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_DESCRIPTION="""\
Dataset for storing training evaluations of pythia models, e.g. loss, perplexity
"""
import datasets
import json
class PythiaTrainingEvals(datasets.GeneratorBasedBuilder):
MODEL_SIZES = [
"70m",
"160m",
"410m",
"1.4b",
"2.8b",
]
BUILDER_CONFIGS = []
for model_size in MODEL_SIZES:
BUILDER_CONFIGS.extend([
datasets.BuilderConfig(
name=f"{model_size}",
description=f"Dataset of pythia training evaluation metrics for pythia model size: {model_size}",
version="1.0.0",
),
])
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager):
"""
Returns data for different splits - we define a split as a model size.
"""
checkpoint_steps = [0, 1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1000, ]
checkpoint_steps.extend([3000 + (i * 10000) for i in range(0, 15)])
to_download_files = []
model_size = self.config.name.split("__")[0]
for checkpoint_step in checkpoint_steps:
to_download_files.append(f"./models/{model_size}/checkpoint_{checkpoint_step}/evals.json")
downloaded_files = dl_manager.download_and_extract(to_download_files)
return [
datasets.SplitGenerator(
name='default',
gen_kwargs={
"filepaths": downloaded_files,
}
)
]
def _generate_examples(self, filepaths):
"""
Yields examples from each file in filepaths that are stored as jsons
with the evaluation metrics for a given checkpoint step.
"""
checkpoint_steps = [0, 1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1000, ]
checkpoint_steps.extend([3000 + (i * 10000) for i in range(0, 15)])
# the filepaths should be a list of filepaths
if isinstance(filepaths, str):
filepaths = [filepaths]
for idx, filepath in enumerate(filepaths):
with open(filepath, 'rb') as f:
data = json.load(f)
record = {
"checkpoint_step": checkpoint_steps[idx],
**data
}
yield idx, record
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