

  • Method: Top-k gradient computation
  • Block Configuration: 3P (3 parallel blocks)
  • Dataset Size: 1M tokens
  • Files: Multiple .safetensors files with corresponding metadata JSON files
  • Layers: Data from layers 1, 6, and 11
  • Token Counts: Various token counts (1, 10, 100, 1000, 10000, 100000, 1000000)

File Naming Convention

Files follow the pattern:

magnitudes_{method}_gradient_{layer}_{token_count}_{timestamp}.safetensors
magnitudes_{method}_gradient_{layer}_{token_count}_{timestamp}.safetensors.metadata.json

Where:

  • {method}: Either "staircase" or "topk"
  • {layer}: Layer number (1, 6, or 11)
  • {token_count}: Number of tokens processed
  • {timestamp}: Timestamp when the data was generated

Data Format

  • Main Data: .safetensors files containing gradient magnitude tensors
  • Metadata: .safetensors.metadata.json files containing metadata about the corresponding tensor files

Usage

This dataset is designed for research in:

  • Model interpretability
  • Gradient-based analysis
  • Ablation studies
  • Circuit analysis in transformer models

Citation

If you use this dataset in your research, please cite the original work and this dataset repository.

License

[Add appropriate license information here]

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